# RIFF: Inducing Rules for Fraud Detection from Decision Trees

arXiv: https://arxiv.org/abs/2408.12989 · HTML: https://mlsanity.com/paper/2408.12989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints](https://mlsanity.com/paper/1311.2106/) (2013)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [Learning Accurate and Interpretable Decision Rule Sets from Neural Networks](https://mlsanity.com/paper/2103.02826/) (2021)
- [FairGBM: Gradient Boosting with Fairness Constraints](https://mlsanity.com/paper/2209.07850/) (2022)
- [Differentiable Rule Induction with Learned Relational Features](https://mlsanity.com/paper/2201.06515/) (2022)
- [ARMS: Automated rules management system for fraud detection](https://mlsanity.com/paper/2002.06075/) (2020)

## Built on by (most cited first, 1 of 1)

- [Graph-Based Fraud Detection with Dual-Path Graph Filtering](https://mlsanity.com/paper/2604.14235/) (2026)

## Abstract (arXiv)

Financial fraud is the cause of multi-billion dollar losses annually. Traditionally, fraud detection systems rely on rules due to their transparency and interpretability, key features in domains where decisions need to be explained. However, rule systems require significant input from domain experts to create and tune, an issue that rule induction algorithms attempt to mitigate by inferring rules directly from data. We explore the application of these algorithms to fraud detection, where rule systems are constrained to have a low false positive rate (FPR) or alert rate, by proposing RIFF, a rule induction algorithm that distills a low FPR rule set directly from decision trees. Our experiments show that the induced rules are often able to maintain or improve performance of the original models for low FPR tasks, while substantially reducing their complexity and outperforming rules hand-tuned by experts.