Nature of Consumer-Type Insurance Fraud and the Size of Associated Financial Losses among Medical Insurance Providers in Kenya

Medical insurance fraud is currently a pressing challenge in Kenya, yet its detection and scale, especially within consumer-initiated schemes, remain under-examined in scholarly literature. This study sought to investigate the relationship between the nature of detected consumer-type medical insurance fraud and the size of the associated financial losses among insurance providers in Kenya. Anchored in the Fraud Hexagon Theory and the Red Flag System Approach, the study employed a descriptive cross-sectional design using a non-reactive methodology. A census of 53 fraud cases detected over a six-month period across 14 medical insurance providers was conducted. Data were gathered from organizational records, and analysis was performed using SPSS version 28. Descriptive statistics assessed forms of fraud, while chi-square tests and Pearson correlation was used to examine associations between fraud characteristics and financial impact. Findings showed that falsification of claims, pharmacy-related fraud, and member substitution were the most prevalent forms of insurance fraud. The study established that while the nature of fraud (e.g., form, perpetrator, motivation) contributes to variability in loss magnitude, there is limited use of predictive detection models within the sampled organizations. The study concludes that fraud detection strategies, including the application of red flag indicators and institutional capacity to act on early signals, are critical to minimizing financial losses. It recommends the institutionalization of predictive analytics, integration of fraud typologies into detection protocols, and establishment of a national guideline on fraud detection and reporting. The study also contributes to existing criminological and financial fraud literature by providing reliable empirical data on the typologies and economic impact of medical insurance fraud in the Kenyan context. Limitations and areas for future inquiry are discussed.

Medical insurance fraud, fraud detection, consumer fraud, fraud magnitude

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