Abstract
Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders’ requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator’s judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.
Original language | English |
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Title of host publication | Proceedings of the 56th Hawaii International Conference on System Sciences |
Editors | Tung X. Bui |
Number of pages | 10 |
Place of Publication | Honolulu |
Publisher | Hawaii International Conference on System Sciences (HICSS) |
Publication date | 2023 |
Pages | 3474-3483 |
ISBN (Electronic) | 9780998133164 |
DOIs | |
Publication status | Published - 2023 |
Event | The 56th Hawaii International Conference on System Sciences. HICSS 2023 - Lahaina, United States Duration: 3 Jan 2023 → 6 Jan 2023 Conference number: 56 https://hicss.hawaii.edu/ |
Conference
Conference | The 56th Hawaii International Conference on System Sciences. HICSS 2023 |
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Number | 56 |
Country/Territory | United States |
City | Lahaina |
Period | 03/01/2023 → 06/01/2023 |
Internet address |
Series | Proceedings of the Annual Hawaii International Conference on System Sciences |
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ISSN | 1060-3425 |
Keywords
- High stakes decisions
- AML (Anti-Money Laundering)
- Decision-making
- Machine learning
- Explainable AI
- xAI
- Automation
- Augmentation