mlsanityData to 7 Oct 2026

Machine Learning for Fraud Detection in E-Commerce: A Research Agenda

arXiv 2107.01979 ↗First posted 5 Jul 2021cs.LG cs.CR stat.APcited by 5

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Abstract (arXiv)

Fraud detection and prevention play an important part in ensuring the sustained operation of any e-commerce business. Machine learning (ML) often plays an important role in these anti-fraud operations, but the organizational context in which these ML models operate cannot be ignored. In this paper, we take an organization-centric view on the topic of fraud detection by formulating an operational model of the anti-fraud departments in e-commerce organizations. We derive 6 research topics and 12 practical challenges for fraud detection from this operational model. We summarize the state of the literature for each research topic, discuss potential solutions to the practical challenges, and identify 22 open research challenges.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 30 of its 102 references that are in the map. First posted on arXiv 5 Jul 2021.

Builds on

30 of 102 references.

References · 30

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18 more
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 1.

5 papers.

  1. 2023TCE: A Test-Based Approach to Measuring Calibration Errorcited by 5
  2. 2022Extending Open Bandit Pipeline to Simulate Industry Challengescited by 2
  3. 2022Lightweight Automated Feature Monitoring for Data Streamscited by 1
  4. 2023Active learning with biased non-response to label requestscited by 0
  5. 2024Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.comcited by 0