Is generative AI (artificial intelligence) the next advent in the evolution of finance and navigating financial crime and regulation?
Charanjit Singh
Abstract
Purpose Generative artificial intelligence (Gen AI) is changing the trajectories of Banking (FinTech) and Law (RegTech/LawTech). The rate at which the technology is innovating is astounding. The ability of AI and Gen AI systems to simulate human intelligence (human thinking) and independently perform tasks and develop intelligence that is premised on its own experiences, process layers of information and continually learn and re-learn increasingly complex representations of data has resulted in improvements in “it” being able to perform complex, technical and time-consuming tasks; identify objects, people, voices and patterns; and screen for “problems” much earlier and provide solutions. This has economic, political and social benefits. The purpose of this study is to explore how Gen AI is changing the face of finance and its impact on the risks, regulatory and operational challenges faced by financial institutions in the UK. Design/methodology/approach The subject is explored through the analysis of data and domestic and international published literature. The first part of this study summarises the context of current risks and regulatory and operational issues; the discussion then moves on to explore Gen AI and how it can be embedded as part of the arsenal that financial institutions can use/are using to innovate and provide solutions to the regulatory and operational challenges they face as of August 2024. Findings It is suggested that UK financial institutions can further use Gen AI as part of their armoury of solutions to respond to the risk of financial crime and tackle the regulatory burden to achieve high levels of operational efficiency as well as promoting better customer satisfaction. Originality The work is original because, to the best of the author’s knowledge, it is the first to specifically explore how Gen AI is assisting UK financial institutions to find solutions to financial crime risk and regulatory challenges, customer satisfaction, cyberattacks and cybercrime.
7 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.47 × 0.4 = 0.19 |
| M · momentum | 0.68 × 0.15 = 0.10 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.