Network Analysis of Global Banking Systems and Detection of Suspicious Transactions
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913730130870272 |
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| author | Bonato, Anthony Palan, Juan Chavez Szava, Adam |
| author_facet | Bonato, Anthony Palan, Juan Chavez Szava, Adam |
| contents | A novel network-based approach is introduced to analyze banking systems, focusing on two main themes: identifying influential nodes within global banking networks using Bank for International Settlements data and developing an algorithm to detect suspicious transactions for anti-money laundering. Leveraging the concept of adversarial networks, we examine Bank for International Settlements data to characterize low-key leaders and highly-exposed nodes in the context of financial contagion among countries. Low-key leaders are nodes with significant influence despite lower centrality, while highly-exposed nodes represent those most vulnerable to defaults. Separately, using anonymized transaction data from Rabobank, we design an anti-money laundering algorithm based on network partitioning via the Louvain method and cycle detection, identifying unreported transaction patterns indicative of potential money laundering. The findings provide insights into system-wide vulnerabilities and propose tools to address challenges in financial stability and regulatory compliance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network Analysis of Global Banking Systems and Detection of Suspicious Transactions Bonato, Anthony Palan, Juan Chavez Szava, Adam Social and Information Networks A novel network-based approach is introduced to analyze banking systems, focusing on two main themes: identifying influential nodes within global banking networks using Bank for International Settlements data and developing an algorithm to detect suspicious transactions for anti-money laundering. Leveraging the concept of adversarial networks, we examine Bank for International Settlements data to characterize low-key leaders and highly-exposed nodes in the context of financial contagion among countries. Low-key leaders are nodes with significant influence despite lower centrality, while highly-exposed nodes represent those most vulnerable to defaults. Separately, using anonymized transaction data from Rabobank, we design an anti-money laundering algorithm based on network partitioning via the Louvain method and cycle detection, identifying unreported transaction patterns indicative of potential money laundering. The findings provide insights into system-wide vulnerabilities and propose tools to address challenges in financial stability and regulatory compliance. |
| title | Network Analysis of Global Banking Systems and Detection of Suspicious Transactions |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2503.08456 |