How Data science and AI can protect banks from sanctions and reputational damage
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How Data science and AI can protect banks from sanctions and reputational damage

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Illegal transactions, financial terrorism, money laundering, tax evasion … there are some bad people in the world, and some of them try to use the financial system to carry out their evil deeds. Banks can unwittingly be dragged into such affairs. The damage to their bottom lines, to their reputations and to their statures can be close to irreparable. So how does it happen? How is it that banks can be exposed to risk in this way? Well, part of it is down to how a bank conducts a risk analysis of its customers and partners as part of its Know Your Customers (KYC) and Anti-money Laundering (AML) procedures. Money is flowing farther, faster and more frequently than ever before. Threats are becoming more sophisticated and increasingly difficult to detect. It might seem as if banks are on the back foot. Not if they have the right technology. ITMAGINATION has helped one of Poland’s leading banks meet its KYC and AML requirements. Marcin Dąbrowski, CIO at ITMAGINATION, explains how banks can harness the power of Artificial Intelligence (AI) and Data Science to verify the trustworthiness of potential and existing customers, and the feasibility of doing business with them. Equipped with these capabilities, banks can confidently operate knowing that the risk of them being involved in financial misdemeanors is minimized.

Every bank is duty bound to evaluate the risk of serving a new client or working with a partner (both at the start of the relationship and periodically thereafter). Banks are obliged to monitor their activities for any suspicious behavior, such as illegal transactions, possible financing of terrorism or other criminal activities, money laundering or tax evasion. If a bank or other financial institution fails to perform appropriate risk evaluation, the costs can be significant – both from a financial perspective (e.g. fine) and in terms of reputational damage. According to research conducted by Fenergo, in the ten years since the onset of the financial crisis in 2018, poor management of AML (Anti-money Laundering) and KYC (Know Your Customer) processes has cost banks around the world 26 billion dollars (USD) in fines.

Financial penalties obviously hurt, though it’s also important to consider the reputational damage that banks are potentially exposed to. Imagine, for example, if the institution you bank with was accused or supporting (even unwittingly) international terrorism by turning a blind eye to the nature and integrity of some of its customers and their transactions. Would you still feel confident and comfortable doing business with such an institution?

How can banks get to know and onboard their customers faster and reliably in the digital age?

In the traditional operating model, when a bank signs up a new customer at one of its branches, a clerk or advisor will request a variety of information and a document or two as evidence of a person’s name, age, address, etc. All of this data and information is typically handled using paper form, photocopiers, additional print-outs. This is done to confirm details and other ‘old school’ processes. That data and information is then manually entered by a bank employee into the system. After this, an evaluation or risk is undertaken (either by a computerized system or by an analyst at the bank). Banks that are further along their digital transformation journey offer customers the option of entering all of this information on a secure online form. They allow to take photos of documents and/or submit scans of documents.

Imagine if, as a bank, you could conduct a risk analysis of any new customer or partner in real time. By using QR and/or barcode readers and sophisticated Optical Character Recognition (OCR) technology, banks – even those that are still using the paper-and-pen method of gathering data – could digitalize the data they gather from customers. After that, they combine it with available data from external online sources of information (such as Companies House or FCA in the UK or the U.S. Securities and Exchange Commission in the US). and/or credit-rating agencies (like Experian, Equifax or TransUnion), criminal registrars.  They can combine it with internal data repositories, such as CRM systems, transaction recipient lists and customer/partner blacklists. The result of this activity would be a profile of a customer or potential partner, against which algorithms could be run to determine the trustworthiness and integrity of the that person or party. All of this could be done in real time and could be set to run periodically to conduct frequent checks. This would empower the bank to take on new customers or partners quickly and without making excessive time or data demands on them, while ensuring it does so – and then continues to monitor their activities – in a way that is compliant with all locally applicable regulatory requirements and minimizes the bank’s exposure to risk.

All of this is possible with the integration of the right tools, the digitalization of the right processes, and the expert application of AI and Data Science.

AI, Data Science can help identify, predict and prevent fraudulent activities

In order to ensure compliance with AML requirements, banks must continuously monitor the transactions being run through its system. This is done for a variety of reasons, such as prevention of fraud, identification of suspicious transactions, and even to protect customers from themselves (e.g. not falling too far into debt). In recent years, many of us have experienced a bank card being abruptly deactivated while we’re traveling or a phone call from a bank asking us to confirm our location and the nature of a recently made transaction. These are indicators that a bank has noticed some form of irregular behavior. Furthermore, bank wants to be sure that the account holder is aware of and responsible for this activity (and that it is not, for example, a case of a card being stolen or cloned and in use elsewhere).

Banks monitor activity on an ongoing basis. When a red flag is raised, they need to take action. As banks seek to speed up their operations to keep pace with how businesses and customers transact, and as they seek to make their operations more cost effective in the face of increased competition (e.g. the fintech ‘challenger banks’ like Revolut, Monzo and Starling), it’s vital that as much of this monitoring of transactions and flagging of irregular behavior is as automated as possible. With the right implementation of AI and application of Data Science, it’s possible to analyze – in detail – millions of transactions simultaneously and reduce the need for human involvement in this analysis to almost zero. Applied effectively, such a solution means that human intervention is required only when transactions are identified as non-standard and cannot be explained. Thanks to AI and Machine Learning, such systems can learn what constitutes a standard and non-standard transaction as it goes along, which serves to increase the accuracy and efficiency of the system on an ongoing basis. Looking into the not-too-distant future, such systems will not only be able to identify an AML incident, but they will also learn how to predict them or indicate a likelihood of one occurring. This means that banks can run more efficiently. They can reduce their risk of exposure to financial penalties related to non-compliance with AML and KYC requirements, and secure the trust and confidence of their customers.

How can ITMAGINATION help?

ITMAGINATION has helped the Polish division of one of the world’s largest banks by building a system that will help it meet its AML and KYC requirements. The system makes use of AI and Data Science and involves the application of algorithms to automatically identify irregular activities that warrant further investigation by the bank. The system operates on a 24/7 basis to reflect the needs and behavior of the bank’s customers. Today, the bank benefits from a dramatically reduced exposure to risk of non-compliance with its AML and KYC requirements. The system is fully integrated with the bank’s internal CRM system.  It empowers the bank and its employees to build up a 360-degree view of its customers and propose appropriate products and services at the right time, based on their activities, preferences and profiles.

If you’re a bank and your bottom line and reputation matter, talk to ITMAGINATION about how you can fulfil your regulatory requirements and build trust with your customers … all while operating more efficiently than ever before.

Learn it. Know it. Done.

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