Detecting Fraud With a Real-Time Data Platform

The financial services industry is struggling to keep up with the increasing number of fraudulent transactions and evolving tactics of cybercriminals. As digital banking usage increases, online fraud has also risen, with 51% of businesses experiencing fraud in the last two years, resulting in an estimated $42 billion loss. To address this issue, banks and financial services organizations are turning to digital identities, combining document verification, biometric records, and behavioral patterns to create a dynamic and complex identity for each user. However, updating information quickly enough to stay ahead of criminals without hindering the genuine user's experience remains a challenge. Machine learning algorithms and artificial intelligence predictive models can evolve and learn as they analyze and detect payment fraud based on historical and real-time transactional information, but successful implementation depends on accurate models, performance, and resiliency of underlying machine learning operations database. The industry needs to adapt to the digital age and move away from legacy relational database management systems to support modern AI/ML-based fraud detection and dynamic digital identities.

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