Artificial Intelligence

“Noise in machine learning just means errors in the data, or random events that you cannot predict.”

Pedro Domingos

“Noise” – the quantum of which has grown over the years in the loan processing, is one of the main reasons why bankers have been rooting for automation of loan processing for some time now. The other reason is data integrity, which gets compromised when low-end manual labor is employed during loan processing. In a poll conducted by Moody’s Analytics, when questioned about the challenges they faced in initiation of loan processing, 56% of the bankers surveyed answered that manual collection of data was the biggest problem.

Manual processing of loan documents involves:

  • Routing documents/data to the right queue
  • Categorizing/classifying the documents based on type of instruction
  • Extracting information – relevant data points vary by classification and relevant business rules Feeding the extracted information into the ERP, BPM, RPA
  • Checking for soundness of information
  • Ensuring the highest level of security and transparency via an audit trial.

“There’s never time to do it right. There’s always time to do it over.”

With data no longer remaining consistent, aggregating, and consolidating dynamic data (from sources such as emails, web downloads, industry websites, etc.) has become a humongous task. Even when it comes to static data, the sources and formats have multiplied over the years, so manually extracting, classifying, tagging, cleaning, tagging, validating, and uploading the relevant data elements: currency, transaction type, counterparty, signatory, product type, total amount, transaction account, maturity date, the effective date, etc., is not a viable option anymore. And adding to the complexity is the lack of standardization in the Taxonomy with each lender and borrower using different terms for the same Data Element.

Hence, the need for automation, and integration of the multiple workflows used in loan origination – right from the input pipeline, the OCR pipeline, pre-and post-processing pipelines, to the output pipeline for dissemination of data downstream. With the added advantage of achieving a standard Taxonomy, at least in your shop.

The benefits of automating certain low-end, repetitive, and mundane data extraction activities

Reducing loan processing time from weeks to days: When the integrity of data is certain, when all data exchanges are consolidated and centralized in one place instead of existing in silos in back, middle, and front offices, only then can bankers reduce the loan processing time from months, weeks to days.

That was what JP Morgan Case achieved with COIN. They saved an estimated 360k hours or 15k days’ worth of manual effort with their automated contract management platform. It is not hard to imagine the kind of impact it had on the customer experience (EX)!

More time for proper risk assessment: There is less time wasted in keying and rekeying data. With machines taking over from nontechnical staff, the AI (Artificial Intelligence) pipelines are not compromised with erroneous, duplicate data stored in sub-optimal systems. With administrative processes streamlined, there’s time for high-end functions such as reconciliation of portfolio data, thorough risk assessment, etc.

Timely action is possible: Had banks relied on manual processes, it would have taken ages to validate the client, and by that time it could have been too late.

Ensuring compliance: By automating the process of data extraction from the scores of documents (that banks are inundated with during the course of loan processing) and by combining the multiple pipelines where data is extracted, transformed, cleaned, validated with a suitable business rules engines, and thereafter loaded for downstream, banks are also able to ensure robust governance and control for meeting regulatory and compliance needs.

Enhances the CX: Automation has a positive impact on CX. Bankers also save dollars in compensation, equipment, staff, and sundry production expenses.

Doing it Right!

One of Magic FinServ’s success stories comprises a solution for banking and financial services companies that successfully allows them to optimize the extraction of critical data elements (CDE) from emails and attachments with Magic’s bespoke tool – DeepSightTM for Transaction processing and accelerator services.

The problem:

Banks in the syndicated lending business receive large volume of emails and other documented inputs for processing daily. The key data is embedded in the email message or in the attachment. The documents are in PDF, TIF, DOCX, MSG, XLS, form. Typically, the client’s team would manually go through each email or attachment containing different Loan Instructions. Thereafter the critical elements are entered into a spreadsheet and then, uploaded, and saved in the bank’s commercial loan system.

As is inherent here there are multiple pipelines for input, pre-processing, extraction, and finally output of data, which leads to duplication of effort, is time consuming, resulting in false alerts, etc.

What does Magic Solution do to optimize processing time, effort, and spend?

  • Input Pipeline: Integrate directly with an email box or a secured folder location and execute processing in batches.
  • OCR Pipeline: Images or Image based documents are first corrected and enhanced (OCR Pre-Processing) before feeding them to an OCR system. This is done to get the best output from an OCR system. DeepSightTM can integrate with any commercial or publicly available OCRs.
  • Data Pre-Processing Pipeline: Pre-Processing involves data massaging using several different techniques like cleaning, sentence tokenization, lemmatization etc., to feed the data as required by optimally selected AI models.
  • Extraction Pipeline: DeepSight’s accelerator units accurately recognize the layout, region of interest and context to auto-classify the documents and extract the information embedded in tables, sentences, or key value pairs.
  • Post-Processing Pipeline: Post-Processing pipeline applies all the reverse lookup mappings, business rules etc. to further fine tune accuracy.
  • Output Storage: Any third-party or in-house downstream or data warehouse system can be integrated to enable straight through processing.
  • Output: Output format can be provided according to specific needs. DeepSightTM provides data in excel, delimited, PDF, JSON, or any other commonly used format. Data can also be made available through APIs. Any exception or notifications can be routed through emails as well.

Technologies in use

Natural language processing (NLP): for carrying out context-specific search from emails and attachments in varied formats and extracts relevant data from it.

Traditional OCR: for recognizing key characters (text) scattered anywhere in the unstructured document is made much smarter by overlaying an AI capability.

Intelligent RPA: is used to consolidate data from various other sources such as ledgers, to enrich the data extracted from the documents. And finally, all this is brought together by a Rules Engine that captures the organization’s policies and processes. With Machine Learning (ML) and a human-in-the-loop approach to carry out truth monitoring, the tool becomes more proficient and accurate every passing day.

Multi-level Hierarchy: This is critical for eliminating false positives and negatives since payment instructions could comprise of varying CDEs. The benefits that the customer gets are:

  • Improve precision on Critical Data Elements (CDEs) such as Amounts, Rates and Dates etc.
  • Contains false positives and negatives to reduce the manual intervention

Taxonomy: Train the AI engine on taxonomy is important because:

  • Improve precision and context specific data extraction and classification mechanism
  • Accuracy of the data elements which refer to multiple CDEs will improve. For e.g., Transaction Type, Dates and Amounts

Human-eye parser: For documents that contain multiple pages and lengthy preambles you require a delimitation of tabular vs. free flow text. The benefits are as follows:

  • Extraction of tabular data, formulas, instructions with multiple transaction types all require this component for seamless pre and post processing

Validation & Normalization: For reducing the manual intervention for the exception queue:

  • An extensive business rule engine that leverages existing data will significantly reduce manual effort and create an effective feedback loop for continuous learning

OCR Assembling: Highly required for image processing of vintage contracts and low image quality (i.e., vintage ISDAs):

  • Optimize time, cost and effort with the correct OCR solution that delivers maximum accuracy.

Conclusion

Spurred on by competition from FinTech and challenger banks, that are using APIs, AI, and ML for maximizing efficiency of loan processing, the onus is on banks to maximize efficiency. The first step is ensuring data integrity with the use of intelligent tools and business-rules engines that make it easier to validate data. It is after all much easier to pursue innovation and ensure that SLAs are met when workflows are automated, cohesive, and less dependent on human intervention. So, if you wish to get started and would like more information on how we can help, write to us mail@magicfinserv.com.

Wealth managers are standing at the epicenter of a tectonic shift, as the balance of power between offerings and demand undergoes a dramatic upheaval. Regulators are pushing toward a ‘constrained offering’ norm while private clients and independent advisors demand a more proactive role. FinTech Innovation: Paolo Sironi

Artificial Intelligence, Machine Learning-based analytics, recommendation engines, next best action engines, etc., are powering the financial landscape today. Concepts like robo-advisory (a $135 Billion market by 2026) for end-to-end self-service investing, risk profiling, and portfolio selection, Virtual Reality / Augmented Reality or Metaverse for Banking and Financial trading (Citi plans to use holographic workstations for financial trading) are creating waves but will take time to reach critical value.

In the meanwhile, there’s no denying that Fintechs and Financial Institutions must clean their processes first – by organizing and streamlining back, middle, and front office operations with the most modern means available such as artificial intelligence, machine learning, RPA, and the cloud. Hence, the clarion call for making back, middle and front office administrative processes of financial institutions the hub for change with administrative AI.

What is administrative AI?

Administrative AI is quite simply the use Artificial Intelligence based tools to simplify and make less cumbersome administrative processes such as loans processing, expense management, KYC, Client Life Cycle Management / Onboarding, data extraction from industry websites such as SEC, Munis, contract management, etc.

Administrative AI signals a paradigm shift in approach – which is taking care of the basics and the less exciting first. It has assumed greater importance due to the following reasons:

  1. Legacy systems make administrative processes chaotic and unwieldy and result in duplication of effort and rework:

Back and middle office administrative processes are cumbersome, they are repetitive, and sometimes unwieldy – but they are crucial for business. For example, if fund managers spend their working hours extracting data and cleaning excel sheets of errors, there will be little use of the expensive AI engine for predicting risks in investment portfolios or modeling alternative scenarios in real time. With AI life becomes easier.

  1. Administrative AI increases productivity of work force, reduces error rate resulting in enhancec customer satisfaction

AI is best for processes that are high volume and where the incidences of error are high such as business contracts management, regulatory compliance, payments processing, onboarding, loan processing, etc. An example of how Administrative AI reduces turnaround time and costs is COIN – contract intelligence developed by J P Morgan Chase that reviews loan agreements in a record time.

