Artificial intelligence (AI) in finance

Artificial intelligence (AI) systems are machine-based systems with varying levels of autonomy that can,

for a given set of human-defined objectives, make predictions, recommendations or decisions. AI

techniques are increasingly using massive amounts of alternative data sources and data analytics referred

to as ‘big data’. Such data feed machine learning (ML) models which use such data to learn and

improve predictability and performance automatically through experience and data, without being

programmed to do so by humans.

The COVID-19 crisis has accelerated and intensified the digitalisation trend that was already

observed prior to the pandemic, including around the use of AI. Global spending on AI is forecast to

double over the period 2020-24, growing from USD50 bn in 2020 to more than USD110 bn in 2024 (IDC,

2020[1]). Growing AI adoption in finance, in areas such as asset management, algorithmic trading, credit

underwriting or blockchain-based financial services, is enabled by the abundance of available data and by

increased, and more affordable, computing capacity.

The deployment of AI in finance is expected to increasingly drive competitive advantages for

financial firms, through two main avenues: (a) by improving the firms’ efficiency through cost reduction

and productivity enhancement, therefore driving higher profitability (e.g. enhanced decision-making

processes, automated execution, gains from improvements in risk management and regulatory

compliance, back-office and other process optimisation); and (b) by enhancing the quality of financial

services and products offered to consumers (e.g. new product offering, high customisation of products and

services). Such competitive advantage can, in turn, benefit financial consumers, either through increased

quality of products, variety of options and personalisation, or by reducing their cost.

Why is the deployment of AI in finance relevant to policy makers

AI applications in finance may create or intensify financial and non-financial risks, and give rise to potential

financial consumer and investor protection considerations. The use of AI amplifies risks that could affect a

financial institution’s safety and soundness, given the lack of explainability or interpretability of AI model

processes, with potential for pro-cyclicality and systemic risk in the markets. The difficulty in understanding

how the model generates results could create possible incompatibilities with existing financial supervision

and internal governance frameworks, while it may even challenge the technology-neutral approach to

policymaking. AI may present particular risks of consumer protection, such as risks of biased, unfair or

discriminatory consumer results, or data management and usage concerns. While many of the potential

risks associated with AI in finance are not unique to AI, the use of AI could amplify such vulnerabilities

given the extent of complexity of the techniques employed, the dynamic adaptability of AI-based models

and their level of autonomy for the most advanced AI applications.

Figure 1. Relevant issues and risks stemming from the deployment of AI in finance

 


 
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