Artificial intelligence (AI) is rapidly transforming the global financial services industry. The benefits of implementing an Artificial intelligence financial model in finance—for task automation, fraud detection, and delivering personalized recommendations—are monumental.
Artificial intelligence (AI) is increasingly deployed by financial modeling service providers across industries within the financial sector. It has the potential to transform financial models and markets for trading, credit, and blockchain-based finance, generate efficiencies, reduce friction and enhance product offerings. With this potential comes the concern that AI could also amplify risks already present in financial markets, or give rise to new challenges and risks.
This is becoming more of a preoccupation amidst the high growth of Artificial intelligence applications in finance. This chapter examines how policymakers can support responsible AI innovation in the financial sector while ensuring that investors and financial consumers are duly protected and that the markets around such products and services remain fair, orderly, and transparent.
The adoption of artificial intelligence systems and techniques in finance has grown substantially, enabled by the abundance of available data and the increase in the affordability of computing capacity.
The deployment of Artificial intelligence techniques in finance can generate efficiencies by reducing friction costs (e.g. commissions and fees related to transaction execution) and improving productivity levels, which in turn leads to higher profitability. In particular, the use of automation and technology-enabled cost reduction allows for capacity reallocation, spending effectiveness, and improved transparency in decision-making. AI applications for financial service providers can also enhance the quality of services and products offered to financial consumers, increase the tailoring and personalization of such products and diversify the product offering. The use of Artificial intelligence mechanisms can unlock insights from data to inform investment strategies, while it can also potentially enhance financial inclusion by allowing for the analysis of the creditworthiness of clients with limited credit history (e.g. thin file SMEs).
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