  1. Administrative costs are running sky-high: In 2019, as per a Forbes article, Banks spent an estimated $ 67 billion on technology. The spending on administrative processes is still umongous. From the example provided below (Source: McKinsey) 70% of the IT spend is on IT run and technical debt that is the result of unwieldy processes and silos.
  1. Without reaching the critical mass of process automation, analytics, and high-quality data fabric, organizations risk ending up paralyzed

And lastly, even for the moonshot project, you’ll need to clear your core processes first. The focus on financial performance does not mean that you sacrifice research and growth. However, if processes that need cleaning and automation are not cleaned and automated, then the business could be saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them.” (Source Harvard Business Review )

So, if businesses do not wish to squander the opportunities, they must be practical with their approach. Administrative AI for Fintechs and FIs is the way forward.

Making a difference with Magic DeepSightTM Solution Accelerator

Administrative AI is certainly a great way to achieve cost reduction with a little help from the cloud, machine learning, API-based AI systems. In our experience, we provide solutions for such administrative tasks that provides significant benefits in terms of productivity, time and accuracy while improving the quality of work environment for the Middle and Back-office staff. For banks, capital markets, global fund managers, promising Fintechs and others, a bespoke solution that can be adapted for every unique need like DeepSightTM can make all the difference.

“Magic DeepSightTM is an accelerator-driven solution for comprehensive extraction, transformation, and delivery of data from a wide range of structured, semi-structured, and unstructured data sources leveraging cognitive technologies of AI/ML along with other methodologies to provide holistic last-mile solution.”

Success Stories with DeepSightTM

Client onboarding/KYC

  • Extract and process a wide set of structured/unstructured documents (e.g., tax documents, bank statements, driver’s licenses, etc.
  • From diverse data sources (email, pdf, spreadsheet, web downloads, etc.)
  • Posts fixed format output across several third-party and internal applications for case management such as Nice Actimize

Trade/Loan Operations

  • Trade and loan operation instructions are often received as emails and attachments to emails.
  • DeepSightTM intelligently automates identifying the emails, classifying and segregating them in folders.
  • The relevant instructions are then extracted from emails and documents to ingest the output into order/loan management platforms.

Expense Management

  • Invoices and expense details are often received as PDFs or Spreadsheets attached to emails
  • DeepSightTM Identifies types of invoices – e.g., deal related or non-deal related or related to any business function legal, HR etc.
  • Applies business rules on the extracted output to generate general ledger codes and item lines to be input in third-party applications (e.g., Coupa, SAP Concur).

Website Data Extraction

  • Several processes require data from third party websites e.g., SEC Edgar, Muni Data.
  • This data is typically extracted manually resulting in delays.
  • DeepSightTM can be configured to access websites, identify relevant documents, download the same and extract information.
  • Several processes require data from third party websites e.g., SEC Edgar, Muni Data.
  • Applies business rules on the extracted output to generate general ledger codes and item lines to be input in third-party applications (e.g., Coupa, SAP Concur).

Contracts Data Extraction

  • Contract/Service/Credit agreements are complex and voluminous text-wise. Also, there are multiple changes in the form of renewals and addendums.
  • Therefore, managing contracts is a complex task and requires highly skilled professionals.
  • DeepSightTM provides a configured solution that simplifies buy-side contract/service management.
  • Combined with Magic FinServ’s advisory services, the buy-side firm’s analyst gets the benefits of a virtual assistant.
  • Not only are the errors and omissions that are typical in human-centric processing reduced significantly, but our solution also ensures that processing becomes more streamlined as documents are categorized according to type of service, and for each service provider, only relevant content is identified and extracted.
  • Identifies and segregates different documents and also files all documents for a particular service provider in the same folder to enable ease of access and retrieval.
  • A powerful business rules engine is at work in the configuration, tagging, and extraction of data.
  • Lastly, a single window display ensures better readability and analysis.

Learning from failures!

Before we conclude, an example of a challenger bank that set up an account within 10 minutes, and provided customers access to money management features, and a contactless debit card in record time to prove why investor preferences are changing. It was once a success story that every fintech wanted to emulate. Toda. y, it is being investigated by the Financial Conduct Authority (FCA) over potential breaches of financial crime regulations. (Source: BBC) There were reports of freezing several accounts on account of suspicious activity. The bank has also undergone losses amounting to £115 million or $142 million in 2020/21 and its accountants about the “material uncertainty” of its future.

Had they taken care of the administrative processes, particularly those dealing with AML and KYC? We may never know? But what we do know is that it is critical to make administrative processes cleaner and automated.

Not just promising FinTechs, every business needs to clean up its administrative processes with AI:

Today’s business demands last-mile process automation, integrated processes, and a cleaner data fabric that democratizes data access and use across a broad spectrum of financial institutions such as Asset Managers, Hedge Funds, Banks, FinTechs, Challengers, etc. Magic FinServ’s team not only provides advisory services; we also get into the heart of the matter. Our hands on approach leveraging Magic FinServ’s Fintech Accelerator Program helps FinTechs and FIs modernize their platforms to meet emerging market needs.

For more information about Magic Accelerator write to us mail@magicfinserv.com Or visit our website: www.magicfinserv.com

Money laundering is a crime, a fraudulent activity to cleanse “dirty” money by moving it in and out of the financial system without getting detected. This takes a big toll on banks and financial institutions as they end up paying hefty fines and penalties for anti-money laundering breaches.

Often changes in regulations or sanctions convert otherwise legal money into “dirty” money requiring banks and FIs to report deposits and transactions and also freeze them. Inadvertently releasing these funds could also result in regulatory action.

Constantly changing rules of AML require retraining of staff, changes to workflows, and case tools. Until the staff becomes adept at the new rules, errors and omissions are a huge risk.

A typical money laundering scheme looks something like below.

  • Collecting and depositing dirty money in a legal account.
  • With banks in the US having a threshold limit of $ 10,000 in deposits scammers deposit lesser amounts to prevent detection using false invoices, made-up names, etc.
  • Afterwards, they take out the dirty money via purchases of property and other luxury items through shell companies.
  • With this process, money becomes legitimate, and they take out the money from the system.

With regulators across the world coming heavy on any financial institution found negligent of AML compliance, many banks, and financial institutions are turning to machine learning, big data, AI, and analytics for ensuring regulatory compliance and saving themselves the hefty penalties and fines or being named as a defaulter. They are also preventing the disruption to services when costly investigations ensue because of flaws or breaches in AML. Though AML compliance or processing can seem like a gigantic exercise, it is primarily all about collating data and drawing meaningful insights using advanced rules and machine learning.

Quality of data is either an impediment or an asset

Whether it is investigating anomalies, or raising the red flag in time, or ensuring accurate customer profiling (watchlist or sanctions screening), the quality of data is of paramount importance. It is either an impediment that is throwing false positives or an asset which streamlines processes and results in cost effectiveness and efficiency while ensuring compliance.

So, before you proceed with automating AML processing through use of automation tools and machine learning, you need to question –

Is my data clean?

While machine learning has multiple benefits, implementing it is not easy.

  1. As underlined earlier – data today is like many headed hydras – emanating from many sources, and in multiple formats – pdfs, invoices, emails, scanned text, spool files, etc.
  2. Good data is an asset and bad data an impediment resulting in poor decisions
  3. Most of the machine learning technology is all about identifying just the relevant data from terabytes of available data and self-learning over a period of time to become more efficient. However, this needs to be coupled with other technologies to help cleanse the data, and if you are not efficient at cleaning it, you will never get the desired results.

Unfortunately, most data-related work even today is primarily the responsibility of the back- end staff of banks and FIs. The manual process makes it expensive and time consuming. Not just that, human intelligence/capability limits the amount of data that can be optimally processed and hence results in potential errors and exposure.

Result – Delays, late filing of suspicious activity report (SAR), time, resources, and money wasted in investigations, poor customer experience (duplication of effort during Know Your Customer (KYC) and onboarding), potential politically exposed person (PEPS), offenders, and others on the watchlist evading detection, etc. When you fail to spot a suspicious transaction in time or scale up exponentially as per need, you end up bearing the burden of costly fines later.

Magic’s DeepSightTM Solution raising the bar in fighting money laundering

AI and Machine Learning aided solutions help in finding patterns of unlawful movement of money like layering and structuring, deciphering suspicious activities in time, accurately identifying customers in the sanctions list, transaction monitoring, risk-based monitoring, investigations, and reporting for suspicious activities enterprise-wide. However, the efficiency of these tools is limited by the amount of clean data available. Enter Magic DeepSightTM , a tool leveraging AI, ML and a host of other automation technologies embedded with Rules Engines and Workflows to deliver extensive amounts of clean data.

Reading like a human but faster: Magic FinServ’s OCR technology and form parsing intelligence use advanced technologies like natural language processing (NLP), computer vision, and neural network algorithms to read like humans and infinitely faster. From tons of unstructured data in the form of text, character, and images, it figures out the relevant fields with ease. What is time-consuming and tedious for the average staff is made easy with Magic DeepSightTM .

Scaling data cleansing effort exponentially: The importance of cleaning data at scale can be realized from the fact that if it is not done at an exponential pace, machines will end up learning from untrustworthy data. Magic DeepSightleverages RPA, API and workflows to extract data from various sources, compare and resolve errors and omissions.

