Forensic Data Analytics: The future of detecting, predicting and preventing Anti-Money Laundering risks

Author: Emmanuel Vignal, Chi Chen, Diana Shin

October-07 2016

Chi Chen and Diana Shin are both Partners at EY, based in Shanghai. Emmanuel Vignal is the Greater China Leader and Partner, Fraud Investigation & Dispute Services. For more information on FIDS, visit

The changing regulatory landscape within the financial services industry has made recent years extremely complex and difficult for both domestic and international banks to operate efficiently. In attempts to assimilate current business operations to the ongoing market changes, banks are devoting more resources to Know Your Customers (KYC), Anti-Money Laundering (AML), fraud detection and prevention, as well as sanctions compliance.

Recent AML penalties and fines handed down to major banks around the world verify that one or two dimensional customer screening or transactions monitoring programs are simply not sufficient to properly manage the compliance risks present today. In order to meet the ever-evolving regulatory environment and address the AML regulatory environment, the best possible solution for banks is to use a scalable, multi-dimensional data analytics technology to identify hidden abnormal behaviors within mass amounts of random data, predict risks based on real world scenarios and deliver intelligence business insights to make strategic decisions, as well as to continue improving AML processes and procedures based on data and behavior rather than just updating traditional rules in the hopes of identifying the modern day criminal.

Traditional AML transaction monitoring is a rule based monitoring system (two dimensional monitoring) that satisfies basic regulatory and business requirements. However, we need to ask ourselves if a rule based monitoring system based on standard policies and procedures is impenetrable or is it applicable and encompassing to your global operations? The global financial environment and the way people conduct business with it or within it is ever changing; yet, AML policies and procedures have remained mostly unchanged. As such, business growth requires more advanced and innovative strategies to be competitive, yet remain compliant in today’s environment.

Analytics as a part of the business discipline has existed for decades within the banking industry. Its application and acceptance have increased recently due to a number of reasons, including but not limited to: the pace and scale at which data is accumulating from different sources, the vast amounts of structured and unstructured data, the speed of access, the rapid reduction of storage costs and the sophistication of enabling tools and technology. Forensic Data Analytics (FDA) combines highly specialized and skilled people, advanced technology and years of forensic experience to help banks and their management make quicker, more intelligent and well-informed business decisions. FDA now sits at the top of the AML monitoring agenda for many leading financial institutions looking for more effective ways to identify outliers, hidden transactions, unknown relationships and abnormal activities that cannot be seen at the transactional level and could now be linked together with FDA.

FDA takes the data collected and analyzes it at a whole new level, a multidimensional analysis combining structured and unstructured data that identifies hidden relationships and correlations. By comparing each transaction, understanding the behavior of the transactions, and applying WHO, WHAT, WHERE, WHEN, WHY and HOW techniques to all transactions, we are not only able to identify suspicious activities, but we are also capable of finding loopholes in the internal controls, policies and procedures that require constant fixing and improvement. Furthermore, FDA allows banks to predict and score day-to-day transactions as high/medium/low risk activities based on past behaviors at the bank and historical industry benchmarks. Changing and customizing policies and procedures based on the data that has been collected and analyzed for each individual bank is essential, because the data pinpoints and tells you what is really happening at each bank and what the areas are that need to be strengthened. Generic and traditional rules can no longer be used to apply to global banking operations when in fact, each market is unique and its peoples’ behavior differs due to diverse customs.

The heart and soul of FDA is within the risk indicators that are tailored to each bank and its multiple business operations. These quantifiable risk indicators are developed from risk scoring algorithms based on historical unethical behavior data and weights calculated for each risk factor. These indicators should be dynamically changed and updated based on data behaviors, business growth strategies, and regulatory changes. Assigning mathematical risk scoring to each transaction, person and company gives banks a three-dimensional data analytics capability to predict potential high-risk AML/CFT non-compliance issues before they occur.

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