2023 Fintech and Financing Conference & Expo

OECD Report: AI in Business and Finance – 2021 Outlook

OECD Library | Oct 5, 2021

examples of AI in Finance - OECD Report:  AI in Business and Finance - 2021 Outlook

Source: OECD Library

Deployment of AI applications across the full spectrum of finance and business sectors has progressed rapidly in recent years such that these applications have become or are on their way to becoming mainstream. AI, i.e. machine-based systems able to make predictions, recommendations or decisions based on machine or human input for a given set of objectives, is being applied in digital platforms and in sectors ranging from health care to agriculture.

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It is also transforming financial services. In 2020 alone, financial markets witnessed a global spend of over USD 50 billion in AI, and a total investment in AI venture capital of over USD 4 billion worldwide, accompanied by a boom in the number of AI research publications and in the supply of AI job skills.

AI applications offer remarkable opportunities for businesses, investors, consumers and regulators. AI can facilitate transactions, enhance market efficiency, reinforce financial stability, promote greater financial inclusion and improve customer experience. Banks, traders, insurance firms and asset managers increasingly use AI to generate efficiencies by reducing friction costs and improving productivity levels. Increased automation and advances in “deep learning” can help financial service providers assess risk quickly and more accurately. Better forecasting of demand fluctuations through data analytics can help to avoid shortages and overproduction. Consumers also have increased access to financial services and support thanks to AI-powered online customer service tools like “chat-bots”, credit scoring, “robo-advice” and claims management.

As AI applications become increasingly integrated into business and finance, the use of trustworthy AI becomes more important for ensuring trustworthy financial markets. Increasing complexity of AI-powered applications in the financial sector, as well as the functions supported by AI technologies, pose risks to fairness, transparency, and the stability of financial markets that current regulatory frameworks may not adequately address. Appropriate and transparent designs and uses of AI-powered applications are essential to ensuring these risks are managed, including risks to consumer protection and trust, as well as AI’s ability to introduce systemic risk for the sector.

Explainability, transparency, accountability and robust data management practices are key to trustworthy AI in the financial sector. Explaining how AI algorithms reach decisions and other outcomes is an essential ingredient of fostering trust and accountability for AI applications. Outcomes of AI algorithms are often unexplainable, however, which presents a conundrum: the complexity of AI models that can hold the key to great advances in performance is also a crucial challenge for building trust and accountability.

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Transparency is another key determinant of trustworthy AI. Market participants should be able to know when AI is being used and how it is being developed and operated in order to promote accountability and help minimise the risks of unintended bias and discrimination in AI outcomes. Data quality and governance are also critical as the inappropriate use of data in AI-powered applications and the use of inadequate data can undermine trust in AI outcomes. Failing to foster these key qualities in AI systems could lead to the introduction of biases generating discriminatory and unfair results, market convergence and herding behaviour or the concentration of markets by dominant players, among other outcomes, which can all undermine market integrity and stability.

View the OECD AI in Business and Finance 2021 Report Online --> here

 


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