11 Aug Artificial Intelligence to the rescue?
While some of the financial institutions are struggling to meet the stringent requirements of 4th Anti-Money Laundering (AML) Directive, the European Commission proposed a fifth revision of the Anti-Money Laundering Directive as part of its Action Plan for strengthening the fight against terrorist financing.
In scenarios like this when meeting the current regulations itself is a nail-biting activity, imposing new and stricter regulations is taking a toll on the financial institutions. Today financial institutions (FIs) are required by the regulators to comply with several international AML laws, and they need to meet several other internal policies and standards. All of this is becoming more complex as the regulatory environment is constantly changing, and this makes a financial services organisation’s ability to comply more challenging than ever.
FIs are forced to spend a fortune to meet the compliance requirements, as per a Thomas Reuters survey, the average annual spend on global CDD/KYC (including labour and third-party costs) is reported as US$48 million and the numbers are on the rise year after year. As per Lexis Nexis on average, a mere 19% of AML budgets is spent on technology and that personnel costs are by far the largest portion 81% of AML compliance spending in the APAC region. Apart from bearing these operational expenses, there is a lot of capital drain due to the heavy fines levied by the regulators for non-compliance.
It’s time for FIs worldwide to invest more in technology and reap the benefits, few have already implemented AML operations that rely on sophisticated technology to automate behaviour analysis and entity screening and to support analysis workflow and decisions.
Using AI to solve AML operational challenges as below
1. Unified Pool of Data: The biggest advantage of AI lies in its ability to harness and clean and validate data from multiple sources. Data from internal and external sources can be brought together under one roof, annotated and enhanced to make it more searchable, context-sensitive and relevant for specific users. This pool of data can be used identity relationships between pieces of information, creating a complete view of interactions between clients and products which could build a complete, consistent, 360° view of a customer.
2. Risk management: AI-enabled solution’s self-calibrating models can learn money laundering techniques and proactively ascertain specific (geographic, industrial etc.) risks. So for example, an emerging geographic risk – related to a previously low-risk country that has of late become high-risk due to questionable activities of its prominent citizens would be proactively identified.
3. Probabilistic matching: When data from different sources are integrated into one pool, issues around duplicate records and inconsistent formats are inevitably encountered. For instance, Ryan Taylor, R. Taylor, and Jonathan Ryan. Taylor may be the same person, but conventional technologies aren’t able to flag these records as possible matches. However, AI solution can help implement probabilistic matching, allowing banks to consolidate and integrate information about the same customer, building a complete picture with whom they are working with.
4. Smarter with time: Unlike traditional data mining tools, AI technologies can learn from previous cases and ongoing updates and incorporate this knowledge into future searches and analyses. It can also add new information to past knowledge, flagging, for instance, transactions linked to geographies that have recently been classified as risky.
The ultimate goal of using AI for KYC compliance is not to build a customer profile. The true purpose of KYC compliance is to know your customer at the point when you are doing business with them, even when you have thousands or millions of customers in your system.
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