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

Author: Emmanuel Vignal, Chi Chen, Diana Shin

October-28 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 www.ey.com/FIDS

The benefits of using FDA:

  1. 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.

  2. 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.

  3. 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.

  4. 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

  • One person may have multiple credit card accounts with different names, IDs, and addresses. How do they identify which credit card account belongs to which customers?
  • Identify and compare account level behavior for each account in cash deposits to the credit card and cash withdraws to the credit card
  • Identify potential outliers in credit card account transactions and continue improving AML policies and procedures through collected data
  • FDA Solution

  • Leveraging current banking system infrastructure to collect relevant data and identify bank’s own risk Indicators
  • Leveraging cluster analytics to identify which person may potentially have multiple credit card accounts and the behavior of those accounts
  • Leveraging network analytics to determine the relationships between each account and their behavior towards cash deposit and cash withdrawal activities
  • Leveraging predicted modeling to identify high/medium/low credit card transactions that relate to AML
  • Develop rule-based risk scores for each and every transaction, account and business to identify potentially high risk activities
  • Developed interactive and visual dashboards that allow senior management to quickly see where the high risks are in their bank and to easily navigate through the transactional data
  • 1. A global leading international bank is leveraging FDA to detect AML activities and predict high/medium/low risk transactions

    Bank’s current challenges

  • Existing clients’ IT infrastructure was neither designed to process today’s magnitude, complexity or workload of data, nor was it designed to effectively prevent and detect fraud
  • The top two client challenges using Forensic Data Analytics were “getting the right tools and expertise for forensic data analysis” and “combining data across various IT systems”
  • To view global transactional data in one dashboard with the capabilities of identifying outliers, viewing transaction relationships and predicting high risk transactions from rule based to statistical prediction
  • FDA Solution

  • Developed interactive, visualized dashboards for senior management to navigate through their transactional data
  • Leveraged predicted modeling to identify high/medium/low transitions that relates to AML by region, by country, by department and by resources
  • Developed rule based risk scores for each and every transaction, account and business to identify high risk activity
  • Leveraged network analytics to understand internal resource relationships, behavior of the customers (Know Your Customers) and third-party relationships

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.

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