FSense: Fraud Detection via Complex Financial Pattern Analysis

This NSF Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) funded project focuses on building a platform that will integrate data from multiple sources and explore data analysis techniques that can more accurately detect indications of financial fraud. According to Federal Trade Commission's (FTC) annual "Consumer Sentinel Network Data Book", the most comprehensive database of U.S. fraud trends, American consumers submitted more than 1.5 million complaints - a 62 percent increase in just three years, and they reported losing over $1.6 billion to fraud in 2013. Detecting increasingly complex fraud schemes requires services that are able to integrate and enrich data from disparate financial and other data sources and hunt for recurring and often interconnected anomalous patterns in large networks. The proposed platform will enable integration and enrichment of limited private financial data with larger publicly available data sets to detect fraud and reduce losses due to fraudulent transactions. The project will also include training and research experience for undergraduate and graduate students.

The data linkage and financial pattern discovery platform which is to be developed via visual analytics will enable "smart" fraud detection and prevention services. Today, in order to obtain a single unified view of fraud activity across the enterprise and manage fraud on a cross-institution basis, fraud detection companies collect, verify, and analyze consumer data and financial information. Researchers recognize, however, that new insights into fraud and risk patterns require the ability to integrate financial data with domain independent data through real-time entity/identity discovery, resolution, cross-linking and schema mapping techniques. Therefore, the importance of the research discovery underpinning this project includes solving platform and processing challenges that arise from the need to integrate, filter, analyze, and visualize, in a secure and scalable manner, large private knowledge networks, also incorporating uncontrolled, unrestricted, untrusted, unstructured and unpredictable data from external domains. The ability to treat together financial and domain independent data will lead to enriched unified data, unprecedented predictive accuracy in fraud prevention and detection, and an entirely new suite of risk management services and products.

In this project, we are partnering with  Early Warning Services, LLC, known throughout the financial services industry as a leader in fraud prevention and risk management. EarlyWarning, a limited liability company owned by Bank of America, BB&T, Capital One, JPMorgan Chase and Wells Fargo,  provides its customers with fraud and risk management tools through collaboration and sharing of information within the industry.