Information Spillovers and Semicollaborative Networks in Insurer Fraud Detection

IT-related spillovers occur via data- and information-related transmission paths, so these types of spillovers are best studied through process-level measures. The medical claims fraud detection is a prototypical data- and information-intensive process in insurance companies. This paper examines the impact of IT-related spillovers on fraud detection outcome for insurers and patient-care quality for hospitals. I theorize three semicollaborative networks formed between state-level subsidiaries of insurers (regulation-bound network), between subsidiaries of an insurer parent company (sibling network), and between insurers and hospitals (risk-sharing), and hypothesize that these networks convey information spillovers. I then examine the impact of spillover benefits and the impact of spillover IT pools on future IT-related investments. The empirical analysis is conducted using 2011-2013 data for insurers and hospitals. I use a generalized linear model with a Tweedie distribution to correct for the finite mass of zeros for my dependent variable. Results reveal that the sibling network contributed the most of the spillover benefit, and the risk-sharing network did not contribute to fraud detection. I also find that the sibling network depresses future spending on fraud detection