While covered financial institutions are required by FinCEN to know the identity of the individuals who own or control their legal entity customers (also known as beneficial owners), the standard is increased significantly over the years. The beneficial ownership requirement of the CDD (Customer Due Diligence) Rule provides information that will assist law enforcement agencies in financial investigations, help prevent evasion of targeted financial sanctions, improve the ability of financial institutions to assess risk, facilitate tax compliance, and advance U.S. compliance with international standards and commitments.

Most financial institutions take a client-centric view to understand the nature and purpose of their relationships, but rarely review account activity from both a client and individual account perspective. With the CDD Rule, financial institutions are required to understand the purpose of the account, account operating behavior, and which products and/or services are in use, as well as the volumes and values of transactions. Account-level analysis enables financial institutions to improve their risk management and better understand client needs.

Many institutions set activity thresholds which are tested against bank accounts. These thresholds are typically static in nature and estimated by clients or relationship managers to approximate the expected activity of accounts in terms of both volume of transactions and their value. Deviation of activity from what is expected can be considered a red flag in terms of identifying possible money laundering. Setting static thresholds is a good start and better than not setting any activity thresholds.

However, being able to get a regular view of expected account activity in a fashion that is specific to individual accounts, while controlling for expected deviations driven by seasonality, foreign exchange deviations or any other legitimate driver of change is paramount to understand the risks inherent with a particular account.

This is where artificial intelligence (AI) and machine learning can help. The ability to mathematically understand the context of an account, and then calculate a dynamic expectation of activity will eliminate many false positives that are often triggered using traditional, rules-based methods. Equally important is the ability to identify the true risks when an account displays an anomaly that may be within a static threshold.

Fortunately, implementation of AI technology and setup is easy for banks and covered financial institutions in that there is no need to manually evaluate individual accounts or set a threshold – this is all done automatically by the AI system. By analyzing at least six months of account activity, institutions can develop more accurate expectations of future account behavior.

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