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Explainable anomaly detection for procurement fraud identification—lessons from practical deployments
Author(s) -
Westerski Adam,
Kanagasabai Rajaraman,
Shaham Eran,
Narayanan Amudha,
Wong Jiayu,
Singh Manjeet
Publication year - 2021
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12968
Subject(s) - procurement , anomaly detection , computer science , audit , leverage (statistics) , officer , database transaction , work (physics) , construct (python library) , identification (biology) , computer security , process management , data science , operations research , risk analysis (engineering) , business , data mining , accounting , artificial intelligence , marketing , database , mechanical engineering , biology , political science , law , programming language , engineering , botany
This article reports the results of our work to construct a system for the detection of fraudulent behavior in procurement transactions. To solve the problem, we model different types of fraud via separate statistical indicators. We propose a formalized framework to describe the severity of fraud in a unified way regardless of underlying fraud mechanics. Subsequently, we leverage this concept to build indicator ensembles that collect evidence from multiple indicators and deliver an interpretable per transaction score to the procurement audit officer. As a case study, we overview 48 such fraud indicators constructed for our client and describe two examples in detail showing how our formal definitions can be transformed into a practical implementation. The presented results include experiments with all indicators on data covering four years of procurement activity with approximately 216,000 transactions coming from a large government organization in Singapore. The final evaluation of our system shows 67.1% precision in detecting suspicious transactions. The article describes how outcome of our work helped to effectively cope with the problem of anomaly detection explainability and the lessons learned from integrating this solution to operational practices of a procurement department.