Summary of Financial Fraud Detection Using Jump-attentive Graph Neural Networks, by Prashank Kadam
Financial Fraud Detection using Jump-Attentive Graph Neural Networks
by Prashank Kadam
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in online financial services has led to an increase in fraudulent activities, prompting the development of more sophisticated machine learning models for fraud detection. Traditional rule-based methods have become ineffective due to their reliance on manual rule creation and inability to capture complex data patterns. To address this issue, various machine learning algorithms such as XGBoost, Random Forest, and neural networks have been employed to model transaction data. However, these techniques still struggle to detect interactions between transactions and their interrelationships. Graph-based techniques have shown promise in financial fraud detection, leveraging graph topology to aggregate neighborhood information using Graph Neural Networks (GNNs). Despite improvements over previous methods, these techniques suffer from information loss due to over-smoothing. To address this issue, a novel algorithm is proposed that employs an efficient neighborhood sampling method for camouflage detection and preserving crucial feature information from non-similar nodes. Additionally, a novel GNN architecture is introduced that utilizes attention mechanisms and preserves holistic neighborhood information to prevent information loss. The proposed algorithm outperforms other state-of-the-art graph algorithms on financial data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fraudulent activities in online financial services have become a major concern. To detect these frauds, new machine learning models are being developed. In the past, simple rules were used to spot suspicious transactions, but this approach became outdated as fraudsters found new ways to trick the system. Today, more advanced techniques like XGBoost and neural networks are used to analyze transaction data. However, even these methods have their limitations, as they struggle to understand how different transactions relate to each other. A newer approach uses graph-based techniques to study these relationships, but this method also has its drawbacks. Researchers have now proposed a new algorithm that can detect camouflage tactics and preserve important information about transactions. |
Keywords
» Artificial intelligence » Attention » Gnn » Machine learning » Prompting » Random forest » Xgboost
