Privacy Technologies for Financial Intelligence
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917274886078464 |
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| author | Li, Yang Ranbaduge, Thilina Ng, Kee Siong |
| author_facet | Li, Yang Ranbaduge, Thilina Ng, Kee Siong |
| contents | Financial crimes like money laundering and terrorism financing can have significant impacts on society, including loss of trust in the integrity of the financial system, misuse and mismanagement of public funds, increase in societal problems like drug trafficking and illicit gambling, and loss of innocent lives due to terrorism activities. Effective detection of complex financial crimes remains a formidable challenge for regulators and financial institutions because the critical data needed to establish patterns and criminality are often dispersed across multiple organisations and cannot be linked due to privacy constraints around large-scale data matching. Recent advances in privacy and confidential computing technologies, which enable private and secure data analysis across organisations, offer a promising opportunity for regulators and the financial industry to come together to enhance their collaborative risk detection while maintaining privacy standards. This paper, through a survey of the financial intelligence ecosystem, seeks to identify opportunities for the utilisation of privacy technologies to improve the state-of-the-art in financial-crime detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09935 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy Technologies for Financial Intelligence Li, Yang Ranbaduge, Thilina Ng, Kee Siong Cryptography and Security Financial crimes like money laundering and terrorism financing can have significant impacts on society, including loss of trust in the integrity of the financial system, misuse and mismanagement of public funds, increase in societal problems like drug trafficking and illicit gambling, and loss of innocent lives due to terrorism activities. Effective detection of complex financial crimes remains a formidable challenge for regulators and financial institutions because the critical data needed to establish patterns and criminality are often dispersed across multiple organisations and cannot be linked due to privacy constraints around large-scale data matching. Recent advances in privacy and confidential computing technologies, which enable private and secure data analysis across organisations, offer a promising opportunity for regulators and the financial industry to come together to enhance their collaborative risk detection while maintaining privacy standards. This paper, through a survey of the financial intelligence ecosystem, seeks to identify opportunities for the utilisation of privacy technologies to improve the state-of-the-art in financial-crime detection. |
| title | Privacy Technologies for Financial Intelligence |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2408.09935 |