# Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization

arXiv: https://arxiv.org/abs/1811.08212 · HTML: https://mlsanity.com/paper/1811.08212/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Label Augmentation via Time-based Knowledge Distillation for Financial Anomaly Detection](https://mlsanity.com/paper/2101.01689/) (2021)
- [Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications](https://mlsanity.com/paper/2312.16799/) (2023)

## Abstract (arXiv)

The automatic detection of frauds in banking transactions has been recently studied as a way to help the analysts finding fraudulent operations. Due to the availability of a human feedback, this task has been studied in the framework of active learning: the fraud predictor is allowed to sequentially call on an oracle. This human intervention is used to label new examples and improve the classification accuracy of the latter. Such a setting is not adapted in the case of fraud detection with financial data in European countries. Actually, as a human verification is mandatory to consider a fraud as really detected, it is not necessary to focus on improving the classifier. We introduce the setting of 'Computer-assisted fraud detection' where the goal is to minimize the number of non fraudulent operations submitted to an oracle. The existing methods are applied to this task and we show that a simple meta-algorithm provides competitive results in this scenario on benchmark datasets.