Originally Presented: November 7, 2018
Institutions are facing increased pressure to mitigate the risk of fraud and money laundering, and stay compliant. But as workload and costs increase, false positives can overwhelm already strained resources, so there is significant risk in relying on manual processes or rules-based monitoring systems.
To effectively fight crime, investigators need to leverage vast amounts of data from numerous sources in their analysis. Verafin imports and analyzes an immense data set from multiple sources, including core data, ancillary data, open-source and third-party data, and consortium data. In fact, before Verafin it would have been unthinkable to analyze that much data. By applying cross-institutional analysis and machine learning technology to analyze a billion transactions every week, our approach reduces false positives and increases the quality of your alerts.
Learn how Verafin’s Big Data Intelligence approach keeps you ahead of fraud trends and regulatory changes, provides higher-quality, targeted alerts, and gives you a complete view of activity, including crimes that span multiple institutions.
Highlights of this presentation will include how Verafin’s Big Data Approach helps you fight crime by:
- Providing a complete picture of activity in a single system ensuring greater visibility of potential risks, while saving you valuable time and money.
- Leveraging cloud technology and cross-institutional analysis to proactively detect and mitigate risk, reduce costs, and protect your customers and institution.
- Integrating and analyzing multiple data sources to improve alert quality, such as peer profiling, high-risk customer labeling, payee confidence, and geolocation data.
- Applying Machine Learning to learn from labeled data, further improving detection and monitoring capabilities.
- Partnering with you to develop solutions to real-world problems with targeted, expert-driven models for Fraud Detection, AML Transaction Monitoring and High-Risk Customer Management.