Forensic Data Analytics: The future of detecting, predicting and preventing Anti-Money Laundering risks (PART II)
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 www.ey.com/FIDS
The benefits of using FDA:
Behavior and prediction – FDA combines the extensive use of big data and statistical and qualitative analysis in conjunction with explanatory and predictive models to guide and identify AML/CFT violations and areas warranting further review. Our fact-based evidence drives actionable business decisions, focuses investigative efforts where it matters, and optimizes outcomes. FDA comprises proactive and reactive methodologies that leverage the information contained in large-scale, structured and unstructured data sets. This allows banks to effectively detect and prevent fraud, to identify instances of error, ML/TF typologies and misconduct, or to address to a regulatory response.
Network analytics – There are three key advantages to network analytics. Firstly, the technique itself helps identify high-risk stakeholders, key entities, sensitive files and keywords. Secondly, it ensures that data sources for monitoring includes due diligence databases, hidden network access of social media networks, blogs, forums and high-risk IP addresses. Lastly, it identifies certain behavior, including cyphers, negative emotions and topic modeling.
Ability to detect hidden behaviors – These behaviors cannot be found using standard and traditional two-dimensional models. The FDA approach incorporates targeted model-based mining and visual analytics tools that allow the data to ‘speak for itself’. When deployed over large data sets, our analytics can be a powerful tool to identify large and unusual transactions or anomalies derived from multidimensional attributes within a bank’s transactional data. Model-based mining shifts the focus to high-risk areas where controls may not necessarily exist or are perhaps even bypassed. FDA has the ability to detect abnormal behavioral changes in the data, as it compares all data sources against each other.
Predictive modeling and detection – With historical corporate big data, unethical behavior models and statistics, and data from numerous past and real world scenarios, FDA can be transformed into a predictive analytical platform to detect potential risks or threats before they occur. With today’s technological advances, the future of fighting crime and exceeding regulatory requirements is the ability to predict and mitigate risks before they occur.
Major Banks are currently using FDA in the following ways:
Banks are implementing FDA risk scoring models into their existing transaction monitoring systems, which focus on identification of suspicious patterns of transactions that may result in the filing of Suspicious Activity Reports (SARs) or Suspicious Transaction Reports (STRs). FDA transforms a traditional, two dimensional outlier identifier into a 3D analytics platform. FDA risk scoring models also allow banks to convert and profile reports into high-risk populations of people/entities for further data mining.
1. A major Chinese bank with strong credit card business is leveraging FDA to detect and identify potential AML activities outside of the traditional rule-based monitoring system.
Bank’s current challenges
1. A global leading international bank is leveraging FDA to detect AML activities and predict high/medium/low risk transactions
Bank’s current challenges
Developing an FDA strategy and putting the systems, software and tools in place to execute is critical. But more importantly, an effective FDA program requires conversations across multiple departmental lines to understand the business and its data from all angles. As such, it is vital to cultivate the right thinking and skillsets within the business and define new positions that defy traditional job titles and responsibilities. At EY, we understand the significance of Big Data and FDA, and as such we have invested over USD500M into our data analytics strategy to help our clients develop and implement successful FDA programs by combining state-of-the-art technology with our people and our rich experience in banking, AML/CFT, sanctions and fraud risks.