A Comprehensive Survey of Data Mining-based Fraud Detection Research

TL;DR

This comprehensive survey reviews a decade of data mining-based fraud detection research, categorizing methods, types of fraud, and data evidence, highlighting technical advancements and alternative solutions across industries.

Contribution

It uniquely consolidates a wide range of technical articles, formalizes fraud types, and introduces alternative data sources and solutions from related domains.

Findings

Extensive categorization of fraud detection techniques

Identification of data evidence used in industries

Comparison of methods and their challenges

Abstract

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.