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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.08593 |
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| _version_ | 1866909333370961920 |
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| author | Blanuša, Jovan Baraja, Maximo Cravero Anghel, Andreea von Niederhäusern, Luc Altman, Erik Pozidis, Haris Atasu, Kubilay |
| author_facet | Blanuša, Jovan Baraja, Maximo Cravero Anghel, Andreea von Niederhäusern, Luc Altman, Erik Pozidis, Haris Atasu, Kubilay |
| contents | In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering patterns in financial transaction graphs in real time. These patterns are used to produce a rich set of transaction features for downstream machine learning training and inference tasks such as detection of fraudulent financial transactions. We show that our enriched transaction features dramatically improve the prediction accuracy of gradient-boosting-based machine learning models. Our library exploits multicore parallelism, maintains a dynamic in-memory graph, and efficiently mines subgraph patterns in the incoming transaction stream, which enables it to be operated in a streaming manner. Our solution, which combines our Graph Feature Preprocessor and gradient-boosting-based machine learning models, can detect illicit transactions with higher minority-class F1 scores than standard graph neural networks in anti-money laundering and phishing datasets. In addition, the end-to-end throughput rate of our solution executed on a multicore CPU outperforms the graph neural network baselines executed on a powerful V100 GPU. Overall, the combination of high accuracy, a high throughput rate, and low latency of our solution demonstrates the practical value of our library in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08593 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection Blanuša, Jovan Baraja, Maximo Cravero Anghel, Andreea von Niederhäusern, Luc Altman, Erik Pozidis, Haris Atasu, Kubilay Machine Learning Artificial Intelligence In this paper, we present "Graph Feature Preprocessor", a software library for detecting typical money laundering patterns in financial transaction graphs in real time. These patterns are used to produce a rich set of transaction features for downstream machine learning training and inference tasks such as detection of fraudulent financial transactions. We show that our enriched transaction features dramatically improve the prediction accuracy of gradient-boosting-based machine learning models. Our library exploits multicore parallelism, maintains a dynamic in-memory graph, and efficiently mines subgraph patterns in the incoming transaction stream, which enables it to be operated in a streaming manner. Our solution, which combines our Graph Feature Preprocessor and gradient-boosting-based machine learning models, can detect illicit transactions with higher minority-class F1 scores than standard graph neural networks in anti-money laundering and phishing datasets. In addition, the end-to-end throughput rate of our solution executed on a multicore CPU outperforms the graph neural network baselines executed on a powerful V100 GPU. Overall, the combination of high accuracy, a high throughput rate, and low latency of our solution demonstrates the practical value of our library in real-world applications. |
| title | Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.08593 |