# On the Potential of Network-Based Features for Fraud Detection

arXiv: https://arxiv.org/abs/2402.09495 · HTML: https://mlsanity.com/paper/2402.09495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index](https://mlsanity.com/paper/2302.00775/) (2023)
- [Credit card fraud detection through parenclitic network analysis](https://mlsanity.com/paper/1706.01953/) (2017)

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

- [Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations](https://mlsanity.com/paper/2507.01980/) (2025)

## Abstract (arXiv)

Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses. Conventional rule-based systems struggle to keep pace with evolving fraud tactics, leading to high false positive rates and missed detections. Machine learning techniques offer a promising solution by leveraging historical data to identify fraudulent patterns. This article explores using the personalised PageRank (PPR) algorithm to capture the social dynamics of fraud by analysing relationships between financial accounts. The primary objective is to compare the performance of traditional features with the addition of PPR in fraud detection models. Results indicate that integrating PPR enhances the model's predictive power, surpassing the baseline model. Additionally, the PPR feature provides unique and valuable information, evidenced by its high feature importance score. Feature stability analysis confirms consistent feature distributions across training and test datasets.