Survey of Insurance Fraud Detection Using Data Mining Techniques

TL;DR

This survey reviews data mining techniques used for detecting insurance fraud, highlighting recent advances, challenges, and solutions in financial fraud detection within the insurance industry.

Contribution

It provides a comprehensive overview of data mining methods specifically applied to insurance fraud detection, emphasizing recent developments and future research directions.

Findings

Data mining techniques improve accuracy in insurance fraud detection.

Challenges include handling large and complex financial data.

Various solutions enhance fraud detection effectiveness.

Abstract

With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.