Keeping track of changing rules: AML rules keep changing frequently, people and entities sanctioned keep changing. In a manual operation, this is bound to cause problems. Magic DeepSight™ leverages Rules Engines where changes in rules can be updated to ensure uniform and complete adherence to new rules.

Identifying customers accurately even when information changes. Digitalization has amplified the efforts that firms have to put in for ensuring AML compliance. Customers move places, they change names, addresses, and other information that sets them apart. It is a tedious and time- consuming affair to keep up to date. Magic DeepSightTM resolves entities and identifies customers accurately.

Keeping pace with sophisticated transaction- monitoring: Transaction Monitoring is at the heart of anti-money laundering, with sophisticated means adopted by hackers requiring more than manual effort to ensure timely detection. Establishing a clear lineage of the data source is one of the foremost challenges that enterprises face today. Magic DeepSightTM can read the transactions from source and create a client profile and look for patterns satisfying the money laundering rules.

Act Now! Fight Fraud and Money Laundering Activities

The time to act is now. You can prevent money launderers from having their way by investing right in tools that can do the work of extracting data efficiently at half the time and cost and which can be integrated into your AML workflows seamlessly.

Our research data indicates that 45% of businesses that invested in more AI/ML deployments and had clearer data and technology strategies have fared relatively better in terms of garnering a competitive advantage than the remaining 55% that are still stuck in the experimental phase. Do not take the risk of falling further behind. Download our brochure on AML compliance to know more about our offerings or write to us mail@magicfinserv.com.

Gain a competitive advantage with advanced AML solutions powered by AI and ML  

2021 was a blockbuster year for the regulators. An estimated $ 2.7 billion was  raised in fines or anti-money laundering (AML) and Know Your Customer (KYC) violations in the first half of 2021 itself. The number of institutions that were fined quadrupled from 24 in 2020 to 80 in 2021.

There was a diverse list of defaulters last year, something not seen earlier.  

  • There was a bank holding company specializing in credit cards, auto loans, banking, and saving accounts.
  • A fintech that achieved phenomenal growth for trading
  • A cryptocurrency platform 
    All failed to meet AML compliance standards and were fined.
  • Credit Suisse, the Global Investment Bank, was another major defaulter.

All this is an indication that the regulators’ tolerance for default is very limited.

And secondly, the variance in defaulter listing also indicates once and for all that the ambit for AML breaches has widened. No longer confined to the big banks only, post covid-19 all financial institutions must comply with the new reforms enforced by the Financial Crimes Enforcement Network (FinCEN), Financial Action Task Force (FATF), and Office of Foreign Assets Control (OFAC) or face the heat.  

What are the key challenges that banks and fintechs face regarding AML compliance?

Considering that AML observance is mandatory why do banks and financial institutions fail to comply with the standards repeatedly? The biggest challenge remains the overt reliance on outdated systems and processes and manual labor. The others we have enumerated below. 

  • Lack of a gold standard for data. Sources of data have grown exponentially and the formats in which they are found have diversified over the years. Further, the widespread use of digital currency has increased the risks of money laundering phenomenally.
  • Outdated and incomplete documentation: Data has grown prolifically. This makes customer profiling and integrating data from multiple sources more demanding than ever before. In the absence of automation, it becomes a time-consuming exercise. Most systems used for AML processing are extremely limited in scope and cannot scale as rapidly as desired or have clarity in terms of data accuracy.
  • Gaps/flaws in AML-IT infrastructure and false positives: As evident in the instances of NatWest which risked censure for failing to make their systems robust, failure to raise timely alerts could be expensive. However, if the AML systems are not able to distinguish between illegal and legitimate transactions and notify good transactions as well, it loses its veracity. False positives result in duplication of effort and resultant wastage of time.     
  • External factors such as WFH and digitization: Rapid advance in digitization and WFH culture post the coronavirus pandemic has increased the threat landscape. Sophisticated means for money laundering like structuring and layering (adopted by criminals/frauds) require exceptional intelligence. However, many of the existing systems are not able to distinguish between illegal and legal transactions, let alone spot activities such as smurfing, where small cash deposits are made by different people. 
  • Human factors: When firms are dependent on manual labor, they will face certain typical problems. One of them is the late filing of suspicious activity reports (SAR). Then there is the case of bias and employee conflict of interest. We have the example of NatWest being fined £265 million where employee bias was evident in the laundering of nearly £400 million.
  • Investment: Newer and tougher AML reforms call for investment in technology as existing systems are not sophisticated enough. But with the volatility in the market continuing unabated and worsening geopolitical crisis, that is tough.

Simplifying the Complexity of Compliance with Magic FinServ

Magic FinServ brings efficiency and scalability by automating AML operations using Magic DeepSightTM.

Magic DeepSightTM is an extremely powerful tool that on the one hand extracts data from relevant fields in far less time than before and on the other checks and monitors for discrepancies from a plethora of complex data existing in diverse formats and raises alerts in a timely manner. Saving you fines for late filing of suspicious activity report (SAR) and ensuring peace of mind.

Customer due diligence: Before onboarding a new customer as well as before every significant transaction, banks and financial institutions must ascertain if they are at substantial risk for money laundering or dealing with a denied party.However, conductingchecks manually prolongs the onboarding and due diligence process, and is the leading cause of customer resentment and client abandonment.

We simplify the process and make it smoother and scalable. Our solution is powered by AI and RPA which makes the AML process more efficient in monitoring, detecting, reporting, and investigating money laundering and fraud along with compliance violations across the organization.

Magic DeepSightTM scours through the documents received from the customer during the onboarding process for onboarding and due diligence. These are verified against external sources for accuracy and for establishing credibility. Information is compared with credit bureaus to establish credit scores and third-party identity data providers to verify identity, Liens, etc.

Sanctions/Watchlist screening: One of the most exhaustive checks is the sanctions or the watchlist screening which is of paramount importance for AML compliance. The OFAC list is an extremely comprehensive list, that looks for potential matches on the Specially Designated Nationals (SDN) List and on its Non-SDN Consolidated Sanctions List.  

Magic FinServ simplifies sanctions compliance. Our powerful data extraction machine and intelligent automation platform analyze tons of data for watchlist screening, with the least possible human intervention. What could take months is accomplished in shorter time spans and with greater accuracy and reduced operational costs.

Transactions monitoring: As underlined earlier, there are extremely sophisticated means for carrying out money laundering activities.

One of them is layering, where dirty money is sneaked into the system via multiple accounts, shell companies, etc. The Malaysian unit of Goldman Sachs was penalized with one of the biggest fines of 2020 for its involvement in the 1MBD scandal, where several celebrities were the beneficiaries of the largesse of the fund. This was the first time in its 151-year-old history that the behemoth Goldman Sachs had pleaded guilty to a financial violation. It was fined $ 600 million. The other is structuring where instead of a lump sum deposit (large), several smaller deposits are made from different accounts.

Magic DeepSightTM can read the transactions from the source and create a client profile and look for patterns satisfying the money laundering rules.

Reducing false positives: Magic DeepSightTM uses machine learning to get better in the game of distinguishing legal and illegal transactions in time. As a result, businesses can easily affix rules to lower the number of false positives which are disruptive for business.

KYC: KYC is a broader term that includes onboarding and due diligence and ensuring that customers are legitimate and are not on Politically Exposed Persons (PEPs) or sanctions lists. Whether it is bank statements, tax statements, national ID cards, custom ID cards, or other unique identifiers, Magic DeepSightTM facilitates a compliance-ready solution for banks and fintechs. You not only save money, but also ensure seamless transactions, reduce the incidences of fraud, and not worry about poor customer experience.           

What do you gain when you partner with Magic FinServ?

  • Peace of mind
  • Streamlined processes
  • Comprehensive fraud detection
  • Minimum reliance on manual, less bias 
  • Cost efficiency and on-time delivery
  • Timely filing of SAR 

The time to act is now!

The costs of getting AML wrong are steep. The penalties for non-compliance with sanctions are in millions. While BitMex and NatWest have paid heavy fines – BitMex paid $ 100 million in fines, others like Credit Sussie suffered a serious setback in terms of reputation. Business licenses could be revoked. Firms also stand to lose legit customers when the gaps in their AML processes get exposed. No one wants to be associated with financial institutions where their money is not safe.

The astronomical AML fines levied by regulators indicate that businesses cannot afford to remain complacent anymore. AML fines will not slowdown in 2022 as the overall culture of compliance is poor and the existing machinery is not robust enough. However, you can buck the trend and avoid fines and loss of reputation by acting today. For more about our AML solutions download our brochure and write to us at mail@magicfinserv.com.

2022, began with a cautionary note. Stocks slumped and inflation spiked to unprecedented levels worldwide. There was massive disruption of the supply chain due to the pandemic. Just when we thought the worst was over, the breadbasket of Europe, Ukraine was drawn into a devastating war. The uncertainties and geopolitical tensions had a massive impact on the world’s economy – best reflected in the volatility of the stock markets.

It is clear that we are going through uncertain times. That the uncertainties will continue for a long time to come is definite. How must organizations then preempt the challenges lying ahead? What is the key to survival?

In this blog, we’ll attempt to answer these. But first, let us take stock of the primary challenges that organizations will face in 2022. For many, survival will depend on how they tackle the challenges mentioned below.

