Automatic Procurement Fraud Detection with Machine Learning

TL;DR

This paper explores the use of neural network models trained on procurement data to automatically detect and classify procurement frauds, aiming to assist audit departments in identifying suspicious activities more efficiently.

Contribution

The study introduces a neural network approach utilizing 9 features per procurement event to detect and classify procurement frauds in a large real-world dataset.

Findings

Models achieved effective fraud detection on 50,000 samples.

Neural networks showed promise despite room for improvement.

Automated detection can supplement traditional audit methods.

Abstract

Although procurement fraud is always a critical problem in almost every free market, audit departments still have a strong reliance on reporting from informed sources when detecting them. With our generous cooperator, SF Express, sharing the access to the database related with procurements took place from 2015 to 2017 in their company, our team studies how machine learning techniques could help with the audition of one of the most profound crime among current chinese market, namely procurement frauds. By representing each procurement event as 9 specific features, we construct neural network models to identify suspicious procurements and classify their fraud types. Through testing our models over 50000 samples collected from the procurement database, we have proven that such models -- despite having space for improvements -- are useful in detecting procurement frauds.