Abstract
This chapter provides an overview of the recent literature on the diverse applications of artificial intelligence-based solutions to financial crime issues. This is the first survey of its kind, with a practitioner-oriented, multidisciplinary approach focusing broadly on financial crime-related solutions, not targeting a specific crime typology. In so doing, we are specifically targeting potential decision makers and relevant stakeholders both from industry and governmental agencies, such as risk or fraud analysts, compliance officers, law enforcement officers, managers in financial institutions, policymakers, and academics with neighboring interests, so that they can appraise the capabilities, drawbacks, and particular techniques favored by researchers when confronting issues specific to the context of financial crime. Following a chronological approach, current techniques, emerging applications, and developing trends are discussed, contextualizing artificial intelligence as a good resource yet to be employed to its full potential. Specific focus is given to the recent role of industry-academia partnerships in shaping future research and helping overcome the applicability gap that has emerged in pre-existing research.
| Original language | English |
|---|---|
| Title of host publication | Cybersecurity for Decision Makers |
| Editors | Narasimha Rao Vajjhala, Kenneth David Strang |
| Place of Publication | Boca Raton |
| Publisher | Taylor & Francis |
| Chapter | 12 |
| Pages | 199-213 |
| Number of pages | 15 |
| ISBN (Electronic) | 9781003319887 |
| ISBN (Print) | 9781032334967 |
| Publication status | Published - 19 Jul 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 16 Peace, Justice and Strong Institutions
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SDG 17 Partnerships for the Goals
Keywords
- Artificial intelligence
- machine learning
- financial crime
- crime detection
- fraud detection
- money laundering
- cryptocurrency
- risk management
- decision making
- neural networks
- graph analysis
- social network analysis
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