The Effectiveness of Machine Learning Based Anomaly Detection Algorithms Applied to Defense Contract Financial Data
Abstract
In fiscal year 2020, the U.S. Army spent nearly $77 billion on contracts. Auditors employ various techniques, including anomaly detection, to select contracts that merit scrutiny. But in a resource-constrained environment, auditors can review only a limited number of contracts. Using data obtained from USAspending.gov, we consider how anomaly detection combined with dimensionality reduction can be used to recommend contracts for investigation. We analyze over 20,000 fixed-price Army contracts between fiscal years 2017 to 2020, using more than one hundred combinations of dimensionality reduction and anomaly detection techniques, and formations of artificial anomalies. A consistent finding is that dimensionality reduction using principal components or autoencoders is not demonstrably beneficial. This finding may be due to the discrete nature of the USAspending.gov data and may not apply to other data sets. The best performance is obtained using isolation forests for anomaly detection without dimensionality reduction.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2020
- Accession Number
- AD1126877
Entities
People
- Keith D. Edmonds
Organizations
- Naval Postgraduate School