Key challenges 2022

1. Limited budget and spend: Faced with revenue and growth uncertainties, organizations are limiting spend on non-critical areas. While technology is a leveler, to make the best use of the dollars spent on technology, you must ensure that the processes are optimized first by investing in areas that deliver quick wins rather than aiming for the moonshot.

2. Great attrition and the battle for brains: With more than 19 million American workers quitting their jobs since April 2021, the disruption is massive. But holding on to low- talent employees isn’t effective in the long run.

3. Managing support function: With the WFH culture, the demands on the support function have increased exponentially. Fortunately, most of the time-consuming, repetitive, work in accounts payable, loans processing, KYC, AML, and onboarding can be handled more accurately and cost-effectively with AI and ML, and RPA.

4. Ensuring compliance in WFH: We have seen how the organization’s reputation takes a hit when it falls prey to data breaches as well as compliance failures as was the case with Uber and Panera Bread, where employee carelessness resulted in data breaches. However, an effective cloud strategy and cloud risk management approach navigates risks and improves customer experience. All by driving a collaborative ecosystem.

5. Getting data right: Surveys indicate that nearly a quarter of firms are concerned about fragmented and unreliable data. Though the amount of data has increased manifold times, it is unwieldy and of poor quality.

5. Getting rid of silos – integrating fast: Today one of the biggest problems with data is its existence in silos. You want to make your data useful; you will have to clean it up and structure it. You want to migrate to the cloud; you’d have to know how to make it cost- effective.

2022 would require Enterprises to Adapt, Consolidate, Reinforce with AI, ML, and the Cloud

Data, it is evident, will be playing a defining role in 2022. Whether it be for creating a strong governance framework, or for consolidating systems, data, and processes, or promoting a risk- averse culture. So,

  • Organizations must act fast and consolidate and reinforce their key capabilities
  • They must become agile and nimble – and learn how to manage their data faster than the others.
  • In a highly leveraged world with a fractured supply chain, organizations must get rid of multiple and disparate systems – the silos. They must integrate their processes. This cannot be done without bridging the silos and ensuring last mile process automation.

Magic FinServ: Making Enterprises Agile, Responsive, and Integrated with its IT Services Catalogue, Last Mile Process Automation, and DeepSightTM

Magic FinServ’s unique capabilities centered around data and analytics and the IT services catalog bring a differentiated flavor to the table and reinforce the organization’s key capabilities while navigating the challenges of data management, broken tech stacks, and scalability.

Our core competence is data while leveraging our cloud and automation capabilities: McKinsey estimates that many time-consuming and repetitive processes like accounting operations, payments processing, KYC and onboarding, and AML along with strategic functions like financial controlling and reporting, financial planning and analysis, treasury will have to be automated. Magic FinServ with its focus on data will be strategic to this initiative.

Comprehensive IT services catalog: We focus on multiple needs whether it be advisory, or cloud management and migration, platform engineering, production support, or quality engineering, DevOps and Automation, production support in an integrated manner to help our customers, whether it be fintech’s or financial institutions, modernize their platforms and Improve Time and Cost to Market.

Domain experience: The fintech and financial institutions’ business landscape is highly complex and diverse. This has been serviced through customized solutions which often create fragmentation and silos. With firms strategically focusing on which core competencies to fortify, you will need a partner that understands the complexities of your focus areas. We bring to the table a rare combination of financial services domain knowledge and new-age technology skills to give you a competitive advantage.

Speedy delivery, minimum dependence on manual effort: From our recent experiences, we know that excessive reliance on manually operated support functions is costly. Our comprehensive last mile process automation tool, Magic DeepSight TM , expedites the time required to turn mountainous data into insights, while meeting regulatory standards and ensuring compliance, with minimum human intervention.

Tailored solutions for financial institutions and fintech: Whether it is a KYC, AML, loans processing, expense management, the AI optimization framework utilizes structured and unstructured data to build tailored solutions that reduce the need for human intervention.

Recover costs quicker than the others: For firms worried about spiraling costs, or having no budget allocated for automation and optimization, our solutions, with a payback period of less than a year can be a huge game changer.

Introducing Magic DeepSight TM

Compliance-ready solutions: What organizations need today are compliance-ready solutions, as they can no longer afford to invest in building one. Our compliance-ready solution for KYC and onboarding is built for broker-dealers, custodians, corporates, fund admins, investment managers, and service providers and is in accordance with industry guidelines and local, national, and international laws.

Ensuring last mile process automation by speedily bringing all disparate processes into one environment. It is observed that when fintech scales, its IT system is put under immense pressure. As a result, organizations have to deal with disruption. Additional staff are then hired. Increasing costs. With our focus on cloud capability and automation and data-focused services we are in a position to facilitate the last mile process automation. Thereby bridging the gap that still exists in our daily workarounds. Also, DeepSight TM , a Magic FinServ platform with AI/ML and RPA at its heart, automates and integrates last mile business processes for improved user experience and enhanced benefits realization.

A precursor of tough times: Act Fast, Act Now!

The current situation is a precursor of tough times ahead. Jamie Dimon, CEO of JPMorgan, said in his annual address to shareholders last year, banks and Financial Institutions needed to adopt new technologies such as artificial intelligence and cloud technology “as fast as possible.”

So, the time to act is now. We understand your problems, and we have a solution to address those. For more information write to us or visit our website www.magicfinserv.com for a comprehensive overview of what we do.

Garbage In, Garbage Out: How Poor Data Quality Clogs Machine Learning Training Pipeline 

What is common in the success stories of businesses as diverse as Amazon, Airbnb, and Kakao Bank. The answer is data and a leadership that was relentless in the pursuit of good data quality. In the digital age, good quality data is a key differentiator – an invaluable asset that gives organizations an edge over competitors who have not been as dogged about the same (data quality). As a result, they are burdened with substandard, scattered, duplicate, and inconsistent data, that weighs them down more heavily than iron shackles. In the world businesses are operating in today, the divide is not been the big and the small organizations but between organizations who have invested in improving their data quality and those who have not. 

A single rotten apple spoils the barrel!    

We have all heard of the story about how a single rotten apple spoils a barrel. It is more or less the same story when it comes to data. Unclean, unrefined, and flawed data does more harm than good. Gartner estimates that poor quality data costs an organization $ 15 million per year. Though survey after survey talks about monetary losses – unclean data or data that has not been refined impacts more than the bottom line – it prevents businesses from deriving actionable insights from their data, leads to poor quality decisions, and drives dissatisfaction among all the people who matter – partners, vendors, and regulatory authorities. We have also heard of several instances where poor data quality has quickly snowballed into a major issue- like a money-laundering scam leading to loss of reputation as well.

Today when a majority of organizations are leveraging the power of AI and machine learning tools and investing millions to stay ahead of the curve, bad data can be a reason for not meeting the ROI. While organizations pour money for AI and ML tools, it is constrained due to bad quality data.      

Bad data hurts the American economy  

The impact of bad data on the American economy is not trickle-down, rather it is a gigantic leak that is hard to plug. Collectively, the impact of bad decisions made from data that are flawed goes into millions and billions. Ollie East, Director, Advanced Analytics and Data Engineering, of the public accounting and consulting firm, Baker Tilly, says that bad data costs the American businesses about $3 trillion annually, and breeds bad decisions made from having data that is just incorrect, unclean, and ungoverned. 

Banks and FIs are no exception to the rule. In fact, because of the privacy and regulatory requirements – they stand to lose more due to bad data. Of the zebibytes of data (including dark data) in existence organization-wide today, organizations are capitalizing only a minuscule percentage. Banks and FIs can ensure that they do not lose business, revenue, and clientele on account of poor data quality. It only takes a bit of effort and strategic planning. Further, the phenomenal success of new-age technologies like AI and machine learning has changed the rules of the game and has enabled banks and FIs to fish value from even the dark data – if only they undertake a planned approach to data standardization, data consistency, and data verification, and ensure that is streamlined for use again and again. Organizations must also account for the new data that enters the workflows and pipelines and ensure that a suitable mechanism is in place to ensure that it is always clean and standardized.     

To reiterate, why lose on the competitive advantage? Here’s a look at how organizations – banks and FIs- can invoke the power of cleaner, structured, data to make their processes crisper, leaner and undeniably more efficient.  

Step 1: Pre-processing data – making data good for downstream processes        

Pre-processing of data is the first step in the journey towards cleaner and refined data. Considering that not many organizations today can claim that their data quality meets expectations – the Harvard Business Review  states: “only 3% of companies’ data meets basic quality standards ” – pre-processing of data is critical for the following reasons: 

  • Identifying of what’s wrong with the organization’s data. What are the core issues? 
  • As the data is more likely to be used again and again in workflows, processes and systems enterprise-wide, good quality data with the right encryptions minimizes conflict of interest and other such discrepancies.        
  • Also, as most organizations are likely to be using some kind of AI and ML for their processes involving this data – it is better to get it in shape to reap the maximum benefits. 

Garbage In Garbage Out – The true potential of AI and ML can be leveraged only when data quality is good 

Today, data scientists and analysts spend more time pre-processing data for quality (fine-tuning it), than analyzing it for business and strategic insights. This iterative pre-processing of data even though extremely time-consuming is important because if organizations feed “bad or poor-quality unrefined data” into the AI model it will spew (to put it across literally) garbage. Garbage In, results in Garbage Out. To leverage the true potential of AI and ML, it is essential that the quality of data being fed into the machine-learning pipeline downstream is of high quality. 

