# SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark

arXiv: https://arxiv.org/abs/1709.08920 · HTML: https://mlsanity.com/paper/1709.08920/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2017.

## Built on by (most cited first, 15 of 15)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives](https://mlsanity.com/paper/2307.01390/) (2023)
- [Improving Credit Card Fraud Detection through Transformer-Enhanced GAN Oversampling](https://mlsanity.com/paper/2509.19032/) (2025)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [ATM Fraud Detection using Streaming Data Analytics](https://mlsanity.com/paper/2303.04946/) (2023)
- [GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs](https://mlsanity.com/paper/2407.12440/) (2024)
- [GAN based Data Augmentation to Resolve Class Imbalance](https://mlsanity.com/paper/2206.05840/) (2022)
- [Learning under Commission and Omission Event Outliers](https://mlsanity.com/paper/2501.13599/) (2025)
- [FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection](https://mlsanity.com/paper/2502.02290/) (2025)
- [ROSFD: Robust Online Streaming Fraud Detection with Resilience to Concept Drift in Data Streams](https://mlsanity.com/paper/2504.10229/) (2025)
- [Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanations](https://mlsanity.com/paper/2601.12839/) (2026)
- [Multilingual Financial Fraud Detection Using Machine Learning and Transformer Models: A Bangla-English Study](https://mlsanity.com/paper/2603.11358/) (2026)
- [SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection](https://mlsanity.com/paper/2609.27287/) (2026)
- [Online AutoML: Evaluating Poisoning Attacks on Cost-Utility Adversarial Training Defence Strategy for IoT Networks](https://mlsanity.com/paper/2610.05810/) (2026)

## Abstract (arXiv)

The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field. In this paper we present a SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions.