Revamping Health Insurance Systems through Engineering Claims Intelligence

Abstract

This research explores the use of claims-intelligence engineering as a technology that can be used to modernize health-insurance systems, with a specific focus on the implementation of machine-learning systems, specifically, the use of Random Forests, to detect fraud and estimate the likelihood of risk events. Among the factors that impede the health-insurance field are lengthy claims filing, fraud, and wasteful allocation of funds. Through claims-intelligence engineering, insurers are in a position to automate and optimize the adjudication processes, thus enhancing the accuracy and operational throughput. The empirical findings prove that the model of the Random Forest combines a 75% fraud claim forecast accuracy with considerable precision and recall. However, a rather poor recall rate of the model confirms the need to tune the model further, particularly in the reduction of false negatives. Future trends in claims intelligence include the integration of more complex machine-learning systems, the bringing together of disparate data, and the transition to real-time processing of data streams. Influential parts in the guidance of the transformation of claims intelligence within the health-insurance sector will be ethical considerations, regulatory compliance, and the need to have explainable artificial intelligence.