# ROSFD: Robust Online Streaming Fraud Detection with Resilience to Concept Drift in Data Streams

arXiv: https://arxiv.org/abs/2504.10229 · HTML: https://mlsanity.com/paper/2504.10229/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark](https://mlsanity.com/paper/1709.08920/) (2017)
- [Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization](https://mlsanity.com/paper/1804.07481/) (2018)
- [Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning](https://mlsanity.com/paper/2107.13508/) (2021)
- [Spade: A Real-Time Fraud Detection Framework on Evolving Graphs (Complete Version)](https://mlsanity.com/paper/2211.06977/) (2022)
- [Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection](https://mlsanity.com/paper/2211.10595/) (2022)
- [Feature Selection for Fault Detection and Prediction based on Event Log Analysis](https://mlsanity.com/paper/2208.09440/) (2022)
- [Transformer-Based Financial Fraud Detection with Cloud-Optimized Real-Time Streaming](https://mlsanity.com/paper/2501.19267/) (2025)
- [ATM Fraud Detection using Streaming Data Analytics](https://mlsanity.com/paper/2303.04946/) (2023)

## Abstract (arXiv)

Continuous generation of streaming data from diverse sources, such as online transactions and digital interactions, necessitates timely fraud detection. Traditional batch processing methods often struggle to capture the rapidly evolving patterns of fraudulent activities. This paper highlights the critical importance of processing streaming data for effective fraud detection. To address the inherent challenges of latency, scalability, and concept drift in streaming environments, we propose a robust online streaming fraud detection (ROSFD) framework. Our proposed framework comprises two key stages: (i) Stage One: Offline Model Initialization. In this initial stage, a model is built in offline settings using incremental learning principles to overcome the "cold-start" problem. (ii) Stage Two: Real-time Model Adaptation. In this dynamic stage, drift detection algorithms (viz.,, DDM, EDDM, and ADWIN) are employed to identify concept drift in the incoming data stream and incrementally train the model accordingly. This "train-only-when-required" strategy drastically reduces the number of retrains needed without significantly impacting the area under the receiver operating characteristic curve (AUC). Overall, ROSFD utilizing ADWIN as the drift detection method demonstrated the best performance among the employed methods. In terms of model efficacy, Adaptive Random Forest consistently outperformed other models, achieving the highest AUC in four out of five datasets.