Fraud Detection in Health Insurance Using Mac...

Fraud Detection in Health Insurance Using Machine Learning

Author Name : M H Hemanth Kumar, Mohammed Ahmed Ali, Anuraag Raghuramchandra Kaveeshwar, Gaddam Anudeep, Gorle Chandra Sekhar Naidu, Sandesh Maddila

ABSTRACT

Inappropriate payments by insurance organizations or third-party payers occur because of errors, abuse, and fraud. It is estimated that approximately 10% of medical expenditures are wasted in medical fraud and abuse. The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. Combining automated methods and statistical knowledge lead to the emergence of a new interdisciplinary branch of science that is named Knowledge Discovery from Databases (KDD). Data mining is a core of the KDD process. Data mining can help third-party payers such as health insurance organizations to extract useful information from thousands of claims and identify a smaller subset of the claims or claimants for further assessment. We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. Most available studies have focused on algorithmic data mining without an emphasis on or application to fraud detection efforts in the context of health service provision or health insurance policy. More studies are needed to connect sound and evidence-based diagnosis and treatment approaches toward fraudulent or abusive behaviors

Problem: Despite putting up various technologies and strategies to fight fraud such as planned, targeted, audits and random audits, whistle blowing, and biometric systems, fraud in claims have continued to be a challenge in most of the health insurance providers across the world.

Purpose: This project tried to analyze the appropriateness of data mining techniques in detecting fraudulent health insurance claims

The goal of this project is to “predict the potentially fraudulent providers " based on the claims filed by them. Along with this, we will also discover important variables helpful in detecting the behavior of potentially fraud providers. Further, we will study fraudulent patterns in the provider's claims to understand the future behavior of providers.

Method: To achieve our goal classification models were used to guide the entire knowledge discovery process. Classification and regression algorithms such as Random Forest, SVM, Naïve Bayes, Decision Tree, Gradient booster etc. were used to build predictive models.

Findings: Several Experiments were conducted and the resulting models shows that SVM works well among the other algorithms in predicting fraud claims with an accuracy of 91.7%

Conclusion: Fraud detection in health insurance companies, is much needed in developed and undeveloped countries so as to reduce loss of money and resources and in return improve the service delivery to patients.