Artificial Intelligence

Navigating the new normal   

The goalposts are changing and so must you in the new normal defined by Generative AI. Artificial Intelligence in financial services transforms businesses at a speed that is critical for business survival and success. Whether it is adopting a fresh approach or reframing and remodeling business with financial services automation at the core, or embracing innovation at scale, and driving growth along the new curve shaped by AI, it is Artificial Intelligence now or never…

“Intelligence is the ability to adapt to change.” – Stephen Hawking

How did David emerge victorious in the battle with Goliath? With a slingshot. From the five stones he had picked up for the battle, David just needed one stone to defeat the giant (Book of Samuel, Old Testament). While much has been written about his intelligence and valor, his aim streamlined and precise was just as important in ensuring Goliath’s defeat. From a purely technical standpoint, since we are in the technology and data business, had David been late by a split second, or had his aim not been streamlined or precise, the story of David and Goliath would have had a different ending.

Similarly, in financial and capital markets, there is a lot that can be achieved (or lost) when processes are not streamlined, particularly in critical areas like invoice processing. Just as David’s perfectly timed and streamlined shot delivered maximum impact, a streamlined invoice processing mechanism shields organizations from irritants such as delays, errors, and penalties, and future proofs them from disruptions and missed opportunities.

Understanding the Essence of Invoice Processing

In simplest terms, invoice processing is defined as the series of steps that takes place right from the time when the supplier invoice is received to the time when it is paid and recorded in the general ledger. The invoice can be received via mail, or as a PDF attached to an email, or as an e-invoice. Whatever the format, the invoice must be manually/automatically scanned into the accounting system, digitized, ETL performed, data validated, and later inserted in the workflow approval and payment system.

Why Manual Invoice Processing is No Longer Viable

While earlier, invoice processing was largely dependent on human effort, most organizations are now moving to some forms of invoice automation solutions. This shift is primarily driven by the complexities of global financial and capital markets, which involve multiple asset classes. Manually processing invoices is impractical due to the higher likelihood of errors introduced by humans and the significant amount of time it takes, such as a lengthy 19-day process. Consequently, implementing automated workflows with Straight-Through Processing (STP) becomes a strategic investment that empowers firms to improve their spending practices and enhance efficiency.

Getting there: Why Rules-Based AI/ML Engine Critical for End-to-End Automation

While hedge funds and asset managers invest in intelligent platforms like Coupa and SAP Concur, they are still unable to accrue the true benefits. Many still rely on manual labor at some stages of invoice processing. For example, while an automated platform makes the processes of extraction of relevant information easy from the plethora of document types, there are other more complicated information that must be extracted for non-PO invoices, such as allocation and tax withholding, which require a more advanced invoice processing solution than just plain data extraction or OCR. It requires the application of pertinent business rules for ensuring that invoices are processed correctly and do not result in compliance or regulatory failures. While a centralized invoice receipt, digitized information (including data from emails, faxes, and email attachments) and optimized processes – internal sources and outsourced integrations) are prerequisite for ensuring productivity and efficiency, but for true transformation, additional value with quality and compliance, absolute traceability, and control end-to- end is also necessary. And hence the need for a platform like Magic DeepSightTM for both structured and unstructured invoices.

Magic DeepSightTM : When your need is for a Streamlined and Advanced Solution

Since we are talking about the David and Goliath, all it takes to sort out the invoice processing problems is well-sorted, streamlined solution like DeepSightTM, that can be easily integrated with existing invoice processing platforms and organization-wide workflows, organizations can counter traditional invoice processing woes related to lack of efficiency, high costs, no real-time financial visibility, non-compliance, penalties, reputation damage, business disruptions, and revenue leakage with ease.

DeepSightTM brings together the best-of-breed technologies such as AI, Robotic Process Automation (RPA), machine learning, a rules-based engine, and analytics, to integrate seamlessly in existing systems and transform how your businesses handle invoice data. Highly advanced, DeepSightTM builds up the right momentum for modern enterprises (enriches data quality with a rules-based, deep learning/self-learning approach), and facilitates institutions to make well-informed decisions. Its user-friendly interface is a significant value-add as it helps finance teams be aligned and on the same page.

What makes DeepSight an Exceptional Tool for Automated Invoice Processing

  1. Complete End-to-End Solution
    DeepSight is the ideal solution if you seek an end-to-end approach. It ensures a smooth and efficient invoice processing experience, whether you’re dealing with PO or non-PO invoices, requiring minimal human intervention. While conventional automated Invoice Processing platforms, even the most intelligent ones, focus on data classification, extraction, and cleansing, they fail to utilize business rules for determining the appropriate invoice allocation for non-PO invoices, necessitating manual work. However, DeepSight stands apart with its intelligent data technology. Its AI/ML engine not only extracts data but also offers significant value by performing critical analytical functions. This capability proves invaluable for Hedge Funds, Investment Managers, and Brokerages handling a vast number of non-PO invoices alongside their PO invoices.
    DeepSight utilizes a pre-built, highly intelligent, advanced, and comprehensive rule-based AI/ML engine to effortlessly determine allocations and tax withholding. This process effectively meets essential compliance and stakeholder requirements while preventing revenue losses. The enhanced invoice information, also referred to as the golden data, is securely stored in the internal accounting system through Magic FinServ’s API for accurate record keeping. By offering a single window or centralized single source of truth, it promotes transparency and traceability.
    DeepSight’s continuous upgrades and updates enable it to adapt to new information using its pre- trained AI/ML and rule-based data extraction capabilities. This results in an end-to-end automated solution that learns with minimal supervision, making it an exemplary version of such a comprehensive solution.
  2. Rules-Based Data Extraction through Pre-Trained AI/ML Models
    DeepSight’s intelligent document content recognition performs accurate identification, classification and segregation of documents. Critical data elements from documents are recognized and extracted by pre- trained ML/AI models through a rules-based engine. Incorrect, Out-of-date, Redundant, Incomplete, Incorrectly Formatted extracted data is cleansed utilizing DeepSight’s reverse lookup algorithm and pre- trained models. So, more insights and less effort.
  3. Straight Through Processing (STP)
    One of the key requisites for future-proofing any process is STP. Data from invoice documents is extracted, cleansed, transformed and invoices are created in Coupa without requiring any manual intervention or active monitoring is required.
  4. Streamlined Exception Management
    The system will be implemented with an exhaustive exception workflow to alert users for any manual input (if required). Real-time exceptions resolution mechanism available in the system (Review UI) aims to reduce redundant effort of filling same information every time with onset of new legal entities/funds/deals Our rule-based user interface ensures clean and consistent data, reducing errors, and streamlining the process for Coupa customers.
  5. Enabling Traceability with Regimented and Well-defined Processes
    One of the major requirements when it comes to invoice processing is traceability. DeepSight system provides audits in exception handling, error handling, and changes in rules. These are performed in a well-defined and regimented manner. All activities are documented as version changes or user actions as appropriate and can be accessed via the audit table or in the form of reports for further audit.
  6. Create Standardized Nomenclature
    All of the business rules/key parameters will be picked from a Single Golden Copy (maintained in DeepSight). This nullifies the chances of inadvertent human errors that can result in data integrity issues.
    By embracing streamlined and automated invoice processing, it is possible for organizations to unlock multiple benefits. They can eliminate bottlenecks, improve accuracy, enhance visibility into financial operations, and achieve greater control over their accounts payable function. They are also in a better position to direct their time, effort and resources for more strategic and value-added tasks.

DeepSight futureproofs Invoice Processing for a Global Alternative Investment Manager

A global alternative investment manager that was facing problems due to its archaic largely manual- oriented invoice processing, was able to tackle high-level issues related to redundancy, productivity, costs, etc., with DeepSightTM. The customer’s business across different asset classes such as credit, private equity, and real estate, was difficult to manage due to the extremely high volumes of invoices. Manually mapping the complex business rules related to charges and tax withholdings was not an easy task to accomplish manually. With Magic FinServ’s DeepSightTM, the client was able to override the obstacles of time, operational costs, and errors while bolstering efficiency and productivity. Provided below is a step-by step guide of how DeepSight was able to meet the client’s objectives.

  • Parse through diverse invoice formats whether it is an image, PDF, excel, etc., with ease
  • Using DeepSight, the global alternative investment company, was able to extract information smartly and accurately related to different vendor invoices and carrying varied templates.
  • One of the biggest advantages of the tool is its ability to self-learn user’s action and feedback, thereby making it easier for the tool to auto populate from new learnings.
  • Our advanced and highly comprehensive solution based on AI/ML, fixated issues related to tax withholding and allocation with a rules-based approach.
  • For ensuring seamless end-to end processing, data was enriched via RPA and an RPA-enabled workflow was implemented for approvals and authorizations processing.
  • Using APIs for seamless integration with the core platform

The use case above exemplifies how DeepSightTM can be easily integrated with existing workflows or platforms to provide the results that firms had been looking for.

A truly transformative Invoice processing with DeepSightTM

While productivity and cost savings are the essential pillars of automated invoices, truly transformative invoice processing is one that focuses on increasing stakeholder satisfaction, improving agility and ensuring better management of working capital. For true transformation, additional value with quality and compliance, absolute traceability, and end-to-end control is also necessary. As a new shift is surfacing in the business ecosystem, it is time to redefine invoice processing and shift from the traditional approach to the DeepSightTM approach powered by AI technologies. With a highly refined and comprehensive solution that self learns with little help and has the gigantic capacity to scale up while ensuring end-to-end automated invoice processing of both PO and non-PO invoices, organizations not only can significantly reduce the percentage of errors and improve efficiency, but more essentially, they get a head start because of the deep insight they have gained thanks analytical capabilities of DeepSight (with rules-based approach) and an end-to-end solutioning of invoicing and prevention of revenue leakage.

For more information on how we can transform and enrich invoice processing with DeepSightTM, drop us a mail, at to know about our invoice automation solutions. Even if you already have invested in an IP platform, you will be surprised to find what you are missing and how we can ensure a true end to end automation, where you will not have to worry about manual work ever again.

Though for years, overwrought developers had secretly been wishing for a genie who could automatically make their troubles vanish by automatically generating source code, that idea seemed far- fetched. But not anymore.

What seemed like wishful thinking once is now within reach thanks to the Developer Companion, an Artificial Intelligence-enabled platform designed specifically for the harried developers and development teams walking the thin line between contradicting business interests. The Developer Companion can do a lot more than just write a line of code, as you are about to find out very soon. But before that, a sneak peek into the developments that are underway in software programming.

