# Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization

arXiv: https://arxiv.org/abs/1804.07481 · HTML: https://mlsanity.com/paper/1804.07481/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024)
- [Machine Learning for Fraud Detection in E-Commerce: A Research Agenda](https://mlsanity.com/paper/2107.01979/) (2021)
- [Active learning for imbalanced data under cold start](https://mlsanity.com/paper/2107.07724/) (2021)
- [Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails](https://mlsanity.com/paper/2303.00870/) (2023)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies](https://mlsanity.com/paper/2405.07440/) (2024)
- [The Fundamental Limits of Fraud Detection in Card Payment Networks](https://mlsanity.com/paper/2605.27557/) (2026)
- [Robust Principal Component Analysis using Density Power Divergence](https://mlsanity.com/paper/2309.13531/) (2023)
- [Active learning with biased non-response to label requests](https://mlsanity.com/paper/2312.08150/) (2023)
- [Stream-Based Active Learning for Process Monitoring](https://mlsanity.com/paper/2411.12563/) (2024)
- [ROSFD: Robust Online Streaming Fraud Detection with Resilience to Concept Drift in Data Streams](https://mlsanity.com/paper/2504.10229/) (2025)
- [Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine](https://mlsanity.com/paper/2602.06955/) (2026)
- [When Can Fraud Operations Authorize Automation? A Decision-Support Framework for Fresh Audit Evidence and Review Workload](https://mlsanity.com/paper/2608.08577/) (2026)

## Abstract (arXiv)

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature.