How Mikhail Matveev Built an AI System That Detects Fraud Without Frustrating Customers
Financial institutions face increasing challenges with fraud, including deepfakes and synthetic identities. Despite significant investments in AI, fraud-related losses continue to rise. Mikhail Matveev, Chief Data Officer at B9, a financial app serving…
Financial institutions face increasing challenges with fraud, including deepfakes and synthetic identities. Despite significant investments in AI, fraud-related losses continue to rise. Mikhail Matveev, Chief Data Officer at B9, a financial app serving 1.6 million customers, has developed an AI system addressing these challenges.
The AI system incorporates liveness verification and behavioral analysis to detect fraudulent activities without disrupting legitimate customers. This approach has resulted in a 35% reduction in chargeback losses over a three-month period. The system continuously monitors customer activity to identify anomalies and potential fraud throughout the customer journey.
Adapting to the Evolving Fraud Landscape
The fraud landscape, including technologies like face-swapping and AI-generated identities, is rapidly evolving. The system updates its rules and strategies swiftly to counteract new fraud techniques. This adaptability ensures that the AI system remains effective against emerging threats.
Since the implementation of AI-powered verification, B9 has seen a 20% reduction in overall risk rate, enhancing portfolio quality while maintaining a seamless experience for legitimate users. The system's adaptability is crucial in balancing fraud prevention and customer experience.
Financial institutions face increasing challenges with fraud, including deepfakes and synthetic identities.
The AI system extends beyond initial verification, analyzing continuous behavioral patterns such as transaction frequency and timing anomalies. It provides real-time alerts for suspicious activities, enabling proactive fraud management. The data strategy focuses on integrating multiple data points to develop a dynamic risk profile for each customer.
Matveev's experience in building data infrastructure across various markets has informed his approach to fraud prevention. In Singapore, he led initiatives that improved data retrieval, reporting accuracy, and reduced data quality issues, which are critical to informed decision-making in financial services.
The future of fraud prevention lies in adaptive systems that leverage generative AI tools for both fraud creation and detection. Companies investing in foundational data infrastructure and clear data management practices will be better positioned to respond to evolving fraud tactics.
Effective fraud prevention requires a balance between robust security measures and customer convenience. The AI system developed by Matveev demonstrates that it is possible to achieve this balance, highlighting the importance of continuous innovation and data strategy in combating financial fraud.
Based on reporting by TechBullion.