Enhancing Anti-Money Laundering Efforts with Network-Based Algorithms
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912010709499904 |
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| author | Bonato, Anthony Palan, Juan Sebastian Chavez Szava, Adam |
| author_facet | Bonato, Anthony Palan, Juan Sebastian Chavez Szava, Adam |
| contents | The global banking system has faced increasing challenges in combating money laundering, necessitating advanced methods for detecting suspicious transactions. Anti-money laundering (or AML) approaches have often relied on predefined thresholds and machine learning algorithms using flagged transaction data, which are limited by the availability and accuracy of existing datasets. In this paper, we introduce a novel algorithm that leverages network analysis to detect potential money laundering activities within large-scale transaction data. Utilizing an anonymized transactional dataset from Coöperatieve Rabobank U.A., our method combines community detection via the Louvain algorithm and small cycle detection to identify suspicious transaction patterns below the regulatory reporting thresholds. Our approach successfully identifies cycles of transactions that may indicate layering steps in money laundering, providing a valuable tool for financial institutions to enhance their AML efforts. The results suggest the efficacy of our algorithm in pinpointing potentially illicit activities that evade current detection methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00823 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Anti-Money Laundering Efforts with Network-Based Algorithms Bonato, Anthony Palan, Juan Sebastian Chavez Szava, Adam Social and Information Networks The global banking system has faced increasing challenges in combating money laundering, necessitating advanced methods for detecting suspicious transactions. Anti-money laundering (or AML) approaches have often relied on predefined thresholds and machine learning algorithms using flagged transaction data, which are limited by the availability and accuracy of existing datasets. In this paper, we introduce a novel algorithm that leverages network analysis to detect potential money laundering activities within large-scale transaction data. Utilizing an anonymized transactional dataset from Coöperatieve Rabobank U.A., our method combines community detection via the Louvain algorithm and small cycle detection to identify suspicious transaction patterns below the regulatory reporting thresholds. Our approach successfully identifies cycles of transactions that may indicate layering steps in money laundering, providing a valuable tool for financial institutions to enhance their AML efforts. The results suggest the efficacy of our algorithm in pinpointing potentially illicit activities that evade current detection methods. |
| title | Enhancing Anti-Money Laundering Efforts with Network-Based Algorithms |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2409.00823 |