Personal Communications of Financial Transaction Data

When banks harness the value of this data, they are able to set up big data analytics to help drive the discovery of money laundering. As banks are actively adopting big data platforms, including Hadoop and supporting analytical tools, they can be applied to help solve this problem. A platform that enables the ingestion, enrichment, analysis, and visualization of these diverse, large, and constantly changing data sets can be the bank’s best asset.

I recently had the opportunity to speak with a former investigator with Homeland Security Investigations (HSI) who runs a consultancy focused on using big data analytical tools to assist banks in achieving better AML compliance. He describes a situation where financial institutions are experiencing an exponential growth in AML compliance requirements and reporting burdens. “By far, the best solution to fulfill rapidly growing compliance requirements is provided by big data analytical tools which drastically lower compliance costs and satisfy the due diligence required by regulatory agencies,” says the former investigator.

Examples of common analytics include reviewing transaction data to see if there are trends in transaction size. Specifically, currency transaction reporting is required for all transactions above $10,000 and money launderers have resorted to completing transactions just below this threshold to avoid those reporting requirements. In the industry, this is known as smurfing and identifying consistent behavior of completing transactions just below this threshold may indicate money-laundering activity.

In addition, by enriching transaction data with client/legal entity data (including names, addresses, and other identifiers), and publicly available OFAC lists, banks can track transactions to determine if they were completed by known high risk individuals or non-cooperative jurisdictions. Enriching this data further, with verbal and written communications information can help cast the net wider when looking at potential indicators.

In the world we live in today – no individual or institution wants to aid money laundering or provide financing to ISIS or other known terrorist or criminal organizations. With the right investment in the right technology and data platforms, we can all sleep better at night knowing that we are applying big data analytics to help address the problem. 


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