Coding Goes through a Shift in Financial Software Development

The approach to financial software development has been undergoing a massive shift thanks to Artificial Intelligence and Machine Learning. Though Integrated Development Environments (IDEs) have been around for some time now, providing autocomplete features that take away some of the pains of coding, AI has been ushering in a paradigm shift underway in the world of software development. AI- assisted coding is simply the use of artificial intelligence/machine learning models to generate code automatically. AI-assisted code generation marks a new epoch because it relies on prebuilt code modules to build applications without the developers writing much (or any) code themselves. Instead, AI-assisted coding tools generate custom code entirely from scratch based on comprehensive training data and smart algorithms. Or to say, the central premise of these tools is the LLM model, that includes training on massive amounts of data existing source code — which usually comes in the form of publicly available source code produced by open-source projects. All that the developer has to do is ascribe in natural language what they want from the code. And viola! The tool does the rest.

With More Time in their Hands, Developers Turn into Superheroes

Artificial intelligence powered coding provides a new lease of life to financial software developers. With the power of AI, we are unlikely to see them frantic, their heads buried in the computer screen figuring out the next line of code or fixing a bug. Assuredly, when writing a boilerplate code for uploading a file, is no longer their sole preoccupation thanks to artificial intelligence and machine learning, they would have time to focus on the real core activities such as:

  • Brainstorming with product teams and carrying out requirement analysis
  • Bettering systems design and designing engaging user experience,
  • Planning the development and other implementation details to ensure timeliness and accelerated time to market
  • Continuously update and better their skills set
  • And in the process metamorphizing from a mere developer to a superhero

Now, let’s move on to the tool that turns them into a superhero and how it accomplishes this.

Developer Companion: Elevating Platform Engineering for the Ultimate Developer Experience and Efficiency

The Developer Companion is an artificial intelligence powered coding companion or rather a teammate for developers. The Developer Companion empowers developers by taking away the mundane aspects of their work and providing them time to focus on core valuable and higher-level activities instead of repetitive administrative tasks. Whether you’re starting from scratch or enhancing existing applications, Developer Companion is here to empower you every step of the way, here’s how:

Increased productivity: With the Developer Companion, developers can save time, improve quality, and increase efficiency. By slashing down the administrative work drastically, the Developer Companion can enable developers to focus more on the design and functional aspects of the coding work, along with other valuable and higher-level activities.

Accelerate the time to market: Thanks to the Developer Companion, and AI-based code prompters, developers can ensure a faster time to market. Instead of carrying out mundane and time-consuming tasks, like writing unit tests or translating code from one language to another, developers can complete the tasks much quicker.

Empowering developers: Whether you’re starting from scratch or enhancing existing applications, Developer Companion is here to empower you every step of the way. It does much of the heavy lifting, leaving developers to focus on truly critical aspects of work.

Extremely intuitive: It will not only predict the next line of code, but to understand your intent and infer context from what you’ve already written (including comments) to generate valid code. Not to mention, it makes mundane and time-consuming tasks, like writing unit tests or translating code from one language to another much easier.

Advantages of Developer Companion that You Cannot Afford to Miss!

Provided below are the tangible benefits of Developer Companion, that you cannot afford to miss out on:

Generate Code Snippet with Ease: Code snippets are templates that make it easier to enter repeating code patterns. Developer Companion can automatically generate code based on high-level descriptions or specifications. For example, AI models can analyze natural language descriptions of a desired program and generate corresponding code snippets or even complete programs. This can help automate the initial stages of coding and reduce development time.

Review the Code with Confidence: Developers have to accept (or reject) automatically generated code as they work. For some developers, the need to review code constantly could be distracting. They may be able to work more efficiently if there was a tool to review all code.

Navigate Complex Codebases with Code Comprehension: Whether it is complex code or legacy code, navigating through both is tricky on account of the complexities involved, lack of documentation, interdependencies, scale (number of lines of code, file size, terms of lines of code, number of files, and overall project scope, and in the case of legacy code there are multiple iterations, updates, and maintenance issues involved as well). Codebase or source code also encompasses multiple modules, libraries, and frameworks. However, the Developer Companion can easily navigate through the complexity of the codebase.

Extract knowledge from the existing code and make enhancements faster and error-free: Complex codebases/source code often involve multiple levels of abstraction. They may consist of high-level architectural components, intermediate modules, and low-level implementation details. With Developer Companion, programmers can extract the relevant details quickly and efficiently.

Bridge the Gap between Programming Languages through Code Conversion: Whether you are migrating a codebase from an archaic programming language such as COBOL to a modern one like Java or C++ or translating functions between C++, Java, and Python 3, the process is difficult and resource- intensive, and moreover requires expertise in the source and target languages. With Developer Companion, developers can bridge the divide through code conversion.

Test Code with Automated Test Case Generation: Automatic test case generation is the process of identifying and creating test cases for an application without the need for any manual intervention. It involves utilizing various techniques and tools to automatically generate test cases for software applications or systems. The primary objective is to automate the creation of test cases, leading to enhanced test coverage, improved efficiency, and reduced effort required for manual testing.

