Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as asset
management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the
abundance of available data and by affordable computing capacity. Machine learning (ML) models use big
data to learn and improve predictability and performance automatically through experience and data,
without being programmed to do so by humans.
The deployment of AI in finance is expected to increasingly drive competitive advantages for financial firms,
by improving their efficiency through cost reduction and productivity enhancement, as well as by enhancing
the quality of services and products offered to consumers. In turn, these competitive advantages can
benefit financial consumers by providing increased quality and personalised products, unlocking insights
from data to inform investment strategies and potentially enhancing financial inclusion by allowing for the
analysis of creditworthiness of clients with limited credit history (e.g. thin file SMEs).
At the same time, AI applications in finance may create or intensify financial and non-financial risks, and
give rise to potential financial consumer and investor protection considerations (e.g. as risks of biased,
unfair or discriminatory consumer results, or data management and usage concerns). The lack of
explainability of AI model processes could give rise to potential pro-cyclicality and systemic risk in the
markets, and could create possible incompatibilities with existing financial supervision and internal
governance frameworks, possibly challenging the technology-neutral approach to policymaking. While
many of the potential risks associated with AI in finance are not unique to this innovation, the use of such
techniques could amplify these vulnerabilities given the extent of complexity of the techniques employed,
their dynamic adaptability and their level of autonomy.
