A Collaborative Intelligence (CI) Framework for Fraud Detection in U.S. Federal Relief Programs

Abstract

Fraud in U.S. Federal Relief Programs poses significant risks to government budgets, public trust, and the sustainability of aid programs that are designed to shore up communities and businesses across this crisis. We argued that a dual approach, which brings together human expertise and state-of-the-art Artificial Intelligence techniques-often referred to as Collaborative Intelligence (CI)-can offer a potent means for detecting, investigating, and preventing such fraud at scale. The proposed model connects many data sources, such as government registries, payroll records, banking transactions, and open-source intelligence in one central data lake, for general oversight. In addition, the application of machine learning algorithms is complemented by graph analytics and natural language processing in underlining various anomalies, such as documentation falsification, misrepresentations regarding eligibility, shell companies, and suspicious patterns of transactions. These AI-generated alerts then get refined by investigators, compliance officers, and auditors by investigating high-risk cases and providing the models with additional contextual knowledge. Moreover, solid governance practices of privacy, security, and legal compliance assure that the personal data of individuals are handled in a responsible manner, along with the ethical and lawful treatment of investigative processes. This could strengthen integrity within relief programs and safeguard much-needed financial assistance for legitimate recipients.