Discover Hidden Code Vulnerabilities: Instead of racking your brains regarding what could be wrong, or where the gap exists, developers can unearth the hidden vulnerabilities with ease using Developer Companion.

Unravel the Data Maze with Unique Database Query Console: Do not get lost in the maze of data, as a unique query console gets you inputs way faster.

Unleash the Power of Developer Companion Today!

In essence, an AI assistant for developers was long due for ridding the irksome, repetitive, and mundane pieces of work that they are saddled with daily. An AI-enabled assistant not only grants them a new ease of life but raises the bar as well when it comes to programming. It improves the time to market, efficiency, while minimizing problems such as legacy code and tackling risks such as shortage of skilled labor, attrition, testing challenges, etc., with AI. Developer Companion is all set to revolutionize AI in financial software development by extending the possibilities and taking it to a new level.

Now, if that was not enough to ask for a demo right away, we will be more than glad to satisfy your curiosity and any further questions that you might have. You can write to us for more information on how we could give coding a new lease or reach out to us at for fintech solutions.

If the world is an oyster, airplanes are the TARDIS, and airports are the veritable blue box (from Doctor Who) – mostly noisy and often claustrophobic. The airport experience is rarely pleasant. Anxiety and stress are common due to flight delays, check-in hassles, seating arrangements (or rather, the lack of them), Wi-Fi problems, and more. This can be distracting for many passengers who would like some quiet time.

An airport lounge is an ideal place to relax and recharge. With free food and drinks, Wi-Fi enabled workstations, and clean facilities, travelers can take advantage of these amenities to unwind or catch up on work. Additionally, the lounge provides a networking opportunity with colleagues and friends. For those who desire a highly personalized or exclusive experience, the Polaris lounge (United Airlines) or Lufthansa’s super exclusive First-Class Terminal offer even more luxurious amenities such as a full bar, cigar lounge, plush sofas, buffet, and salon.

Now, a big pause! Many of you may be wondering if this is a build-up for Premier Lounges or the credit card companies that sell them. The answer is – no, it is not! This is about the parallel between the plight of passengers whose flight gets delayed or cancelled and the fintech’s journey in recent times. While travelers can recharge themselves at airport lounges, financial lounges like Magic FinServ can help fintechs recharge by providing super exclusive, hassle-free, and streamlined services. Magic FinServ’s advisory services and technology solutions can boost the pace of modernization/transformation by a significant 10X times.

The Many Stressors of Fintechs

Similar to how travelers face many stressors such as connecting flights, delayed and cancelled flights, fintech companies can also experience frayed nerves, especially when struggling with legacy workflows, redundant tools and technologies, unending migration problems, data swamps, and potential industry- wide disruptions caused by unexpected forces such as generative AI. Additionally, they must account for past mistakes such as overspending, which has resulted in big banks failing one after the other. The latest to bite the dust is First Republic.

If you are a fintech or financial services provider struggling to keep up with the massive disruptions occurring in the world today and are looking for a way to de-stress and energize, a Financial Lounge is what you need. A financial lounge is perfect for de-stressing your cluttered, siloed workflows and legacy engines from decades ago. Let us explore how.

Welcome to Magic FinServ! The Financial Lounge for FinTechs and Financial Services

  1. Beat the Stress
    • Managing financial data is one of the biggest challenges faced by fintechs and financial services. It is complicated as it involves a lot of sensitive information. Another significant challenge is compliance. Fintechs must stay up to date with the latest standards and work with regulators to ensure that all obligations are met in a timely manner. However, organizations no longer need to worry about these challenges as Magic FinServ can get them up to date.
    • We can help you prioritize data security and privacy, maintain accurate and up-to-date records, and ensure that data is easily accessible and actionable using the cloud and our AI and ML- enabled solution.
    • We can help you streamline your workflows quickly and assist you in embracing disruption.
    • DeepSightTM analyzes vast amounts of data in real-time for use cases such as risk management, fraud detection, AML, KYC, portfolio management, regulatory compliance (Shareholding and voting rights, ESG, T+1, investment and fund monitoring and reporting).
    • You can also count on our team of AI and Machine Learning specialists and FinTech engineers to identify gaps and provide sound advice on how you can leverage technology such as AI to speed up your processes at minimum costs.
  2. Relax and Recharge while our bespoke AI platform crunches numbers faster than Speedy Gonzales
    • Rest and relaxation are important for both physical and mental well-being. Like airport lounges offer a place to unwind and recharge during a stressful time, Magic FinServ’s financial services acts as financial lounge for fintechs providing them with the needed “rest” and allowing them to focus on their core business. Our tools and services can help fintechs manage their finances with ease and confidence. Here is how
    • For humans, sleep is more than a biological requirement. It helps them recharge their batteries for another day of work. However, because of the existence of multiple time zones when markets are alive and active in one part of the world, our analysts weary and tired after a full day of work could be fast asleep, missing several critical incidents in another part of the world. So, when they come back to work the next day, they have their hands full. Most of the work that they would be required to do to keep the processes up to date would be of a manual sort, such as data entry, uploading and downloading files. Making the analyst feel tired like Jack – because all work and no play makes Jack a dull boy.
    • Now you can slay stress and be up-to-date faster than it takes to say I do! Unlike humans, our AI- powered platform does not need any sleep at all. So, while you are fast asleep, our powerful AI-powered solutions are deep at work automating facets of operations that were time-taking and tedious, ensuring that when you come fresh to work the next day, you only have to fine tune or take a summary, as the rest has been taken care of by AI.
  3. Experience a Seamless Transition
    Passengers go through an agonizing period when they wait for a connecting flight, and fintech companies experience a similar sense of anxiety while awaiting the results of cloud migration and modernization efforts. However, with financial lounges such as Magic FinServ, the desired transformation can now be achieved more quickly. Our three-step approach for cloud migration includes:
    • Conducting a cost-benefit analysis with the help of our dedicated cloud team. This analysis takes into account the costs of migration and ongoing maintenance, as well as the savings that organizations can expect to see.
    • Optimizing the cloud environment with the use of automation and orchestration tools.
    • Utilizing cloud-native services such as containerization and managed databases to reduce costs and improve efficiency.
  4. Utilize Time Profitably
    • In today’s fast-paced world, time is a valuable commodity. Similar to how airport lounges with workspaces allow travelers to catch up on work while waiting for their flights, our financial solutions enable fintech companies to leverage the power of AI and ML to stay prepared for any unforeseen consequences. By automating time-consuming and repetitive tasks, our solutions allow fintechs to focus on high-value work and take advantage of opportunities as they arise. This way, they can make the most of their time and operate with maximum efficiency, ultimately leading to increased profitability.
  5. Remain Exclusive
    • People go to these lounges not only when their flights are delayed but also for novelty- it offers a unique experience. So it is for the financial services, we are their niche partner offering them the unique space to innovate and experiment. Because it has become apparent now that despite indications that recession may have set in finally, organizations, especially the bigger ones, are earnest about prolonging investment in moonshot projects. Some of the most common moonshot projects relate to robo- advisory, de-fi, embedded banking.
    • If you are keen on continuing moonshot projects, ensure STF and automation of back and middle-end processes first, because unless these two ends are unified and compatible, it would be unreliable to expect long-term benefits from other areas.

