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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.09127 |
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| _version_ | 1866912581896110080 |
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| author | Namdar, Khashayar Wang, Pin-Chien Raju, Tushar Zheng, Steven Li, Fiona Khan, Safwat Tahmin |
| author_facet | Namdar, Khashayar Wang, Pin-Chien Raju, Tushar Zheng, Steven Li, Fiona Khan, Safwat Tahmin |
| contents | Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this paper, we propose a comprehensive and systematic approach for developing ML pipelines to identify high-risk bank clients in a dataset curated for Task 1 of the University of Toronto 2023-2024 Institute for Management and Innovation (IMI) Big Data and Artificial Intelligence Competition. The dataset included 195,789 customer IDs, and we employed a 16-step design and statistical analysis to ensure the final pipeline was robust. We also framed the data in a SQLite database, developed SQL-based feature engineering algorithms, connected our pre-trained model to the database, and made it inference-ready, and provided explainable artificial intelligence (XAI) modules to derive feature importance. Our pipeline achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.961 with a standard deviation (SD) of 0.005. The proposed pipeline achieved second place in the competition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning Namdar, Khashayar Wang, Pin-Chien Raju, Tushar Zheng, Steven Li, Fiona Khan, Safwat Tahmin Artificial Intelligence Anti-money laundering (AML) actions and measurements are among the priorities of financial institutions, for which machine learning (ML) has shown to have a high potential. In this paper, we propose a comprehensive and systematic approach for developing ML pipelines to identify high-risk bank clients in a dataset curated for Task 1 of the University of Toronto 2023-2024 Institute for Management and Innovation (IMI) Big Data and Artificial Intelligence Competition. The dataset included 195,789 customer IDs, and we employed a 16-step design and statistical analysis to ensure the final pipeline was robust. We also framed the data in a SQLite database, developed SQL-based feature engineering algorithms, connected our pre-trained model to the database, and made it inference-ready, and provided explainable artificial intelligence (XAI) modules to derive feature importance. Our pipeline achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.961 with a standard deviation (SD) of 0.005. The proposed pipeline achieved second place in the competition. |
| title | Anti-Money Laundering Machine Learning Pipelines; A Technical Analysis on Identifying High-risk Bank Clients with Supervised Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.09127 |