Lucas Giovanni Uberti-Bona Marin (Maastricht U Law) et al. have posted “Are Companies Taking AI Risks Seriously? A Systematic Analysis of Companies’ AI Risk Disclosures in SEC 10-K Forms” on SSRN. Here is the abstract:

As Artificial Intelligence becomes increasingly central to corporate strategies, concerns over its risks are growing too. In response, regulators are pushing for greater transparency in how companies identify, report and mitigate AI-related risks. In the US, the Securities and Exchange Commission (SEC) repeatedly warned companies to provide their investors with more accurate disclosures of AI-related risks; recent enforcement and litigation against companies’ misleading AI claims reinforce these warnings. In the EU, new laws-like the AI Act and Digital Services Act-introduced additional rules on AI risk reporting and mitigation. Given these developments, it is essential to examine if and how companies report AI-related risks to the public. This study presents the first large-scale systematic analysis of AI risk disclosures in SEC 10-K filings, which require public companies to report material risks to their company. We analyse over 30,000 filings from more than 7,000 companies over the past five years, combining quantitative and qualitative analysis. Our findings reveal a sharp increase in the companies that mention AI risk, up from 4% in 2020 to over 43% in the most recent 2024 filings. While legal and competitive AI risks are the most frequently mentioned, we also find growing attention to societal AI risks, such as cyberattacks, fraud, and technical limitations of AI systems. However, many disclosures remain generic or lack details on mitigation strategies, echoing concerns raised recently by the SEC about the quality of AI-related risk reporting. To support future research, we publicly release a web-based tool for easily extracting and analysing keyword-based disclosures across SEC filings.

Roxana Mihet (Swiss Finance Institute HEC Lausanne) et al. have posted “Is It AI or Data That Drives Market Power?” on SSRN. Here is the abstract:

Artificial intelligence (AI) is transforming productivity and market structure, yet the roots of firm dominance in the modern economy remain unclear. Is market power driven by AI capabilities, access to data, or the interaction between them? We develop a dynamic model in which firms learn from data using AI, but face informational entropy: without sufficient AI, raw data has diminishing or even negative returns. The model predicts two key dynamics: (1) improvements in AI disproportionately benefit data-rich firms, reinforcing concentration; and (2) access to processed data substitutes for compute, allowing low-AI firms to compete and reducing concentration. We test these predictions using novel data from 2000–2023 and two exogenous shocks—the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. The results confirm both mechanisms: compute access enhances the advantage of data-intensive firms, while access to processed data closes the performance gap between AI leaders and laggards. Our findings suggest that regulating data usability—not just AI models—is essential to preserving competition in the modern economy.

David Krause (Marquette U) has posted “DeepSeek and FinTech: The Democratization of AI and Its Global Implications” on SSRN. Here is the abstract:

DeepSeek, a Chinese AI company, has introduced a new paradigm in FinTech by making high-performing AI models accessible at significantly lower costs. This paper explores how DeepSeek’s cost-efficient and open-source approach is disrupting traditional AI development, lowering barriers to entry for startups, and fostering competition in financial services. The research highlights the transformative applications of democratized AI in lending, investment management, insurance, and payments, as well as the systemic challenges it presents, including regulatory concerns, ethical dilemmas, and geopolitical implications. Additionally, the paper examines the potential impact of DeepSeek on AI efficiency, energy consumption, and market dynamics. While DeepSeek’s success offers opportunities for financial inclusion and innovation, it also raises concerns about security, data governance, and the shifting balance of technological power. The findings underscore the need for global regulatory coordination and strategic adaptation in the FinTech sector.