Magic FinServ: an Oasis for FinTech’s

In today’s fast-paced world, FinTech companies face numerous challenges that can impact their ability to succeed. From managing legacy workflows to keeping up with constantly evolving technologies and regulations, it can be challenging to stay ahead of the curve. That is where Magic FinServ comes in – as an oasis for FinTechs.

As experts in data and AI for FinTechs, Magic FinServ provides customized solutions that help companies manage the stress that comes with operating in this turbulent environment. We understand the unique challenges that FinTechs face and work with our clients to develop solutions that are tailored to their specific needs.

Our services act as a refuge for FinTechs, providing them with a much-needed break from the chaos that often surrounds their operations. Just as an oasis provides water and shade to travelers, Magic FinServ provides FinTechs with the support and resources they need to thrive in an ever-changing landscape.

Our clients can count on us to help them manage complex data sets, navigate legacy workflows, and keep up with the latest technologies and regulations. With Magic FinServ by their side, FinTechs can take on new challenges with confidence, knowing that they have a partner who understands their unique needs and can help them achieve their goals.

In conclusion, Magic FinServ serves as an airport lounge or oasis for FinTechs, providing the support and resources they need to navigate the challenges of the modern business world. With our expertise in data and AI, we help FinTechs manage the stress that comes with operating in a constantly evolving

landscape and enable them to thrive in the face of adversity. Contact us today at to know more.

For most organizations, the lack of timely insights can make contract data management a nightmare. Without the necessary tools and technologies, many organizations can be caught off-guard when it comes to negotiations, execution, renewals, and termination of contracts. It is estimated that lack of timely insight into obligations and terms can result in revenue leakage of more than 9% annually. Source: (World Commerce and Contracting)

Whether it is Auto-Renewal, Termination, and in recent times Force Majeure, these clauses within the contract results in unnecessary diversion from business objectives, as the excessive reliance on the manual approach can only lead to unsavory incidents and disagreements between parties concerned.

No one would want that. The experts point out that an average Fortune 1000 company could handle several tens of thousands of contracts. Now if we were to consider the various stages of the contract management lifecycle – negotiations, execution, renewal and the multiple stakeholders involved – sales, finance, and legal teams, all this would simply mean that the amount of effort deployed (and expenses paid) to ensure streamlined operations would be huge. Source: (World Commerce and Contracting)

Hence in recent times, businesses have switched from the predominantly manual approach of contract data management to a more agile and technology-oriented perspective using automation, digitization, and robust data management (including meta data management) practices such as organizing a unique repository for each contract and curated database for locating a specific contract with ease. This shift provided organizations with a definite strategic advantage even during the pandemic, improved vendor relationships, enhanced visibility, and business gains as negotiations were conducted timely, while keeping at bay all possible risks.

So, if you are considering an overhaul to have better control over your contract data management, here are the 6 key players that could be a game changer for your organization.

5 key players of Contract Lifecycle Management

According to the latest forecast by Gartner, by 2023, artificial intelligence (AI) will enable 30% faster contract negotiation and document completion processes in organizations that deploy leading contract life cycle management (CLM) solutions. As a fintech or financial organization, you can still salvage the situation. For reports point out that the latest tools are enhanced with a version control mechanism and searchable repositories for arriving at the single source of truth with minimum effort and human intervention resulting in significant productivity improvement and reduction of sales cycles.

