Intelligent Financial Fraud Detection Practices: An Investigation

TL;DR

This paper reviews automated financial fraud detection methods, emphasizing computational intelligence techniques, analyzing their effectiveness, challenges, and future research directions in the context of big data and evolving technologies.

Contribution

It provides a comprehensive investigation of data mining and computational intelligence-based practices for financial fraud detection, highlighting key aspects and challenges.

Findings

Classification of detection practices based on algorithms and fraud types

Identification of issues and challenges in current detection methods

Discussion of future research directions in financial fraud detection

Abstract

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.