Robust System for Identifying Procurement Fraud
| Autor: | Amit Dhurandhar, Rajesh Ravi, Bruce Graves, Gopi Maniachari, Markus Ettl |
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| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 29:3896-3903 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v29i2.19045 |
| Popis: | An accredited biennial 2012 study by the Association of Certified Fraud Examiners claims that on average 5% of a company’s revenue is lost because of unchecked fraud every year. The reason for such heavy losses are that it takes around 18 months for a fraud to be caught and audits catch only 3% of the actual fraud. This begs the need for better tools and processes to be able to quickly and cheaply identify potential malefactors. In this paper, we describe a robust tool to identify procurement related fraud/risk, though the general design and the analytical components could be adapted to detecting fraud in other domains. Besides analyzing standard transactional data, our solution analyzes multiple public and private data sources leading to wider coverage of fraud types than what generally exists in the marketplace. Moreover, our approach is more principled in the sense that the learning component, which is based on investigation feedback has formal guarantees. Though such a tool is ever evolving, an initial deployment of this tool over the past 6 months has found many interesting cases from compliance risk and fraud point of view, increasing the number of true positives found by over 80% compared with other state-of-the-art tools that the domain experts were previously using. |
| Databáze: | OpenAIRE |
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