Digitizing data: Begin with the digitization of data. Keeping track of documents, the multiple edits that are incorporated from time to time, can be a tedious and wasteful exercise. Furthermore, with data existing in silos a nightmare situation can arise if stakeholders have multiple versions of the contract. In order to avoid contract nightmares, the first critical step is to digitize data. Whether public documents, emails, or paper agreements, digitize the contracts so that there is a single source of truth.

The 5 levers of transformation

When it comes to contract data management, the following technologies have a key role to play in its transformation. The following 5 levers of transformation supplant the slack/inefficiency associated with traditional manual contract data management and provide it with agility, visibility, accuracy, faster response times, and productivity.

Optical Character Recognition: Once organizations have proceeded with the digitization of the diverse document types, they must work out how they must extract relevant information from the volumes of data available because machines inherently lack the power to read them. Despite the variety of document types, and field types – names, dates, boilerplate text, different fonts, and even handwriting, OCR can read through it all and extract the relevant information faster in a standardized, streamlined fashion.

Natural Language Processing: The language used in contracts is complicated and legal in nature and can be best understood by professionals or the legal experts. A rule-based approach enables progressive understanding of the terms and the context, and organizations can even retain experts for relating and better understanding of context when it comes to ambiguous/difficult-to-understand terms and conditions.

Robotic Process Automation: Contract automation has been defined as the software to enable both legal and non-legal teams to self-serve on routine legal documents, at scale, without needing to involve lawyers every time. It can be described as the process of generating, managing, and storing contracts digitally to create a more efficient contract workflow. In order to bring about greater productivity and efficiency, we recommend automation of all tedious and repetitive data extraction tasks.

Machine Learning and Artificial Intelligence: No organization will like to miss the deadlines as there could be significant costs associated with it. Staying-up-to speed on contract renewals can seem like an uphill task, but with artificial intelligence and machine learning, you can ensure that your teams never miss an important obligation. Artificial Intelligence and Machine Learning comes streamlines the process for most organizations dealing with multiple contracts (big organizations juggle some 20,000 and 40,000 active contracts). Keeping up-to-date and abreast of the latest on a monthly or yearly basis would simply be impossible without the technology underlined above.

Taking control of contract management with Magic FinServ’s DeepSightTM

We can help streamline contract data management. Ensure that there are no contract nightmares that ever cause organizations to pull in resources needlessly for firefighting. Here’s how we assure a smarter contract management powered with OCR, RPA, AI and ML.

Contract Data Extraction Workflow

Magic’s DeepSightTM – the process optimization platform along with our Advisory practice provides a configured solution that acts as a virtual assistant to the analyst thereby reducing the complexity of the task. It automates standard processes to reduce errors and omissions and enables the less experienced and less skilled analysts to be able to perform their tasks.

How DeepSightTM works?

Processing diverse documents: Service Provider Agreements vary by type of service; each service provider has its own unique terms and conditions. These documents have to be categorized according to type of service and for each service provider, relevant content identified and extracted

Unique folder for easier access and retrieval: Our solution not only identifies and segregates different documents but also files all documents for a particular service provider in the same folder to enable ease of access and retrieval.

Rules based: Magic DeepSightTM solution incorporates business specific rules to identify relevant information and extract the same and update it in deal maintenance system after proper cleansing, enriching and transformation. Content extracted can be key value pairs, tables, or free flow content such as covenant clauses. If it is deal related, the key value pair is extracted. In the case of governance, contextual data is extracted and in deal maintenance details, combination of contextual and key value pairs are extracted which are all driven by the rules. Solution can go through the entire set of documents related to a particular deal including annexures, renewals, etc. to identify the latest terms and incorporate in the deal maintenance system.

But remember all this starts and ends with data. Data is the motherboard of contract lifecycle management. So as an organization we are in a unique position to aid data-driven transformations as we have the expertise, and the skill sets to deal with all your concerns. If you too would like to know, how we can be of help, contact us

The Future’s Getting Breathtakingly Simple

Before the Covid-19 pandemic, managing contracts was not an easy business. The legalese that contracts are clouded in, constitutes just a small fraction of the challenge that organizations face during contract management. The bigger challenge is the existence of contracts in diverse forms such as public websites, paper, emails, and ensuring that obligations are met in a timely manner so that there are no monetary losses or wranglings/disputes with the parties concerned later.

A change has been necessitated because as the world gets smaller, as borders fade out when it comes to doing business, and the number of stakeholders multiplies, the stakes have also become disproportionately high, and as a fintech or financial institution you simply cannot afford to leave anything to chance. Hence, the need for augmenting human intelligence and capacity with intelligent tools powered by Artificial Intelligence (AI) and Machine Learning (ML) and relying enormously on digitization capacities and the cloud for managing, storing, and interpreting contractual information effortlessly.

Demystifying the Reasons Behind the Shift!

Though contract data management has always been a long-winded and paper-heavy process, there has been a subtle shift post covid as organizations are becoming more open to using modern technology such as AI and ML post covid 19 for addressing many of their pain points post covid. This was primarily because, during the pandemic and post-it, organizations realized that they could not leave contract data management to the legal experts and the analysts (solely) anymore – for not only was it becoming extremely time-consuming and expensive, but also manual contract data management was proving incapable of providing the desired level of scalability and deep insight that organizations required. So, here’s decoding why as a fintech or Financial Institution, Smarter Contract Lifecycle Data Management is critical for keeping pace in a changing business landscape.

