This is the second Expanding Frontiers episode devoted to quantum computing for finance. It  explores the current (2024) state and future potential of Quantum Machine Learning (QML), specifically focusing on its applications within the financial services industry. We discuss various QML algorithms including Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks, and also touch upon quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks. The paper discussed identifies key financial applications for QML, such as risk management, credit scoring, fraud detection, and stock price prediction, while also outlining the promises and limitations of integrating QML into real-world financial operations. The review aims to serve as a practical guide for financial professionals and data scientists interested in understanding QML's relevance to their field.

References

 A Brief Review of Quantum Machine Learning for Financial Services (July 2024)

Mina Doosti, Petros Wallden, Conor Brian Hamill, Robert Hankache, Oliver Thomson Brown, Chris Heunen

Resources

Medium article: Are You Ready to Learn About Quantum Computing?

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

This episode provides an overview of multimodal Financial Foundation Models (MFFMs), exploring their progress, potential applications, and associated challenges. It emphasizes the ubiquitous nature of multimodal financial data—including text, audio, images, and tabular information—in various financial applications like search, robo-advising, and trading. The paper review also addresses the development lifecycle of MFFMs, from pre-training to fine-tuning and alignment, while highlighting the need for robust benchmarks. Crucially, it discusses significant challenges such as data privacy, the risk of misinformation and hallucination, and the need for ethical AI readiness and governance within the financial sector.

References

Liu, Xiao-Yang and Cao, Yupeng and Deng, Li, Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges (May 31, 2025). Available at SSRN: https://ssrn.com/abstract=5277657 or http://dx.doi.org/10.2139/ssrn.5277657

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the reference listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

In this episode, we explore how smart finance—like social impact bonds and pay-for-success models—can help end homelessness. I share insights from my work with the Coalition to End Homelessness in South Florida and recent policy shifts nationwide, including the controversial Grants Pass ruling. We’ll look at data, dignity, and the human stories behind the numbers. This is about funding outcomes, not overhead.

Reference

Wilkens, K. (May 29, 2025.), Funding the Future: How Smart Finance Can End Homelessness 

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

This episode reviews the book, Quantum Computing for Finance by Oswaldo Zapata, outlining the foundational concepts of classical and quantum computing, including topics like Boolean logic, qubits, quantum gates, and error correction. It explores how quantum computing can potentially enhance classical financial methods such as portfolio optimization, Monte Carlo simulations, and machine learning algorithms used in areas like credit risk assessment and fraud detection. The discussion includes how the book surveys the current quantum computing landscape, detailing different hardware technologies, key companies and startups, and offering advice on how financial institutions can prepare for the integration of quantum technology, emphasizing the importance of talent development and hybrid approaches.

References

Zapata, O. Quantum Computing for Finance

The book is available at:

https://www.scribd.com/document/860542791/Quantum-Computing-for-Finance-Oswaldo-Zapata-PhD

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

In this episode we discuss the increasing integration of artificial intelligence (AI) and machine learning (ML) into asset management, focusing on their application in portfolio management, risk assessment, and trading strategies. AI, particularly ML, allows models to process vast, complex datasets and identify patterns beyond traditional methods, promising enhanced efficiency and predictive accuracy. However, these technologies introduce new challenges, including data quality issues, the risk of overfitting, and the potential for bias in models, necessitating robust governance frameworks and regulatory oversight. One source specifically examines the use of transformer models, similar to those in large language models, to improve asset pricing by enabling sophisticated cross-asset information sharing.

References

Chakrabarti, Fabozzi, Narain, and Sood (2025) Ethical AI in Asset Management: Frameworks for Transparency, Compliance and Trust, Journal of Financial Data Science, Winter 2025, pp. 18–35. https://www.DOI.org/10.3905/jfds.2025.7.1.018

Kelly, Bryan T. and Kuznetsov, Boris and Malamud, Semyon and Xu, Teng Andrea, Artificial Intelligence Asset Pricing Models (January 2025). NBER Working Paper No. w33351, Available at SSRN: https://ssrn.com/abstract=5103546

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

This podcast episode discusses state and local AI regulations impact investor risk, finding that such laws can decrease risk by incentivizing firms to adopt better AI governance and reduce misconduct. It also examines how financial analysts utilize AI and its impact on their behavior. Finally, we discuss a third academic research paper on AI in risk management and forecasting from a global perspective. It is based on these three references:

Ciconte, Will and Rozario, Andrea and Urcan, Oktay, Artificial Intelligence Regulation and Investor Risk: Evidence from State and Local Artificial Intelligence Mandates (March 19, 2025). Available at SSRN: https://ssrn.com/abstract=5023685 or http://dx.doi.org/10.2139/ssrn.5023685

Shanthikumar, Devin M. and Yoo, Il Sun, Artificial Intelligence and Analyst Productivity (November 30, 2024). Available at SSRN: https://ssrn.com/abstract=5040339 or http://dx.doi.org/10.2139/ssrn.5040339

Vyas, Anshul, Revolutionizing Risk: The Role of Artificial Intelligence in Financial Risk Management, Forecasting, and Global Implementation (April 21, 2025). Available at SSRN: https://ssrn.com/abstract=5224657 or http://dx.doi.org/10.2139/ssrn.5224657

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on the references listed above and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

This episode explores the evolving landscape of digital assets and their implications for institutional investors, highlighting the necessary learning process, expert engagement, and meticulous due diligence required due to unique risks and opportunities. It examines various valuation techniques for these intangible assets, acknowledging their speculative nature and rapid changes. A significant portion discusses tokenization, detailing its potential to enhance liquidity and access in various alternative investments like hedge funds, private equity, and real estate, while also outlining associated risks and constraints. Finally, the text touches upon the concept of financial democratization through fintech and digital assets, presenting both utopian possibilities and dystopian warnings, and emphasizing the need to consider underlying power dynamics beyond mere access to financial services. Let me know if you are interested in any of the references mentioned.

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on an appendix in my book, Alternative Investments: Expanding Frontiers (soon to be released on LeanPub.com), and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

This episode provides a comprehensive overview of private credit and distressed debt markets. It outlines different types of private credit, including leveraged loans, direct lending, mezzanine debt, and distressed debt, detailing their characteristics and the vehicles used for investment. The text explores fixed income analysis, emphasizing the distinctions between bonds and loans regarding liquidity, risk, and interest rates, and explains the significance of credit risk assessment and the bankruptcy process in these markets. Furthermore, it examines capital structure, recovery rates, and various investment strategies within the distressed debt sector. We also discuss the implications of regulatory changes and the growth of alternative lending sources following the 2008 financial crisis. 

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on a chapter from my book, Alternative Investments: Expanding Frontiers (soon to be released on LeanPub.com), and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

By listening, you acknowledge that the information provided is subject to change and should not be solely relied upon for decision-making.

This episode introduces structured products, explaining their creation from existing assets to meet specific investor needs regarding risk, tax, and liquidity. It covers various types, including equity-linked products, and the wrappers used to legally present them, detailing their potential tax effects. We discuss mortgage-backed securities (MBS), including residential (RMBS) and commercial (CMBS), along with the critical aspect of prepayment risk. We then examine more complex structured products like collateralized mortgage obligations (CMOs) and collateralized debt obligations (CDOs), outlining their tranching and risk distribution, and relating mezzanine tranches to option collars and spreads. Finally, we addresses valuation techniques, the use of payoff diagrams, investor motivations for using these products, and the recent availability of liquid structured products in ETF form.

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on a chapter from my book, Alternative Investments: Expanding Frontiers (soon to be released on LeanPub.com), and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

By listening, you acknowledge that the information provided is subject to change and should not be solely relied upon for decision-making.

This episode provides a comprehensive overview of digital assets, beginning with the foundational distributed ledger technology (DLT) and its financial applications like asset tokenization and decentralized finance (DeFi). It explores various consensus mechanisms, including Proof of Work and Proof of Stake, and categorizes DLTs by governance and decentralization. We outline different types of digital assets, such as cryptocurrencies, tokens (including NFTs, security, and utility tokens), and stablecoins, alongside their investment characteristics like volatility and potential returns. Furthermore, we detail the forms and vehicles for investing in digital assets and analyze their unique risks, returns, and diversification benefits compared to traditional asset classes. Ultimately, the discussion serves as an introduction to the landscape of digital assets, highlighting their technological underpinnings, financial uses, investment considerations, and potential within the broader financial ecosystem.

Podcast Disclaimer

This podcast is an independent production and is not affiliated with or endorsed by any third-party entities unless explicitly stated. The content is for educational and informational purposes only and does not constitute financial, investment, legal, or professional advice. Listeners should consult qualified professionals before making any decisions based on this content.

This episode is based on a chapter from my book, Alternative Investments: Expanding Frontiers (soon to be released on LeanPub.com), and was generated using Notebook LM and other AI tools. While I have reviewed the content for accuracy, it may still contain errors, inaccuracies, or omissions. Neither the producers nor any affiliates accept liability for any damages or losses arising from the use or interpretation of this content.

By listening, you acknowledge that the information provided is subject to change and should not be solely relied upon for decision-making.