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

arXiv: https://arxiv.org/abs/2107.01979 · HTML: https://mlsanity.com/paper/2107.01979/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 102 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 30 of 30)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [Positive-Unlabeled Learning with Non-Negative Risk Estimator](https://mlsanity.com/paper/1703.00593/) (2017)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Online Learning under Delayed Feedback](https://mlsanity.com/paper/1306.0686/) (2013)
- [Toward Supervised Anomaly Detection](https://mlsanity.com/paper/1401.6424/) (2014)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [Poisoning Attacks on Algorithmic Fairness](https://mlsanity.com/paper/2004.07401/) (2020)
- [Exacerbating Algorithmic Bias through Fairness Attacks](https://mlsanity.com/paper/2012.08723/) (2020)
- [Technology Readiness Levels for AI & ML](https://mlsanity.com/paper/2006.12497/) (2020)
- [Adapting to Delays and Data in Adversarial Multi-Armed Bandits](https://mlsanity.com/paper/2010.06022/) (2020)
- [Epistemic Uncertainty Sampling](https://mlsanity.com/paper/1909.00218/) (2019)
- [FairOD: Fairness-aware Outlier Detection](https://mlsanity.com/paper/2012.03063/) (2020)
- [A Human-Grounded Evaluation of SHAP for Alert Processing](https://mlsanity.com/paper/1907.03324/) (2019)
- [Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization](https://mlsanity.com/paper/1804.07481/) (2018)
- [Uncovering Insurance Fraud Conspiracy with Network Learning](https://mlsanity.com/paper/2002.12789/) (2020)
- [Nested Multiple Instance Learning in Modelling of HTTP network traffic](https://mlsanity.com/paper/2002.04059/) (2020)
- [Search Improves Label for Active Learning](https://mlsanity.com/paper/1602.07265/) (2016)
- [Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach](https://mlsanity.com/paper/1811.02196/) (2018)

## Built on by (most cited first, 5 of 5)

- [TCE: A Test-Based Approach to Measuring Calibration Error](https://mlsanity.com/paper/2306.14343/) (2023)
- [Extending Open Bandit Pipeline to Simulate Industry Challenges](https://mlsanity.com/paper/2209.04147/) (2022)
- [Lightweight Automated Feature Monitoring for Data Streams](https://mlsanity.com/paper/2207.08640/) (2022)
- [Active learning with biased non-response to label requests](https://mlsanity.com/paper/2312.08150/) (2023)
- [Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com](https://mlsanity.com/paper/2405.13692/) (2024)

## 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.