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Resistant AI’s Transaction Forensics Strengthens FINOM’s Anti-Money Laundering Defenses

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As part of its anti-money laundering efforts, Resistant AI, an AI, and machine learning expert announced today that FINOM, a small business and professional fintech platform that focuses on small businesses and professionals, decided to adapt Resistant AI Transactions Forensics. In addition to providing important context, Resistant AI will uncover new kinds of anomalous behavior that FINOM is unaware of.

Sergey Petrov, co-founder and managing director at FINOM, commented, “In a world that is seeing ever-increasing incidents of fraud and money laundering, we need to have the most effective tools and techniques to detect suspicious activities. On the other hand, they should not create any friction or obstacles for legitimate clients. Resistant AI’s solution perfectly complements FINOM’s AML and Anti-Fraud program with its explainable AI, ensuring transparency to AML analysts as well as the regulator.”

A smart alert prioritization feature enables analysts to focus on the highest-risk cases first by integrating Resistant AI’s technology into FINOM’s existing internal transaction monitoring system. This technology adds additional context to current alerts, detects previously unknown types of suspicious behavior, and provides intelligent alert precedence allowing analysts to focus on the highest-risk cases first.
Resistant AI can help identify legitimate and illegal activities more accurately by analyzing hidden relationships between identities and transactions. Organizations will become more aware of novel criminal activity by identifying advanced layering and muddling techniques.

Martin Rehak, founder, and CEO, of Resistant AI, added, “We are delighted to help FINOM simplify and speed up the detection of suspicious activity. Criminals are always developing more subtle and discreet ways of moving money illegally; our tools use AI to spot both existing and new behavior without generating thousands of false positives which need to be manually processed.”

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