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 DeepSight^TM 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 DeepSight^TM , 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 DeepSight^TM .

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 DeepSight^™ leverages 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 DeepSight^TM 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 DeepSight^TM 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 DeepSight^TM.

Magic DeepSight^TM 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 DeepSight^TM 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 DeepSight^TM 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 DeepSight^TM 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 DeepSight^TM 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 DeepSight^TM

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.