USCIS Fraud Detection Improvement Case Study

This case study details the efforts of the United States Citizenship and Immigration Services (USCIS) to enhance its fraud detection capabilities concerning immigration applications. The primary challenge identified was the agency's reliance on forms-based data collection methods, which led to issues such as duplicate names and addresses, as well as human errors in data entry. To address these challenges, USCIS implemented Senzing entity resolution technology, which facilitates the identification of relationships between applicants and their representatives, including lawyers. The solution integrates network analytics with natural language processing, allowing analysts to efficiently match records and query data from diverse sources. The results of this implementation have shown improved fraud detection capabilities, a more streamlined user experience, and significant cost reductions in data processing. Furthermore, the technology's explainability features enhance compliance by clarifying decision-making processes related to entity resolution.