# Fraud Dataset Benchmark and Applications

arXiv: https://arxiv.org/abs/2208.14417 · HTML: https://mlsanity.com/paper/2208.14417/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Self-Training: A Survey](https://mlsanity.com/paper/2202.12040/) (2022)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [AutoML @ NeurIPS 2018 challenge: Design and Results](https://mlsanity.com/paper/1903.05263/) (2019)

## Built on by (most cited first, 7 of 7)

- [Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data](https://mlsanity.com/paper/2505.22521/) (2025)
- [SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection](https://mlsanity.com/paper/2606.08146/) (2026)
- [Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding](https://mlsanity.com/paper/2507.09385/) (2025)
- [SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection](https://mlsanity.com/paper/2605.26135/) (2026)
- [FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment](https://mlsanity.com/paper/2608.24551/) (2026)
- [When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning](https://mlsanity.com/paper/2608.24631/) (2026)
- [FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences](https://mlsanity.com/paper/2609.11993/) (2026)

## Abstract (arXiv)

Standardized datasets and benchmarks have spurred innovations in computer vision, natural language processing, multi-modal and tabular settings. We note that, as compared to other well researched fields, fraud detection has unique challenges: high-class imbalance, diverse feature types, frequently changing fraud patterns, and adversarial nature of the problem. Due to these, the modeling approaches evaluated on datasets from other research fields may not work well for the fraud detection. In this paper, we introduce Fraud Dataset Benchmark (FDB), a compilation of publicly available datasets catered to fraud detection FDB comprises variety of fraud related tasks, ranging from identifying fraudulent card-not-present transactions, detecting bot attacks, classifying malicious URLs, estimating risk of loan default to content moderation. The Python based library for FDB provides a consistent API for data loading with standardized training and testing splits. We demonstrate several applications of FDB that are of broad interest for fraud detection, including feature engineering, comparison of supervised learning algorithms, label noise removal, class-imbalance treatment and semi-supervised learning. We hope that FDB provides a common playground for researchers and practitioners in the fraud detection domain to develop robust and customized machine learning techniques targeting various fraud use cases.