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An augmented AI-based hybrid fraud detection framework for invoicing platforms

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Abstract

In this era of e-commerce, many companies are moving towards subscription-based invoicing platforms to maintain their electronic invoices. Unfortunately, fraudsters are using these platforms for different types of malicious activities. Identifying fraudsters is often challenging for many companies due to the limitation of time and other resources. A fully automated fraud detection model can be useful, but it creates a risk of false-positive identification. This paper proposed a hybrid fraud detection framework when only a small set of labelled (fraud/non-fraud) data is available, and human input is required in the final decision-making step. This framework used a combination of unsupervised and supervised machine learning, red-flag prioritization, and an augmented AI approach containing a human-in-the-loop process. It also proposed a weighted center based on the feature importance scores for the fraud risk cluster and used it in the red-flag prioritization process. Finally, the approach is illustrated using a case study to identify fraudulent users in an invoicing platform. Our hybrid framework showed promising results in identifying fraudulent users and improving human performance when human input is required to make the final decision.

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Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

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Funding

We acknowledge support from the Natural Sciences and Engineering Research Council (NSERC) Discovery (Award Number: RGPIN-2020-06792) and Mitacs Accelerate Fellowship Program (Award Number: IT16025) programs for their support of this project.

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Authors and Affiliations

  1. School of Computational Science & Engineering, McMaster University, Hamilton, Canada

    Dewan F. Wahid & Elkafi Hassini

  2. DeGroote School of Business, McMaster University, Hamilton, Canada

    Elkafi Hassini

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  1. Dewan F. Wahid
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Correspondence to Dewan F. Wahid.

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Wahid, D.F., Hassini, E. An augmented AI-based hybrid fraud detection framework for invoicing platforms. Appl Intell 54, 1297–1310 (2024). https://doi.org/10.1007/s10489-023-05223-x

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