Hawk Platform Spots Risk by Identifying Links Between Data Points

Hawk’s artificial intelligence (AI)-powered anti-financial crime platform now includes additional capabilities for spotting risk by identifying links between data points.

The company’s new Entity Risk Detection module brings together entity resolution and network analysis with real-time risk identification, according to a Wednesday (July 3) press release.

By consolidating datasets and merging customer profiles, the platform can help users better detect risk and complete investigations, according to the release.

With these capabilities, financial institutions spot links and other clues that will help them identify suspicious activity, at a time when money launderers are constantly trying to throw them off course by creating a “smokescreen,” Wolfgang Berner, chief product officer and co-founder of Hawk, said in the release.

“Entity Risk Detection helps to clear the smokescreen,” Berner said. “For example, it can help financial institutions to see that Customer A is using Product X and does not appear to present a risk to the business, but they are in fact the same person as Customer B using Product Y — and Customer B is linked to Firm C, which is on a sanctions list.”

The new module is fully integrated with Hawk’s anti-financial crime platform, providing banks and payment providers with a comprehensive solution that covers anti-money laundering (AML), sanctions screening and transaction fraud management, per the release.

With the addition of consolidated and enriched data from Entity Risk Detection, the AI-powered platform can improve prevention, detection and investigation, according to the release.

“AI thrives on good data,” Berner said in the release. “The organizations that can fuel their AI with rich, high-quality data will be in a significantly strong position to maximize risk coverage and operational efficiency.”

Hawk said in January that its then-newly appointed chief solution officer, Michael Shearer, authored a book on entity resolution called “Hands-on Entity Resolution: A Practical Guide to Data Matching with Python.”

Seven in 10 financial institutions are now using AI and machine learning (ML) to fend off fraudsters, according to the PYMNTS Intelligence and Hawk collaboration, “Financial Institutions Revamping Technologies to Fight Financial Crimes.”

The report also found that financial institutions rely on a mixture of in-house fraud prevention systems, third-party resources and new technologies to protect their institutions and customers.

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