There are of course other substantial benefits as well. One, when the data is cleaned at the point of capture or during entry, banks and FIs have a cleaner database for future use. For example, by preventing the entry of duplicates at the point of capture (either via manual or automated means), organizations are spared from doing menial and repetitive work. It is also relatively easy to build the training model once the data is refined and streamlined. And when banks and FI have a more dependable AI pipeline (thanks to cleaner data) they can gain valuable insights that give them a strategic advantage.    

Carrying out data quality checks 

For ensuring that their data is up-to-date and foolproof, there are several levels of checks or quality tests including the quick-fact checking of data against a universal known truth – such as the age field – in a dataset age filed cannot have a negative value nor can the name field be null. However, a quick-fact check is a basic check (tests only the data and not the metadata which is the source of extremely valuable information such as the origin of data, creator of data, etc.). Therefore, for a comprehensive test of data quality, holistic or historical analysis of datasets must be carried out where organizations test individual data for authenticity or compare them with historical records for validation.  

Manual testing: Herein, staff manually verifies the values for data types, length of characters, formats, etc. The manual verification of the data is not desirable as it is exceedingly time-consuming. It is also highly error-prone. Instead, there are options such as open-source projects and in some cases, coded solutions built in-house, but both are not as popular as automated data quality testing tools.  

Automated data quality testing tools: Using advanced algorithms, these tools invariably make it easier for organizations to test data quality in a fraction of the time that manual effort takes (using data matching techniques). However, as reiterated earlier, machines are as good as the training they receive. If unclean, flawed data is poured into the training pipeline, it clogs the machine and prevents it from giving the desired results.   

The machines have to be taught like humans to understand and manipulate data so that exceptions can be raised and only clean filtered data remain in the dataset. Organizations can gain intelligence from their data either through rules-based engines or machine learning systems. 

1. Rules-based system: Rules-based systems work on a set of strict rules that suggest “if” a certain criterion is met or not met, then what follows. Rules-based data quality testing tools allow organizations to validate datasets against custom-defined data quality requirements. Rule-based systems requiring less effort and is also less risky – false positives are not a concern. It is often asked if rules-based tools and processes are slowly becoming antiquated as banks and FIs deal with an explosion of data. Probably not. They are still a long way from going out of fashion. It still makes sense to use the rules-based approach where the risk of false positives is too high and hence only rules which ensure 100 percent accuracy can be implemented. 

2. Machine learning systems: A machine learning system simulates human intelligence. It learns from the data that it is given (training model). Like a child that learns from its parent, it picks the good, the bad, and the ugly. Hence businesses must be extremely careful at the onset itself. They cannot expect optimum results if they are not careful with the quality of the data used for training. When it comes to its learning capacity and potential, however, ML-based systems’ capacity is infinite.  

Though there are several ways for the machine to learn, supervised learning is the first step. Every time new data gets incorporated in the datasets, the machine learns. The element of continuous learning means that in time it would require minimum human interference – which is good as banks and FIs would like to engage their manpower in far more critical tasks. As machines interpret and categorize data using its historical antecedents, it becomes much smarter and indefinitely more capable than humans. 

In the realm of dark data 

Every day banks and FIs generate, process, and store humongous amounts of data or information assets. Unfortunately, much of this data (nearly 80%) remains in the dark. Banks and FIs rarely tap into it for business insights and for monetizing the business. However, machine learning systems can help organizations unearth value from dark data with minimum effort. Learning, in this case, begins with making data observations, finding patterns and eventually using it to make good strategic decisions. All based on historical evidence or previous examples. What the system simply does here is alert the supervisor (about the exception) and then process that information and learn – that is what continuous learning does long term. 

Data quality is a burning issue for most organizations 

“By 2022, 60% of organizations will leverage machine-learning-enabled data quality technology to reduce manual tasks for data quality improvement.” Gartner  

At Magic FinServ, we believe that high-quality data is what drives top and bottom-line growth. Data quality issues disrupt processes and result in escalated costs as it calls for investment in re-engineering, database processing, and customized data scrubbing. And more for getting data in shape. 

Organizations certainly wouldn’t want that as they are running short of time already. Knowing that manual testing of data quality is not an option – it is expensive and time-consuming, it is cost-effective and strategically sound to rely on a partner like Magic FinServ with years of expertise.

Ensuring quality data – the Magic FinServ way

Magic FinServ’s strategy to ensure high-quality data is centered around its key pillars or capabilities – people, in-depth knowledge of financial services and capital markets, robust partnerships (with the best-in-breed), and a unique knowledge center (in India) for development, implementation, upgrade, testing, and support. Our capabilities go a long way in addressing the key challenges enterprises face today related to their data quality, spiraling data management costs, and cost-effective data governance strategy with a well-defined roadmap for enhancing data quality. 

Spinning magic with AI and ML: Magic FinServ has machine learning based tools to optimize operational cost by using Al to automate exception management and decision making. We can deliver a savings of 30% – 70% in most cases. As a leading a digital technology services company for the financial services industry, we bring a rare combination of capital markets domain knowledge and new-age technology skills, enabling leading banks and FinTech’s to accelerate their growth.  

Cutting costs with cloud management services: We help organizations manage infrastructure costs offering end-to-end services to migrate (to cloud from enterprise), support and optimize your cloud environment.

Calling the experts: We can bring in business analysts, product owners, technology architects, data scientists, and process consultants at a short notice. Their insight in reference data, including asset classes, entities, benchmarks, corporate actions and pricing, brings value to the organization. Our consultants are well-versed in technology. Apart from traditional programming environments like Java and Microsoft stack, they are also well versed in data management technologies and databases like MongoDB, Redis Cache, MySQL, Oracle, Prometheus, Rocks dB, Postgres, and MS SQL Serve. 

Partnerships with the best: And last but not least, the strength of our partnerships with the best in the industry gives us an enviable edge. We have not only tied with multiple reference data providers to optimize costs and ensure quality, but have partnership with reputed organizations dealing with complex and intractable environments, multi-domains, covering hundreds of thousands of data sources, to help our clients create a robust data governance strategy and execution plan.

So that is how we contain costs and also ensure that the data quality is top notch. So why suffer losses due to poor data quality. 

Connect with us today by writing to us at mail@magicfinserv.com.

The Banking and Financial Services Sector and the fintechs supporting them have not been unaffected by the winds of change sweeping across the business landscape. But unlike the past, technology has proved to be an equalizer. Size is no longer a necessary condition for success. Today we have Challenger Organizations, typically SMEs and large institutions alike, competing on a level playing field, and whosoever simplifies their processes or automates first gains an edge. Examples of how Intuit, Square, and similar Challenger Organizations redefined the meaning of Customer experience are proof enough. Automation, elimination of repetitive manual tasks, and consolidation of redundant activities into fewer steps have played a crucial role in enhancing Straight Through Processing (STP). 

Straight Through Processing has hitherto been addressed by optimizing underlying applications, eliminating data silos, and integrating applications better. While process automation through RPA and similar technologies have helped optimize downstream processes, the manual effort was still significant due to the disparate data sources that could not be consolidated and integrated. It is this final boundary that is sought to be breached through the application of Emerging Technologies. With a holistic end-to-end straight-through processing (STP), banks and FIs have taken a quantum leap forward and what would have once taken days to accomplish now takes minutes. STP removes the need for manual intervention. The only time human intervention is “ ideally” required is during “exception handling ” or “exceptions processing”  – when the system sees something that is unusual and raises a red flag. The human annotator then sets about making the necessary changes.  

White whales for STP implementation or Taming the white whales (with STP)

STP implementation is ideal for processes that involve a lot of repetitive work. The costs of system integration (might) dissuade many smaller players from considering it. However, like Captain Ahab’s relentless pursuit of the White Whale – Moby Dick, digital transformation experts have relentlessly argued the case for STP implementation (without similar calamitous consequences) in some of the most tiresome and time-consuming processes like KYC, loans processing, foreign exchange transaction handling,  accounts payables amongst others. Organizations that must meet quality SLAs as part of the business agreement have much to lose if they do not innovate on the technology front. Humans are more liable to make errors –  attach a wrong document, classify a document incorrectly (highly probable if there are 100 odd classifications to choose from), or simply feed data incorrectly into the system. And in the event this takes place, the likelihood of not meeting the SLAs (resulting in client dissatisfaction) is high.

 Secondly, banks and FIs, manually administering processes like KYC, have no time for value-added activity (like sales and customer retention and experience) as they are busy meeting the deadlines. As manual labor is both slow and expensive, there is a considerable backlog. Let’s take the example of the KYC process – a  McKinsey report states that banks generally employ about 10 percent of the workforce in financial-crime-related activities. Now that is a lot in terms of labor cost. The report has also indicated that KYC reviews were often the costliest activities handled by the bank.

With STP, banks and the financial services sector can eliminate a lot of paperwork, many unnecessary checkpoints (translating into unnecessary headcount), manual data entry while ensuring that SLAs are met and invoices, KYC, onboarding, accounts payable and accounts receivable, and other such processes and document-oriented activities are conducted quickly and cost-efficiently and with relatively fewer margins of error. So that organizations can ensure higher levels of transparency and trust and a good customer experience.

Cost of quality   

Let’s be honest; even the best human keyers/classifiers make more errors or mistakes than machines. While an error percentage of 2 to 5 % might not seem much, if we apply the 1-10-100 rule for the “cost of quality” and take into account a million documents that are being classified and whose data is being extracted, 5 % does make a huge difference. Automatically that would translate into a lot of work that would require human intervention. For every error that could be prevented, the cost of rectifying it is 10 times more. Leaving an error unattended is costlier – 100 times more expensive than doing it right the first time. 