A) Unstructured data in files, cabinets, and silos across the enterprise were difficult to access and update

When the pandemic struck, which was quite sudden, some organizations had either digitized their contracts and processes or hadn’t. For those who hadn’t digitized the swathes of paper agreements or the contract management process, managing contracts turned into a nightmare as accessing files from the repositories or cabinets at a minute’s notice was improbable. This probably was the biggest learning for organizations as they realized that digitization or cloud solutions/cloud-based contract lifecycle management (CLM) enabled a level of business continuity that was not possible when you were relying simply on paper agreements or contracts. If the challenge posed by unstructured data was not enough, the existence of structured data such as the ones in public repositories with multiple stakeholders and existing in silos enterprise-wide constituted another major challenge during the pandemic for the same reason of lack of accessibility.

B) Staying in control was difficult as there was limited visibility into data

In 2020, the number of Force Majeure clauses invoked was a record high in many parts of the world. The force majeure clause implies an Act of God or some unforeseen circumstance, man-made or natural disaster that makes it difficult to fulfill certain obligations of the contract. By invoking this clause in the contract, the parties involved can protect themselves from liabilities that arise due to their failure to provide a certain service or product. Interestingly, the force majeure clause was rarely invoked prior to the pandemic, and if we were to go by the findings of the World Commerce & Contracting Report on the latest Most Negotiated Terms, 2022, it ranks a low 26 in the list of the most negotiated terms, but post the pandemic, it was one of the oft-invoked clauses.

In 2020, the number of Force Majeure clauses invoked was a record high in many parts of the world. The force majeure clause implies an Act of God or some unforeseen circumstance, man-made or natural disaster that makes it difficult to fulfill certain obligations of the contract. By invoking this clause in the contract, the parties involved can protect themselves from liabilities that arise due to their failure to provide a certain service or product. Interestingly, the force majeure clause was rarely invoked prior to the pandemic, and if we were to go by the findings of the World Commerce & Contracting Report on the latest Most Negotiated Terms, 2022, it ranks a low 26 in the list of the most negotiated terms, but post the pandemic, it was one of the oft-invoked clauses.

Today, it would not be hard to imagine how tricky, tiresome, and time-consuming would it be for a human, whether a legal expert or an analyst to skim manually past the swathes of paper contracts or public repositories for finding the terms and conditions associated with clauses such as termination, scope and goals, payment and payment options, data privacy, services levels, acceptance, regulatory compliance among others, and keeping up-to-date and in case of conditions such as the force majeure compliance among others, and keeping up-to-date and in case of conditions such as the force majeure how an untoward incident would have an impact on the organization. Considering the fast-paced business environment that the banks and FinTechs are operating in, finding, updating, and validating the relevant information manually is totally undesirable. However, with intelligent contract data management solutions, and automated solutions, it takes no time to extract, update, validate and monitor information that is required for keeping up to date.

C) Making intelligent decisions was difficult without appropriate tools, approaches, and technology

During the contract data management lifecycle, organizations get the best value from the services or products that must make intelligent decisions. They must access data quicker and faster than ever before. However, with data existing in silos, and in multiple formats, a data swamp builds up over the course of time. In order to make intelligent decisions faster, all data about a particular contract must be in one place instead of clustered everywhere. Using a combination of intelligent tools, powered with artificial intelligence and rules-based engines with capacities for learning and robust data management practices and metadata – a unique identifier without which one would have to wade to tons of data, organizations can take accurate and on-time decisions.

D) The realization that a centralized source of truth is more readily accessible than a file or a cabinet or data in silos

A unique centralized repository where you can store all your data in a digital format is better equipped to handle the challenges of the next-gen contract management lifecycle than the outdated physical file or cabinet. It is bestowed with dynamism thanks to automation, a unique digital repository centralized repository where it is readily available along with metadata, making it much more convenient for organizations than a filing cabinet or a siloed existence.

The Future in Simplicity

Contract data management does not end with the negotiations and signing of the contract between the parties. There is a lot of work that goes in the management of a contract such as keeping up to date, remaining compliant, and realizing maximum benefits, and for that contract management must be made simpler so that there is on-time/on-demand information.

As a financial institution, if you are unable to access information about the contract that you have signed in time and make decisions accordingly, you expose yourself to needless disputes and wranglings later, and are burdened with higher costs, poor performance, unnecessary risks, etc. Contract intelligence solutions begin by centralizing and streamlining contracts providing visibility into all contract data, thus empowering the organization to efficiently monitor all contractual and regulatory compliance obligations. That is the first step to simplifying it.

In the next blog, we will tackle the tools and technologies that make contract management more intelligent and how Magic DeepSightTM enables it. Magic’s DeepSightTM the process optimization platform along with our Advisory practice provides a configured solution that acts as a virtual assistant to the analyst thereby reducing the complexity of the task. It automates standard processes to reduce errors and omissions and enables the less experienced and less skilled analysts to be able to perform their tasks. Stay Tuned!

There is a competition between Cupid and Norah Ephron! Cupid today is as cold as steel. It is more calculative than Shylock. But it is still sassy as Samantha, the artificially intelligent virtual assistant, with a remarkable tenacity for learning.

