# Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model

**Authors:** Giulia Moschini, Régis Houssou, Jérôme Bovay, Stephan Robert-Nicoud  
**arXiv:** [2009.07578](https://arxiv.org/abs/2009.07578)  
**Date:** 2020-09-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2009.07578v1/

## Key points

- Authors address challenge of unsupervised credit card fraud detection in unbalanced datasets
- ARIMA model employed for fraud detection in credit card transactions
- ARIMA model outperforms K-Means, Box-Plot, Local Outlier Factor, and Isolation Forest techniques
- Focus on individual spending behavior enhances fraud detection capabilities
- Research contributes insights to improve fraud detection mechanisms in financial systems
- ARIMA model through time series analysis effective for detecting anomalies and fraudulent activities

## Summary

In their paper titled "Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model," authors Giulia Moschini, Régis Houssou, Jérôme Bovay, and Stephan Robert-Nicoud address the challenge of unsupervised credit card fraud detection in unbalanced datasets by employing the ARIMA model. The study applies this model to credit card datasets and compares its performance against four other anomaly detection techniques: K-Means, Box-Plot, Local Outlier Factor, and Isolation Forest. The results of their analysis reveal that the ARIMA model exhibits superior detection capabilities compared to the benchmark models. By focusing on the inherent spending behavior of individuals, the ARIMA model proves to be effective in identifying fraudulent activities within credit card transactions. This research contributes valuable insights into enhancing fraud detection mechanisms in financial systems, particularly in scenarios characterized by imbalanced data distributions. Overall, the findings underscore the efficacy of utilizing time series analysis through the ARIMA model for detecting anomalies and fraudulent activities in credit card transactions.