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Looking Back Looking Forward (LBLF)

LBLF provides a concise and analytical discussion of issues relating to the intersection of finance, technology, regulation, globalization and sustainable development.

 

Led by Professor Douglas Arner, LBLF focuses on major current issues in global finance, presenting them in their wider context.

 

Topics include digitization and datafication of finance, major trends and themes in global finance and its regulation, and the role of finance in sustainable development.

 

LBLF goes out every month to over 100,000 learners on the HKU-edX Professional Certificate in FinTech.

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​AI, Data & the Future of Finance

Looking Back Looking Forward

with Professor Douglas Arner

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What happens when one of the world’s most digitized industries meets the exponential power of AI?

In this talk originally delivered at APEC's Finance and Central Bank Deputies Meeting in South Korea, Professor Douglas Arner of the University of Hong Kong unpacks the critical intersection of data, finance, and Artificial Intelligence. Drawing on years of research, he explores:

  • How finance became a pioneer in AI adoption;

  • The global clash of data governance models – US, EU, China;

  • The spectrum of AI risks: from macroeconomic disruption to malicious use;

  • Why open data, open finance, and enabling infrastructure are the way forward.

 

Related research: Building Open Finance: From Policy to Infrastructure available on: SSRN.

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AI in Finance: Disrupting the Industry
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AI in Finance: Disrupting the Industry

In this episode of Looking Back, Looking Forward, Professor Douglas Arner explores the impact of ChatGPT and generative AI on finance. He traces the historical adoption of AI in areas such as securities trading, payments, asset management, risk management, and compliance. Factors like increased computing power, data availability, and algorithm advancements have fueled the rapid development of AI in finance. He highlights the transformative nature of ChatGPT and its ability to interface with diverse data sources, including proprietary models and institutional data. He discusses macro and micro regulatory concerns, such as the impact on employment, discrimination, and accountability. He also highlights that existing regulatory frameworks and explainability systems address these concerns, ensuring transparency and responsibility. Data and human rights laws, like GDPR, also influence AI use in finance. Despite challenges, Professor Arner underscores the immense transformative potential of generative AI systems in finance as well as the maximum value people can get from data and analytics. Further Reading: Artificial Intelligence in Finance: Challenges, Opportunities and Regulatory Developments edited by Nydia Remolina and Aurelio Gurrea-Martinez - https://www.amazon.com/Artificial-Intelligence-Finance-Opportunities-Developments/dp/1803926163 Topic Guide: 0:00 Introduction 0:19 ChatGPT and Generative AI in Finance 1:20 Securities Trading: Automation and AI in Action 3:17 Payments and Fraud: Enhancing Security and Efficiency 4:15 Asset Management: The Rise of Robo Advisory Services 4:49 Risk Management, Compliance, and More: AI Applications in Finance 6:18 Factors Driving AI Development in Financial Services 7:39 The Transformative Power of ChatGPT in Finance 9:57 Macro and Micro Regulatory Considerations 11:14 The Role of Explainability Systems in Addressing Concerns
LBLF Season 3
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