MONEY AND CAPITAL MARKETS AND INFORMATION TECHNOLOGY DEPARTMENTS
DEPARTMENTAL PAPERS
Powering the Digital Economy
Opportunities and Risks of Artificial Intelligence in Finance
Prepared by El Bachir Boukherouaa and Ghiath Shabsigh
in collaboration with
Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S. Iskender, Alin T. Mirestean, and Rangachary Ravikumar
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Copyright© 2021 International Monetary Fund
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Names: Boukherouaa, El Bachir. | Shabsigh, Ghiath. | AlAjmi, Khaled. | Deodoro, Jose. | Farias, Aquiles. | Iskender, Ebru S. | Mirestean, Alin. | Ravikumar, Rangachary. | International Monetary Fund, publisher.
Title: Powering the digital economy : opportunities and risks of artificial intelligence in finance / prepared by El Bachir Boukherouaa and Ghiath Shabsigh in collaboration with Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S. Iskender,
Alin T. Mirestean, and Rangachary Ravikumar.
Description: Washington, DC : International Monetary Fund, 2021. | 2021 October. | Departmental paper series. | Includes bibliographical references.
Identifiers: ISBN 9781589063952 (paper)
Subjects: LCSH: Artificial intelligence-Economic aspects. | Machine learning -Economic aspects. | Financial services industry-Technological innovations.
Classification: LCC HC79.I55 B68 2021
ISBN | 978-1-58906-395-2 (Paper) |
JEL Classification Numbers: | C40, C510, C550, E17, G21, G23, G280, O310, O330 |
Artificial Intelligence, Machine Learning, Financial Stability, | |
Keywords: | Embedded Bias, Financial Regulation, Cybersecurity, Risk |
Management, Data Privacy | |
GShabsigh@imf.org; EBoukherouaa@imf.org; KAlAjmi@imf.org; | |
Author's E-Mail Address: | JDeodoro@imf.org; AFarias@imf.org; ESonbulIskender@imf.org; |
AMirestean@imf.org; RRavikumar@imf.org |
The Departmental Paper Series presents research by IMF staff on issues of broad regional or cross-country interest.
The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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1 IMF DEPARTMENTAL PAPERS Powering the Digital Economy
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
AI and ML are technologies with the potential for enormous societal and economic impact, bringing new opportunities and benefits. Recent technological advances in computing and data storage power, big data, and the digital economy are facilitating rapid AI/ML deployment in a wide range of sectors, including finance. The COVID-19 crisis has accelerated the adoption of these systems due to the increased use of digital channels.
AI/ML systems are changing the financial sector landscape. Competitive pressures are fueling rapid adoption of AI/ML in the financial sector by facilitating gains in efficiency and cost savings, reshaping client interfaces, enhancing forecasting accuracy, and improving risk management and compliance. AI/ML systems also offer the potential to strengthen prudential oversight and to equip central banks with new tools to pursue their monetary and macroprudential mandates.
These advances, however, are creating new concerns arising from risks inherent in the technology and its application in the financial sector. Concerns include a number of issues, such as embedded bias in AI/ML systems, the opaqueness of their outcomes, and their robustness (particularly with respect to cyber threats and privacy). Furthermore, the technology is bringing new sources and transmission channels of systemic risks, including greater homogeneity in risk assessments and credit decisions and rising interconnectedness that could quickly amplify shocks.
AI/ML in finance should be broadly welcome, together with preparations to capture their benefits and mitigate potential risks to the financial system's integrity and safety. Preparations include strengthening the capacity and monitoring frameworks of oversight authorities, engaging stakeholders to identify possible risks and remedial regulatory actions, updating relevant legal and regulatory, and expanding consumer education. It is important that these actions are taken in the context of national AI strategies and involve all relevant public and private bodies.
Cooperation and knowledge sharing at the regional and international level is becoming increasingly important. This would allow for the coordination of actions to support the safe deployment of AI/ML systems and the sharing of experiences and knowledge. Cooperation will be particularly important to ensure that less-developed economies share the benefits.
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2 | IMF DEPARTMENTAL PAPERS Powering the Digital Economy | |
Contents | ||
1 | ||
Acronyms and Abbreviations ............................................................................................................................................. | ...4 | |
1. Introduction...................................................................................................................................................................... | ...5 | |
2. Artificial intelligence in the Financial Sector................................................................................................................. | ...7 | |
A. Forecasting.................................................................................................................................................................. | ....7 | |
B. Investment and Banking Services................................................................................................................................ | ....7 | |
C. Risk and Compliance Management............................................................................................................................. | ....9 | |
D. Prudential Supervision................................................................................................................................................. | ....9 | |
E. Central Banking ........................................................................................................................................................... | ..12 | |
3. Risks and Policy Considerations ................................................................................................................................... | .14 | |
A. Embedded Bias ........................................................................................................................................................... | ..14 | |
B. Unboxing the "Black Box": Explainability and Complexity............................................................................................ | ..15 | |
C. Cybersecurity............................................................................................................................................................... | ..16 | |
D. Data Privacy ................................................................................................................................................................ | ..17 | |
E. Robustness.................................................................................................................................................................. | ..17 | |
F. Impact on Financial Stability ........................................................................................................................................ | ..18 | |
4. Conclusion ....................................................................................................................................................................... | ..20 | |
Annexes | ||
Annex 1. How Machine Learning Algorithms Work.................................................................................................. | ..21 | |
Annex 2. Artificial Intelligence in Finance-Risk Profile........................................................................................... | ..24 | |
Annex 3. National Artificial Intelligence Strategies................................................................................................... | ..25 | |
References .......................................................................................................................................................................... | .28 | |
BOXES | ||
Box 1. Artificial Intelligence and Machine Learning Capabilities.............................................................................. | ....6 | |
Box 2. Artificial Intelligence in Investment Management-Sample Use Cases........................................................ | ....8 | |
Box 3. Artificial Intelligence in Credit Underwriting .................................................................................................. | ....8 | |
Box 4. Artificial Intelligence in Regulatory Compliance-Sample Use Cases.......................................................... | ..10 | |
Box 5. Artificial Intelligence in Supervision-Sample Applications .......................................................................... | ..11 | |
Box 6. Artificial Intelligence in Central Banking-Sample Applications.................................................................... | ..13 | |
Box 7. Explaining the "Black Box" ........................................................................................................................... | ..16 | |
FIGURES | ||
Figure 1. Top Five Technologies Employed in Regulatory Technology Offerings ................................................... | ....9 | |
Figure 2. Technologies Used in Suprvisory Technology Tools ................................................................................ | ..10 | |
Annex Figure 1.1. Machine Learning Paradigms..................................................................................................... | ..22 |
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