Automatic Procurement Fraud Detection with Machine Learning
This addresses procurement fraud detection for audit departments in companies like SF Express, but it is incremental as it applies existing machine learning methods to a new dataset.
The authors tackled procurement fraud detection by constructing neural network models using 9 features per procurement event, testing on 50,000 samples from SF Express's database, and found the models useful despite needing improvements.
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.