The benefits of using artificial intelligence in payment fraud detection: A case study

Cristina Soviany
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Cristina Soviany: Features Analytics, Belgium

Journal of Payments Strategy & Systems, 2018, vol. 12, issue 2, 102-110

Abstract: This paper presents a case study on the use of advanced artificial intelligence (AI) for the detection of payments fraud. The process applies AI within a typical online payment environment to detect fraudulent transactions in real time. The design focuses on an effective supervised learning engine with a data analytics component to support high-performance fraud detection, improving the predictive value of the original data. The design exploits the discriminant properties of customer data by finding hidden patterns. This feature significantly improves fraud detection rate and performance stability compared with a rule-based solution. The developed solution, based on an advanced AI-based technology and platform increased fraud detection rate from 85 per cent to 90 per cent (in terms of number of transaction records) and to 95 per cent in related amount volume (in terms of transaction value), while the alert rate (the percentage of daily transactions investigated manually) was reduced from 40 per cent to 10 per cent. The solution falls under the category of explainable AI because it can explain the rationale behind the decisions.

Keywords: artificial intelligence; machine learning; fraud management; real-time fraud detection; explainable AI (search for similar items in EconPapers)
JEL-codes: E5 G2 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:aza:jpss00:y:2018:v:12:i:2:p:102-110

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