Leveraging Artificial Intelligence for Early Fraud Detection in Insurance: Focusing on Intake and Claims Processing

Financial fraud has been resulting in substantial losses, leading researchers and academics to explore developing a rigorous method for detecting and preventing such fraud. They are broadly classified into four different categories namely securities and commodities fraud, bank fraud, insurance fraud and other financial fraud. Insurance fraud, however, is a serious and growing problem, has received a lot of attention since a variety of fraudulent methods result in significant losses for insurance firms and that traditional approaches to tackling fraud are inadequate and has become increasingly complex as fraudsters adapt to new technologies and strategies. Research on insurance fraud has traditionally concentrated on identifying attributes of fraudulent claims and claimants. This emphasis is evident in the latest advancements in forensic and data analysis technologies for detecting fraudulent activities. An alternative method involves optimizing and subsequently enhancing current procedures in the detection of fraudulent activities. Artificial Intelligence (AI) is emerging as a powerful tool in mitigating fraud risks by identifying patterns and behaviors that may indicate fraudulent activity. This paper explores the role of AI in early fraud detection during the intake phase of policy underwriting and the claims processing stage. Additionally, it addresses a more insidious form of fraud involving agents who engage in internal policy manipulation to trick carriers into paying for the same policies multiple times. The paper also highlights AI - driven strategies for combating these fraud risks and suggests best practices for insurers seeking to deploy AI in their fraud detection efforts.