Machines, however, are more capable. A Mckinsey report states, “ Assuming that standardization and coding of rules are performed correctly, quality can be improved significantly (by a range of 15 to 40 percent, experience indicates). Manual errors are reduced, and the identification and documentation of risks are improved. Rework loops can be shortened as well, as “first time right” ratios and regulatory targets are met more quickly.”

And now comes the really tricky part, since 100 percent accuracy is still unthinkable even for machines. When we are talking about documents, and relevant data fields, an accuracy of 99 % at the character level does not ensure STP. If documents need validation from their human supervisors due to high error rates,  zero STP will ensue.  Here we’d need something more robust than RPA. With machine learning (ML) and advanced capture, it is possible to increase accuracy to validate data using advanced rules.  We’d need a system that constantly adjusts and optimizes data. So every time the system encounters a variance or an anomaly, it adapts ( taking help from the human-in-loop) and improvises, becoming better after each iteration.

We would also have to take into account the variance in straight-through processing when it comes to structured and unstructured documents. While a standardized document such as a 10k will enable higher levels of  STP, semi-standardized documents such as invoices and unstructured documents such as notes and agreements will allow lower levels of STP.  

Simplifying banking processes

Today the imperative for banks and FIs to simplify their processes is huge. Future growth today is dependent on the ease with which banks and FIs can conduct their business. There is no escaping that! Reiterating the need for simplification, Hessel Veerbek, partner strategy, KPMG Australia, writes about “how some banks and insurers have replaced key elements of their core systems and consolidated their ancillary systems to rationalize their IT estate, modernize their capabilities, reduce costs and, at the same time, provide the capabilities to adapt and evolve their business models to secure future growth.”

Banks need to simplify operation by a core banking system that takes care of processes like loans processing and accounts payable end-to-end. At the heart of a simplified and automated banking architecture is end-to-end STP, which can be a complex undertaking, but as leading banks have shown, it is worth the trouble as it boosts efficiency. The challenges to STP incorporation are really in the mindset of organizations as “complex processes, high-risk customers and non-standard accounts” are still excluded from the purview of STP. It’s typical for organizations to consider STP while conducting low-risk tasks such as KYC for low-risk accounts in the KYC process. However, as the Mckinsey report suggests, if applied with dexterity and foresight, STP can eventually be enabled for the high-risk segment of customers as well. But here, a simple rules-based approach will not suffice, and organizations would need to rely on data science to create a system that will ensure a reliable output.

Augmenting human labor  – when machine learning tools perform the task and optimize it as well

The question is not really about how STP reduces the need for manual intervention – it is about how it augments human skills. The time it takes a human to classify and extract information from documents is disproportionate to the gains. Considering that almost 100 % of all data entry tasks can be automated and results can be obtained in a fraction of the time,  it makes sense to invest in tools that would ensure end-to-end automation. With STP, banks and Fintechs can not only eliminate the need for manual keying and classification of data, but in time sophisticated machine learning tools can also eliminate the need to verify that data manually.        

Getting to zero-touch! 

For true STP, we want an error rate that is such that human intervention is not required. This STP is the percentage or number of documents that go through the system with zero-touch or human contact. If the error rate or adverse media reaction is high, every document would have to be reviewed. Hence organizations must work on increasing accuracy by leveraging the power of AI and ML tools. 

If we are talking about cost efficiency, the need for software that easily integrates with organization-wide legacy systems is also a  prerequisite.  

The automation success story is not only about STP. 

It is important to remember that the automation success story does not depend on STP alone; other factors like investment costs, capital performance, cycle time, ROI, headcount reduction also matter. While “customer satisfaction” and “experience “ are good to have, Net Present Value (NPV), cost efficiency, and headcount reductions matter a lot. After all, leaner, nimbler, and more efficient operations are what most organizations are after.

While considering STP, it is also essential to do some homework regarding the investment costs, the complexity of the process (number of data elements that must be extracted, variance in documents, etc. ), cycle time, and the headcount reduction, etc. 

Experience tells us that the shift from highly manual-oriented processes to STP is not easy. It requires massive levels of patience and commitment as it takes time to reach the desired levels of accuracy. The actual test of STP success for any process depends on determining with a high degree of precision if a task has been executed accurately or not. A high rate of error or human intervention results in zero STP.   

Regardless of the challenges underlined earlier, STP remains a significant milestone in any organization’s journey towards automation. Banks and FIs that have successfully implemented STP have reaped many visible benefits. With Straight Through Processing, banks and FIs can choose to re-direct their efforts towards customer experience and retention, as they now have the time and bandwidth. When banks and FIs automate invoices and payments, they pave the way for a happier customer and employee experience. 

The question today is not whether STP is the ultimate test for automation progression; the question today is whether organizations can afford to do without STP – considering the astronomical costs of processing files and increased competition. Magic FinServ, with its years of experience serving a diverse clientele set comprising some of the top American banks and Fintechs, is well acquainted with the opportunities and risks associated with process optimization and simplification using AI and ML. If organizations are not careful, the costs could escalate disproportionately and disrupt the drive towards digital transformation.   Magic FinServ helps you navigate uncharted waters by leveraging our understanding of the financial services business to re-engineer existing applications, design new platforms, and validate machine learning solutions to suit your business needs. To explore our solutions, reach out to us mail@magicfinserv.com

Enterprises have increasingly realized that they must implement AI to succeed as digital natives are fast outpacing the ones relying on monolithic architectures. However, lack of synchronization between downstream and upstream elements, failure to percolate the AI value and culture in the organization’s internal dynamics, unrealistic business goals, and lack of vision often means that the AI projects either get stuck in a rut or fail to achieve the desired outcomes. What seemed like a sure winner in the beginning soon becomes an albatross around one’s neck.

Mitigating the pitfalls with a well-drawn and comprehensive AI roadmap aligned to company needs  

According to a Databricks report, only one in three AI and predictive analytics projects are successful across enterprises. Most AI projects are time-taking – it takes six months to go from the concept stage to the production stage. Most executives admit that the inconsistencies in AI adoption and implementation stems from inconsistent data sets, silos, and lack of coordination between IT and management and data engineers and data scientists. Then there’s the human element that had to be taken into account as well. Reluctance to invest, lack of foresight, failure to make cultural changes are as much responsible for falling short of the AI targets as the technical aspects enumerated earlier.

This blog will consider both the technical and the human elements vital for conducting a successful AI journey. To mitigate any disappointment that could accrue later, enterprises must assess the risk appetite, ensure early wins, get the data strategy in place, drive real-time strategic actions, implement a model and framework that resonates with the organization’s philosophy while keeping in mind the human angle – ensuring responsible AI by minimizing bias.

Calculating the risk appetite – how far the organization is willing to go? 

Whether the aim is to enhance customer experience or increase productivity, organizations must be willing to do some soul searching and find out what they are seeking. What are the risks they are prepared to take? What is the future state of readiness/ AI maturity levels? And how optimistic are things at the ground level?  

From the utilitarian perspective, investing in a completely new paradigm of skills and resources which might or might not result in ROI (immediately) is debatable. However, calamities of a global scale like COVID-19 demand an increased level of preparedness. Businesses that cannot scale up quickly can become obsolete; therefore, building core competencies with AI makes sense. Automating processes mitigates the challenges of the unforeseeable future when operations cannot be reliant on manual effort alone. So even if it takes time to reach fruition, and all projects do not translate into the desired dividends, it is a risk many organizations willingly undertake.

There is a lot at stake for the leadership as well. Once AI is implemented, and organizations start to rely on AI/ML increasingly, the risks compound. Any miscalculation or misstep in the initial stages of AI/ML adoption could cause grievous damage to the business’s reputation and its business prospects. Therefore, leadership must gauge AI/ML risks.     

Importance of early wins – focussing on production rather than experimentation.  

Early wins are essential. It elicits hope across an organization. Let us illustrate this with an example from the healthcare sector – the ‘moon shot’ project. Launched in 2013 at the MD Anderson Cancer Centre, the ‘moon shot project’ objective was to diagnose and recommend treatment plans for certain forms of cancer using IBM’s Watson cognitive system. But as the costs spiraled, the project was put on hold. By 2017, “moon shot” had accumulated costs amounting to $62 million without being tested on patients. Enough to put the management on tenterhooks. But around the same time, other less ambitious projects using cognitive intelligence were showing remarkable results. Used for simple day-to-day activities like determining if the patient needed help with bills payment and making reservations, AI drove marketing and customer experience while relieving back-officer care managers from the daily grind. MD Anderson has since remained committed to the use of AI.

Most often, it makes sense to start with process optimization cases. When a business achieves an efficiency of even one percent or avoids downtime, it saves dollars – not counting the costs of workforce and machinery. It is relatively easy to calculate where and how we can ensure cost savings in existing business cases instead of exploring opportunities where new revenue can be driven, as illustrated by the MD Anderson Cancer Centre case study. As we already know how the processes operate, where the drawbacks are, it is easier to determine areas where AI and ML can be baked for easy wins. The data is also in a state of preparedness and requires less effort.