Considering the way AI has made entry in our modern lives it is hardly surprising that the dispenser of love spells in the millennial age is AI as well. What began as a game with Tinder’s profile swipes, has now progressed to serious stuff – chat-catalyzing text prompts for enabling small talk and on-demand emojis for better understanding of the non-verbal cues. Making all this possible are smart algorithms, data analytics, easily navigable interphases, artificial intelligence, machine learning, and natural language processing.

Flirty, funny, or formal – How AI plays cupid’s game?

Though lacking the emotional button, the human element as we prefer to call it, AI can still help anyone impress their date just by processing tons and tons of the data that they unsuspectingly left behind every time they swiped left or right, checked a box, or used a conversational tool to for small talk. Let’s have a look at how apps that leverage technology help you date.

  1. Every time the user swipes left or right, the app decodes what the user likes. Further, the innocent survey questions that the user answers hide codes, encryptions, and rules-engines that also help the app accumulate data and build a concrete knowledge base.
  2. Once the match has been made, it is time to initiate a conversation. Some people are intimidated by small talk. Hence, navigating, the next level of dating which necessarily includes some small talk can be tricky or tiresome – not simply because of the lack of words and time, but also because consistently decoding the non-verbal and verbal cues and signals is a challenge for any human.
  3. Now that there thanks to Open AI, multiple conversation starters can be integrated into the dating application. With the help of artificially stimulated conversation, a person can be anything that they want to be – smart, witty, resourceful, formal, flirty, or fun.
  4. As some tools can also help read non-verbal cues, you can progress with the relationship faster. There are AI love advisors for personalized assistance with dating – from buying gifts to resolving minor tiffs, there’s a lot you can approach it for. In short, with AI you can cut short the needless chit-chat and get to work on the relationship faster.

However, it is too early to bet on AI. Some of the experts have pointed out that while AI could be dumb and was indeed completely over the top other times, there were still times when it truly worked. So, we might still have to wait and watch how effective AI is in taking over from Cupid and Nora Ephron, but when it comes to Capital Markets AI, there is no question that AI is a game changer.

AI in Capital Markets

Doing the dirty work: In the world of capital markets, the messiest task is basically extracting and transforming data so that it is always accurate and up to date. For all the brokers, custodians, corporates, fund admins, investment managers and service providers, who hedge and hustle in the market on a daily basis, data is undoubtedly the key player. Whether it is equities, stocks, futures, derivatives, shareholding rights, mergers and acquisitions, sell-offs, buy-backs, corporate actions, etc., the amount of data that capital markets deal with daily is astronomical. Thanks to its ability to leverage huge quantities of data accurately in concert with RPA, AI is making a huge impact in the world of capital markets.

For some time now AI, automation, and NLP have been making it easier for the back and middle offices to streamline their work flows and make their operations smoother with AI. Many of the repetitive tasks in back and middle offices have been made less cumbersome due to AI. Whether it is updating client data in record systems –digitizing it, error reconciliations, or contract data management, the implications of the use of AI are huge as these provide financial institutions with a cheaper alternative.

Using AI for Customer Onboarding – Getting to the Business without wasting time : Now, let’s take another example of the use but from an absolutely dissimilar world – that of the financial services (because that is what we cater to). Let us take the example of onboarding a new customer. How the KYC or onboarding is conducted goes a long way in defining the customer experience and that in turn will have a big impact on whether the customer decides to stay and provide more business opportunities or leave right away. While it would be presumptuous to compare onboarding with dating, there certainly is some common ground.

New Customers are like First Dates

Customers are like first dates and would like to be treated with respect and instead of wasting time, they would like to know whether it would be reasonable to progress with the business initiative. Erratic customer onboarding is not at all acceptable to millennial customers, and many even feel that the product lacks the credibility required.

Hence, a tool like DeepSightTM , specifically built for broker-dealers, custodians, corporates, fund admins, investment managers and service providers providing customized and compliance-ready solutions is just what they need. It helps navigate the noise, so just like the dating app that matches the profiles, you like, capture only what you want – data from fields that you need.

The tenacity for learning is critical for any AI tool: As the number and type of fresh KYC documents processed into the system increase, the AI engine continuously learns and will be able to interpret, understand and capture data with greater precision.

Seamlessly upload documents and export data: Consolidate documents received from clients from various channels including email, Dropbox, Slack etc. Magic DeepSightTM allows you to immediately import this data and then directly export the clean, organized, structured data to your existing workflow, without disturbing any existing systems.

In conclusion: A thought to how AI has made lives easier and how it is shaking up the capital markets and the dating game. With AI, you are promised agility and flexibility and quick response times. So, if you too would like to know how you can cut through the unnecessary dilly-dallying and onboard customers faster by providing them a better experience, or how to use alterative and unstructured data for staying ahead of competition, you can write to us

The world of finance is constantly evolving, and the emergence of artificial intelligence (AI) has brought about significant changes in the fintech industry. AI technologies are being utilized by financial institutions to streamline operations, reduce costs, and enhance customer experiences. In this infographic blog post, we will explore the impact of AI on the world of fintech, its various applications, and the potential benefits that it can offer to both financial institutions and consumers.

“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.


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

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 Or visit our website:

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

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