Simplifying the Data Tier for Real-Time Fraud Detection

Fraud is a billion-dollar business and increases every year. In 2019, 62% of breached data came from the financial services industry alone. With financial institutions increasing their investments into digital channels, their exposure to new threats will only intensify. While risk management leaders are focused on mitigating this growing threat, they are challenged by the inability of traditional data platforms to handle the speed, scale, and complexity of new online transactions.

In this session, we will discuss how Redis Enterprise is used by risk management professionals to support real-time fraud detection across digital identities, statistical analysis, AI transaction risk-scoring, anomaly detection, and more. Learn how you can leverage a multi-model in-memory database to expedite frictionless online transactions and reduce false positives without overcomplicating your enterprise architecture or external vendor solutions.

Key points covered:

• Increasing investment into digital channels exposes financial institutions to new threats 
• Traditional data platforms were not built to handle the speed, scale, and complexity of online transactions
• Learn how Redis Enterprise is used to support real-time fraud detection across digital identities, statistical analysis, AI transaction risk-scoring, anomaly detection, and more