In the end, the organization will have to show results. They cannot experiment willy-nilly. It is the business impact that they are after. Hence the “concept of productionize” takes center stage. While high-tech and glamorous projects look good, these are best bracketed as “aspirational.” Instead, the low-hanging fruit that enables easy gains should be targeted first.

The leadership has a huge responsibility, and to prioritize production, they must work in tandem with IT.  Both should have the same identifiable business goals for business impact. 

Ensuring that a sound data strategy is in place – data is where the opportunity lies!

If AI applications process data a gazillion times faster than humans, it is because of the trained data models. Else, AI apps are ordinary software running on conventional code. It is these amazing data models trained to carry out a range of complex activities and embedding NLP, computer vision, etc., that makes AI super-proficient. As a result, the application or system can decipher the relevant text, extract data from images, generate natural language, and carry out a whole gamut of activities seamlessly. So if AI is the works, data is the heart.          

Optimizing data pool

Data is the quintessential nail in the absence of which all the effort devised for drafting an operating model for data and AI comes to naught. Data is the prime mover when it comes to devising an AI roadmap. For data to be an asset, it must be “findable, accessible, interoperable, and reusable”. If it exists in silos, data ceases to be an asset. It is also not helpful if it exists in different formats. It is then a source of dubiety and must be cleaned and formatted first. Without a unique identifier (UID), attached data can create confusion and overwrite. What the AI machinery needs is clean, formatted, and structured data that can easily be baked on existing systems. Data that can be built once and used in many use cases is fundamental to the concept of productized data assets.

It serves to undertake data due diligence or an exploratory data analysis (EDA). Find out where data exists, who is the owner, how it can be accessed, linkages to other data, how it can be retrieved, etc., before drawing out the roadmap. 

The kind of data defines the kind of machine learning model that can be applied, for example, for supervised machine learning models, data and labels are essential for enabling the algorithm to draw an inference about the patterns in the label, whereas unsupervised learning comes when data does not have labels. And transfer learning when the data that an existing machine learning model has learned is used to build a new use case.

Once the data has been extracted, it must be validated and analyzed, optimized, and enriched by integrating it with external data sources such as those existing online or in social media and to be fed into the data pipeline. A kind of extract, transform and load. However, if it is done manually, it could take ages and still be biased and error-prone. 

Drawing the data opportunity matrix to align business goals with data

Once the existing data has been sorted, find how it can be optimized for business by integrating it with data from external sources. For this purpose, an opportunity matrix, also known as the Ansoff matrix comes in handy. A two-by-two matrix that references new business and current business with the data subsets (internal and external), it aids the strategic planning process and helps executives, business leaders understand where they are in terms of data and how they would like to proceed further.   

Driving real-time strategic actions for maximum business impact using AI: Leadership matters 

Real-time strategic actions are important. For example, millennial banks and financial institutions must keep pace with customer expectations or else face consequences. By making the KYC process less painstaking with AI, banks and FinTechs can drive unexpected dividends. When the KYC is done manually, it is time taking. By the time the KYC is complete, the customer is frustrated. When AI and Machine Learning capabilities are applied to existing processes, organizations reduce manual effort and errors substantially. The costs of conducting the KYC are reduced as well. However, the biggest dividend or gain that organizations obtain is in the customer experience that rebounds once the timelines ( and human interaction) are reduced. That is like having the cake and eating it too!    

SAAS, on-prem, open-source code – finding out what is best!

If it is the efficiency and customer experience that an enterprise is after, SaaS works best. Hosted and maintained by a third party, it frees the business from hassles. However, if one wants complete control over data and must adhere to multiple compliance requirements, it is not a great idea. On-prem, on the other hand, offers more transparency and is suitable for back-end operations in a fintech company for fast-tracking processes such as reconciliations and AML/KYC. Though SaaS is feasible for organizations looking for quality and ease of application, open-source code produces better software. It also gives control and makes the organization feel empowered.          

Conclusion: AI is not a simple plug and play 

AI is not a simple plug-and-play. It is a paradigm shift and not everyone gets it right the first time. Multiple iterations are involved as models do not always give the desired returns. There are challenges like the diminishing value of data which would require organizations to broaden their scope and consider a wider data subset for maximizing accuracy.  

Notwithstanding the challenges, AI is a proven game-changer. From simplifying back-office operations to adding value to day-to-day activities, there is a lot that AI can deliver. Expectations, however, would have to be set beforehand. The transition from near-term value to closing in on long-term strategic goals would require foresight and a comprehensive AI roadmap. For more information on how your organization could use AI to drive a successful business strategy, write to us at  mail@magicfinserv.com to arrange a conversation with our AI Experts.     

2020-2021 marked a new epoch in the history of business. For the first time, a massive percentage of the workforce was working from home. While employees struggled to cope with the limitations of working virtually, artificial intelligence (AI) emerged as a reliable partner for enterprises worldwide. With AI, enterprises were assured that business processes were not disrupted due to the scarcity of labor and resources.  

Now that the worst seems over, there are more reasons than ever to invest in AI. AI has been an infallible ally for many organizations in 2020. It helped them meet deadlines and streamline internal operations while eliminating wasteful expenditure. It helped them cope with burgeoning workloads. The impact AI had on employee productivity was significant. By unfettering staff in back and middle offices from the cycle of mundane, repetitive, and tiresome tasks, AI-enabled the workforce to engage in high-value tasks. 

So even as employees return to the office in the coming days,  many organizations will continue to amplify their AI efforts. Wayne Butterfield, director of ISG Automation, a unit of global technology research and advisory firm ISG, attributes this new phenomenon to the powerful impact AI had last year. He says, “As the grip of the pandemic continues to affect the ability of the enterprise to operate, AI in many guises will become increasingly important as businesses seek to understand their COVID- affected data sets and continue to automate day-to-day tasks.”

Indeed, in the banking and financial sector, the benefits driven by AI in the past year were monumental. It ensured frictionless interactions, cut repetitive work by half, and reduced error, bias, and false positives – the result of human fallacies – significantly. What organizations got was a leaner and more streamlined, and efficient organization. So there is no question that the value driven by AI in domains like finance and banking, which rely heavily on processes, will only continue to grow in the years to come. 

Setting pace for innovation and change

The pandemic has redefined digital. With enterprises becoming more digitally connected than ever before, it is AI that helps them stay operational. As a report from the Insider indicates, there will be significant savings in the middle, back, and front office operations if AI is incorporated. Automation of middle-office tasks can lead to savings of $70 billion by 2025. The sum total of expected cost savings from AI applications is estimated at $447 billion by 2023. Of this, the front and middle office will account for $416 billion of the aggregate.  

That AI will set the pace for innovation and change in the banking and financial services sector is all but guaranteed. The shift towards digital had started earlier; the pandemic only accelerated the pace. So here are some of the key areas where Fintechs and banks are using AI :   

  • Document Processing  
  • Invoice processing
  • Cyber Security
  • Onboarding/KYC 

Document processing with AI 

Enterprises today are sitting on a data goldmine that comes from sources as diverse as enterprise applications, public/private data sets, and social media. However, data in its raw form is of no use. Data, whether it is in textual, pdfs, spreadsheets, have to be classified, segregated, summarized and converted into formats (JSON, etc.) that can be understood by machines and processes before they can be of use to the organization. 

Earlier, image recognition technologies such as OCR were used for document processing. However, their scope is limited given that organizations deal with humongous amounts of data in diverse formats including print, and handwritten, all of which are not recognizable with OCR. Document processing platforms have a distinct advantage over traditional recognition technologies such as OCR and ICR. The system is trained first using data sets, and a core knowledge base is created. In time the knowledge base expands, and the tool develops the ability to self-learn and recognize content and documents. This is achieved through the feedback or re-training loop mechanism under human supervision. Realizing that artificial intelligence, machine learning, natural language processing, and computer vision can play a pivotal role in document processing, organizations are increasingly relying on these to enhance the efficiency of many front and back-office processes.  

Invoice Processing and AI

Covid-19 has intensified the need for automated Accounts Payable processes. Organizations that were earlier relying on manual and legacy systems for invoice processing were caught off-guard as employees were forced to work from home. Ensuring timely delivery on payment approvals became a challenge due to archaic legacy practices and an increasing number of constraints. Then there was the question of enhanced visibility into outstanding payments. All this led to chaos in invoice processing. A lot of frayed tempers and missed deadlines.

A major chunk of invoice processing tasks is related to data entry. Finance and accounts personnel shift through data that comes from sources such as fax, paper, and e-mail. But a study on 1000 US workers reiterated that no one likes data entry. The survey indicated that a whopping 70 percent of the employees were okay if data entry and other such mundane tasks were automated. With automated invoice processing, it is possible to capture invoices from multiple channels. Identify and extract data (header and lines) using validation and rules. And best in time, with little human supervision, become super proficient in identifying relevant information. It can also do matching and coding.  Magic FinServ’s Machine Learning algorithm correctly determined General Ledger code to correctly tag the invoice against an appropriate charge code and finally, using RPA, was able to insert the code on the invoice.    

Banks and other financial services stand to gain a lot by automating invoice processing. 

  • By automating invoice processing with artificial intelligence, organizations can make it easier for the finance staff and back-office team to concentrate on cash-generating processes instead of entering data as -a typical administration function. 
  • Automating the accounts payable process for instance, can help the finance teams focus on tasks that generate growth and opportunities. 
  • An automated invoice processing provides enhanced visibility into payments and approvals.
  • It speeds up the invoice processing cycle considerably as a result; there are no irate vendors
  • It makes it easier to search and retrieve invoices.      

