Predictive Accuracy of Machine Learning Models in Fraud Detection for Health Insurance in India

Abstract

Purpose: The aim of the study was to assess the predictive accuracy of machine learning models in fraud detection for health insurance in India.

Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.

Findings: The study indicated that advanced machine learning algorithms, such as deep learning and ensemble methods, achieve high accuracy rates in identifying fraudulent claims. These models leverage large datasets encompassing diverse variables related to patient demographics, medical histories, and billing patterns to discern fraudulent activities effectively. Studies indicate that the precision and recall rates of these models often exceed traditional rule-based systems, thereby reducing false positives and enhancing overall detection efficiency. Furthermore, research highlights the adaptability of machine learning models to evolving fraud tactics, such as billing anomalies and coordinated schemes, by continuously learning from new data inputs. This adaptability is crucial in the dynamic landscape of healthcare fraud, where fraudulent patterns evolve rapidly. The integration of these predictive models into existing health insurance fraud detection systems has demonstrated substantial improvements in operational efficiency and cost savings. Consequently, stakeholders in the healthcare industry are increasingly investing in machine learning technologies to bolster their fraud detection capabilities, aiming to mitigate financial losses and preserve the integrity of insurance systems.

Implications to Theory, Practice and Policy: Signal detection theory (SDT), bayesian decision theory and information theory may be used to anchor future studies on assessing the predictive accuracy of machine learning models in fraud detection for health insurance in India. Implement robust feature selection strategies, leveraging domain knowledge and advanced statistical methods, to identify the most relevant features for fraud detection in health insurance. Advocate for the adoption of standardized evaluation metrics, such as F1 score, recall rate, and precision, to benchmark and compare the predictive accuracy of machine learning models across healthcare organizations.