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Using a data platform to reduce money laundering risk

We built a data platform for a Midwest bank to help reduce its risk of fraudulent activity.

Data Operations
Architecture & Platforms
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A Midwest bank needed a data platform to help with risk-profiling, isolating fraudulent activity, and ensuring that legitimate transactions went uninterrupted. In particular, the bank wanted better ways to stay compliant with government anti-money laundering regulations for commercial transactions.

Money laundering is a serious risk for banks. Over $1 trillion is laundered by criminal or terrorist organizations each year — with as much as half of that activity going undetected by the usual fraud alerts. And banks face stiff regulations and crippling penalties if they fail to identify fraudulent money laundering activity.

This bank already had a system in place for its retail customers — those with personal checking and savings accounts. But the system it had in place for commercial customers lacked risk-scoring capabilities and would not meet compliance standards.

The bank chose g2o to aggregate the data it needed for risk-scoring and to develop a data platform that would provide a unified view of the customer’s risk profile across both retail and commercial businesses.

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Developing an anti-fraud solution for the commercial business meant evaluating 45 unique systems that contain valuable risk data. In Phase I, g2o examined these different systems and isolated the data available to feed to the bank’s anti-money laundering platform. The initial systems set was then analyzed for any vulnerabilities to data quality.

Phase II involved developing a detailed plan to transform and standardize the data for the bank’s unified anti-fraud solution. This culminated in finalizing the data architecture and integrating all necessary data.

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The implementation of an accurate and unified anti-money laundering solution helps to ensure regulatory compliance and reduces the expense associated with addressing fraud.

Reports of suspicious transaction activity may be fraudulent or innocent, but it is expensive either way. By law, each positive report must be individually investigated and those high overhead costs are the best case.

At worst, the bank will find fraudulent behavior that makes the bank liable for full restitution. Beyond the reparations, a bank is then vulnerable to federal fines, increased regulation, litigation costs, and a marred reputation. These consequences are avoided with a well-designed program and accurate data. False positives are reduced and patterns of criminal activity can be smothered before grave damage is done.

Armed with a holistic view of customer risk profiles and standardized transaction data, the bank is primed and ready to deliver anti-money laundering protection and real-time fraud detection.

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