# Credit card fraud detection - Classifier selection strategy

arXiv: https://arxiv.org/abs/2208.11900 · HTML: https://mlsanity.com/paper/2208.11900/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [One-Class Adversarial Nets for Fraud Detection](https://mlsanity.com/paper/1803.01798/) (2018)
- [A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective](https://mlsanity.com/paper/1611.06439/) (2016)
- [Challenges and Complexities in Machine Learning based Credit Card Fraud Detection](https://mlsanity.com/paper/2208.10943/) (2022)

## Abstract (arXiv)

Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics over time is high. Additionally, fraud data has massive and varying imbalance. In this work, we evaluate sampling methods as a viable pre-processing mechanism to handle imbalance and propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets. The model derived based on our selection strategy surpasses peer models, whilst working in more realistic conditions, establishing the effectiveness of the strategy.