Detection of fraud risks in retailing sector using MLP and SVM techniques

Abstract

In today's business conditions, where business activities are spreading over a wide geographical area, fraud auditing processes have crucial importance especially for the retailing sector which has a high branch network. In the retailing sector, especially purchasing processes are subject to high fraud risks. This paper shows that it is possible to detect fraudulent processes by applying data mining techniques on operational data related to purchasing activities. Within this scope, in order to detect the fraudulent purchasing operations, support vector machine (SVM) models with different kernels and artificial neural networks methods have been used and successful results have been achieved. The results of the two methods have been examined comparatively and it shows that optimized SVM classifier outperforms others. Besides, in this study, it is presumed that the detected fraud data can be proactively used in the struggle against fraud with fraud-governance risk and compliance software by converting it into scenario analysis.

Identifying Application Areas for Machine Learning in the Retail Sector

“…Fraud detection in retrospect must be dealt with in a human resources management process. Pehlivanli et al [ 69 ] pursued a different approach by analyzing transaction data. ML algorithms were trained to classify fraud and non-fraud on the basis of indicators such as profitability, stock turnover, stock cost, and shelf life.…”