Cyber Security and AI

Cybersecurity has become a prime concern with the enterprises increasing preference for cloud and virtualization. Cybersecurity concerns became graver during Covid-19 as the workforce, including software developing teams, started working from home. As third parties and vendors were involved in many processes as well, it became imperative for organizations to ensure extreme caution while working in virtualized environments. Experiences from the past have taught us that data breaches spell disaster for an organization’s reputation. We need to look no further than Panera bread and Uber to realize how simple code left in haste can alter the rules of the game. Hence a greater impetus for the shift left narrative where security is driven in the DevOps lifecycle instead of as an afterthought. The best recourse is to implement an AI-driven DevOps solution. With AI baked into the development lifecycle, organizations can accelerate the development lifecyc in the present and adapt to changes in the future with ease.

Onboarding/KYC and AI

One of the biggest challenges for banks is customer onboarding and KYC. In the course of the KYC, or onboarding banks have to handle thousands, sometimes even millions of documents. And if that were not enough, they also have to take account of exhaust data and the multiple compliances and regulatory standards. No wonder then that banks and financial institutions often fall short of meeting the deadlines. Last year, as the Covid-19 crisis loomed large, it was these tools powered with AI and enabled with machine learning that helped accelerate paperwork processes. These digitize documents and extract data from it. And as the tool evolves with time, it makes it easier for the organization to extract insights from it. 

Let us take the example of one prominent Insurtech company that approached Magic FinServ for the resolution of KYC challenges. The company wanted to reduce the time taken for conducting a KYC and for SLAs roll-out of new policies gained confidence and customer appreciation as Magic’s “soft template” based solution augmented by Artificial Intelligence provided them the results they wanted.  

Tipping point

Though banks and financial institutions were inclining towards the use of AI for making their processes robust, the tipping point was the pandemic. The pandemic made many realize that it was now or never. This is evident from the report by the management solutions provider OneStream. The report observed that the use of AI tools like machine learning had jumped from about 20% of enterprises in 2020 to nearly 60% in 2021. Surprisingly, analytics firms like FICO and Corinium that a majority of top executives (upwards 65%) do not know how AI works. 

At Magic FinServ, our endeavor is to ensure that the knowledge percolates enterprise-wide. Therefore, our implementation journey starts with a workshop wherein our team of AI engineers showcases the work they have done and then engages in an insightful session where they try to identify the areas where opportunities exist and the deterrents. Thereafter comes the discovery phase, where our team develops a prototype. Once the customer gives the go-ahead as they are confident about our abilities to meet expectations, we implement the AI model that integrates with the existing business environment. A successful implementation is not the end of the journey as we keep identifying new areas of opportunities so that true automation at scale can be achieved.     

Catering to Banks and FinTechs: Magic FinServ’s unique AI optimization framework    

At Magic FinServ, we have a unique AI Optimization framework that utilizes structured and unstructured data to build tailored solutions that reduce the need for human intervention. Our methodology powered by AI-ML-NLP and Computer vision provides 70% efficiency in front and middle office platforms and processes. Many of our AI applications for Tier 1 investment, FinTechs, Asset Managers, Hedge Funds, and InsuranceTech companies have driven bottom and top-line dividends for the businesses in question. We ensure that our custom-built applications integrate seamlessly into the existing systems and adhere to all regulatory compliance measures ensuring agility. 

For some time now, asset managers have been looking at ways to net greater profits by optimizing back-office operations. The clamor to convert back-office from a “cost-center” to a “profit center” is not recent. But it has increased with the growth of passive investment and regulatory controls. Moreover, as investment fees decline, asset managers look for ways to stay competitive. 

Back-office is where AI and ML can drive massive business impact. 

For most financial organizations considering a technology upgrade, it is the back office where they must start first. Whether reconciliation or daily checkout or counterparties, back-office processes are the “low-hanging fruits” where AI and ML can be embedded within existing architecture/tools without much hassle. The investment costs are reasonably low, and financial organizations are generally assured of an ROI if they choose the appropriate third-party vendor with expertise in handling such transitions.         

Tasks in the back-office that AI can replace

AI can best be applied to tasks that are manual, voluminous, repetitive, and require constant analysis and feedback. This makes back-office operations/processes a safe bet for AI, ML, and NLP implementation. 

The amount of work that goes behind the scenes in the back office is exhaustive, never-ending, and cumbersome. Back-office operatives are aided in their endeavors by core accounting platforms. Accounting platforms, however, provide the back-office operator with information and data only. Analysis of data is primarily a manual activity in many organizations. As a result, the staff is generally stretched and has no time to add value. Silos further impede process efficiency, and customer satisfaction suffers as the front, back, and middle offices are unable to work in tandem.  

While there is no supplementing human intelligence, the dividends that accrue when AI is adopted are considerable. Efficiency and downtime reduction boost employee and organization morale while driving revenue upstream.

This blog will consider a few use cases from the back-office where AI and ML can play a significant role, focusing on instances where Magic FinServ was instrumental in facilitating the transition from manual to AI with substantial benefits.  

KYC: Ensuring greater customer satisfaction 

Data that exists in silos is one of the biggest challenges in fast-tracking KYC. Unfortunately, it is also the prime reason behind a poor customer experience. The KYC process, when done manually, is long and tedious and involves chasing clients time and again for the information. 

With Magic DeepSight’s™ machine learning capabilities, asset managers and other financial institutions can reduce this manual effort by up to 70% and accomplish the task with higher speed and lower error rate, thereby reducing cost. Magic DeepSight™ utilizes its “soft template” based solution to eliminate labor-intensive tasks. It has enabled several organizations to reduce the time taken for KYC and overall improve SLAs for new client onboarding.  

Reconciliation: Ensuring quicker resolution

As back-office operations are required to handle exceptions quickly and accurately, they need manual effort supplemented by something more concrete and robust. Though traditional tools carry out reconciliation, many organizations still resort to spreadsheets and manual processes, and hence inconsistencies abound. As a result, most organizations manually reconcile anywhere between 3% to 10% volume daily.

So at Magic FinServ, we designed a solution that can be embedded/incorporated on top of an existing reconciliation solution. This novel method reduces manual intervention by over 95% using artificial intelligence. This fast-tracks the reconciliation process dramatically, ensures quicker time to completion, and makes the process less error-prone. Magic FinServ implemented this ‘continuously learning’ solution for a $250B AUM Asset Manager and reduced the trade breaks by over 95%.

Fund Accounting: Ensuring efficiency and productivity 

Fund accounting can be made more efficient and productive with AI. Instead of going through tons of data in disparate formats, by leveraging the powers of AI, the back office can analyze information in income tax reports, Form K-1 tax reports, etc., at a fraction of time taken manually and make it available for dissemination. For example, Magic FinServ’s Text Analytics Tool, which is based on Distant Supervision & Semantic Search, can summarize almost any unstructured financial data with additional training. For a Tier 1 investment bank’s research team that needed to fast-track and made their processes more efficient, we created an integrated NLP-based solution that automated summarizing the Risk Factors section from the 10-K reports.

Invoice and Expense Automation: Eliminating the manual effort

Automated invoice processing is the answer for organizations that struggle with a never-ending backlog of invoices and expenses. An AI integrated engine captures and extracts invoice and expense data in minutes. Without setting new templates and rules, data can be extracted from different channels. There’s also the advantage of automated learning facilitated by the AI engine’s self-learning and validation interface.

Magic FinServ used its sophisticated OCR library built using Machine Learning to get rid of manual effort in uploading invoices to industry-standard invoice & expenses management applications. Another Machine Learning algorithm was able to correctly determine General Ledger code to tag the invoice against an appropriate charge code correctly, and finally, using RPA was able to insert the code on the invoice.

Streamlining corporate actions operations:  

Corporate actions are one of the classic use-cases for optimization using AI. Traditionally, most corporate actions have been done manually, even though they are low-value activities and can mostly be automated with suitable systems. However, whether it is managing an election process with multiple touchpoints or disseminating accurate and complete information to stakeholders and investment managers, the fallout of missing an event or misreporting can be considerable. One way to reduce the risk is to receive notifications from more than one source. But that would compound the back-office workload as they would have to record and reconcile multiple notifications. Hence the need for AI.

Magic FinServ’s AI solution streamlines several routine corporate action operations delivering superior quality. The AI system addresses inefficiencies by reading and scrubbing multiple documents to capture the corporate action from the point of announcement and create a golden copy of the corporate action announcement with ease and efficiency. This takes away the need for manual processing of corporate action announcements saving up to 70% of the effort. This effort can be routed to other high-risk and high-value tasks. 

Conclusion: 

Back-office automation drives enormous dividends. It improves customer satisfaction and efficiency, reduces error rates,  and ensures compliance. Among the five technology trends for banks (for 2020 and beyond), the move towards “zero back offices” – Forrester report, is a culmination of the increasing demand for process automation in the back office. “Thirty percent of tasks in a majority of occupations can be automated, and robotics is one way to do that. For large back offices with data-entry or other repetitive, low judgment, high-error-prone, or compliance-needy tasks, this is like a panacea.”McKinsey Global Institute. For a long time, we have also known that most customer dissatisfaction results from inadequacies of back-office. As organizations get ready for the future, there is a greater need for synchronization between the back, middle, and front office. There is no doubt that AI, ML,  and NLP will play an increasingly more prominent role in the transition to the next level.

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