# Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine

arXiv: https://arxiv.org/abs/2602.06955 · HTML: https://mlsanity.com/paper/2602.06955/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Hyperparameters in Reinforcement Learning and How To Tune Them](https://mlsanity.com/paper/2306.01324/) (2023)
- [Data Augmentation Using GANs](https://mlsanity.com/paper/1904.09135/) (2019)
- [Collaborative Evolutionary Reinforcement Learning](https://mlsanity.com/paper/1905.00976/) (2019)
- [Hyp-RL : Hyperparameter Optimization by Reinforcement Learning](https://mlsanity.com/paper/1906.11527/) (2019)
- [Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization](https://mlsanity.com/paper/1804.07481/) (2018)
- [Credit card fraud detection using machine learning: A survey](https://mlsanity.com/paper/2010.06479/) (2020)
- [Offline Reinforcement Learning with Imbalanced Datasets](https://mlsanity.com/paper/2307.02752/) (2023)
- [Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach](https://mlsanity.com/paper/2405.00026/) (2024)
- [Improving Detection of Credit Card Fraudulent Transactions using Generative Adversarial Networks](https://mlsanity.com/paper/1907.03355/) (2019)
- [Credit Card Fraud Detection Using Autoencoder Neural Network](https://mlsanity.com/paper/1908.11553/) (2019)
- [A Simple Bias Reduction for Chatterjee's Correlation](https://mlsanity.com/paper/2312.15496/) (2023)

## Abstract (arXiv)

Addressing class imbalance is a central challenge in credit card fraud detection, as it directly impacts predictive reliability in real-world financial systems. To overcome this, the study proposes an enhanced workflow based on the Explainable Boosting Machine (EBM)-a transparent, state-of-the-art implementation of the GA2M algorithm-optimized through systematic hyperparameter tuning, feature selection, and preprocessing refinement. Rather than relying on conventional sampling techniques that may introduce bias or cause information loss, the optimized EBM achieves an effective balance between accuracy and interpretability, enabling precise detection of fraudulent transactions while providing actionable insights into feature importance and interaction effects. Furthermore, the Taguchi method is employed to optimize both the sequence of data scalers and model hyperparameters, ensuring robust, reproducible, and systematically validated performance improvements. Experimental evaluation on benchmark credit card data yields an ROC-AUC of 0.983, surpassing prior EBM baselines (0.975) and outperforming Logistic Regression, Random Forest, XGBoost, and Decision Tree models. These results highlight the potential of interpretable machine learning and data-driven optimization for advancing trustworthy fraud analytics in financial systems.