Daniel Schwarcz (U Minnesota Law) et al. have posted “Regulating Robo-advisors in an Age of Generative Artificial Intelligence” (Washington and Lee Law Review (2025), Forthcoming) on SSRN. Here is the abstract:

New generative Artificial Intelligence (AI) tools can increasingly engage in personalized, sustained and natural conversations with users. This technology has the capacity to reshape the financial services industry, making customized expert financial advice broadly available to consumers. However, AI’s ability to convincingly mimic human financial advisors also creates significant risks of large-scale financial misconduct. Which of these possibilities becomes reality will depend largely on the legal and regulatory rules governing “robo-advisors” that supply fully automated financial advice to consumers. This Article consequently critically examines this evolving regulatory landscape, arguing that current U.S. rules fail to adequately limit the risk that robo-advisors powered by generative AI will convince large numbers of consumers to purchase costly and inappropriate financial products and services. Drawing on general principles of consumer financial regulation and the EU’s recently enacted AI Act, the Article proposes addressing this deficiency through a dual regulatory approach: a licensing requirement for robo-advisors that use generative AI to help match consumers with financial products or services, and heightened ex post duties of care and loyalty for all robo-advisors. This framework seeks to appropriately balance the transformative potential of generative AI to deliver accessible financial advice with the risk that this emerging technology may significantly amplify the provision of conflicted or inaccurate advice.

Nydia Remolina (Singapore Management U Yong Pung How Law) has posted “Mapping Generative AI Regulation in Finance and Bridging Regulatory Gaps” (Journal of Financial Transformation, Forthcoming) on SSRN. Here is the abstract:

Generative artificial intelligence (GenAI) is rapidly reshaping the financial services sector by introducing new avenues for innovation, efficiency, and profitability. GenAI systems, including models like “generative adversarial networks” (GANs) and “transformers”, can autonomously generate content such as synthetic data, trading strategies, and fraud detection insights, transforming traditional financial operations. However, these advancements come with new challenges, particularly in ensuring that GenAI is deployed ethically, securely, and in compliance with evolving regulatory frameworks. Current financial regulations, such as those governing anti-money laundering (AML), market integrity, financial consumer protection, among others, were originally designed for human-driven processes and do not fully address the complexities introduced by AI systems. While some jurisdictions, such as the E.U., Singapore, the U.S., and China, have launched AI regulatory initiatives, frameworks specifically tailored to the financial services industry are still a work in progress. This article seeks to provide an overview of the regulatory landscape while raising awareness of the gaps that financial institutions and regulators should address to bridge the gaps in the GenAI responsible adoption in the financial sector.

Ben Davies (U Calgary Law) has posted “Artificial Intelligence & FINRA Arbitration Awards: Utilizing AI and Arbitral Analytics to Uncover FINRA Arbitration Award Patterns” on SSRN. Here is the abstract:

This paper creates novel analytics and data on FINRA arbitration awards with novel artificial intelligence models fine-tuned and fed unsupervised FINRA awards to provide sentiment analysis on individual sentences and entire awards. This AI generated data, combined with prior FINRA arbitral analytics AI research, results in interesting analytics on which words, sentence structures, and awards (from 2008 to 2020) could increase the chances of a party winning a FINRA claim even if the claim is weak. To better understand AI and arbitral analytics, this paper will provide a brief history of these respective fields, ethical issues surrounding AI usage in the legal field, prior FINRA research, and short conclusion.

Frank Pasquale (Cornell Law) and Mathieu Kiriakos (U Sherbrooke) have posted “Contesting the Inevitability of Scoring: The Value(s) of Narrative in Consumer Credit Allocation” (Algorithmic Transformations of Power: Between Trust, Conflict, and Uncertainty, edited by C. Burchard and I. Spiecker (Nomos, forthcoming 2025).) on SSRN. Here is the abstract:

When firms allocate credit to consumers, credit scoring often seems both inevitable (how else could the decision be made?) and desirable (how else could the decision be objective and fair?). We challenge both assumptions, after exploring the power asymmetries generated by scoring. Evaluations of narrative accounts of creditworthiness are plausible in at least some scenarios, despite the volume of credit applications. Moreover, these alternative paths to credit reflect normative values (such as intelligibility and fair consideration) that are just as compelling as the objectivity and fairness attributed to scoring. 

One of these values is trust. While quantitative assessments of reliability based on third-party data are designed to enable “trustless” transactions, qualitative accounts of creditworthiness depend on evaluators’ trusting the accounts of creditworthiness offered by those applying for credit. What this shift potentially loses in efficiency it has the potential to gain in mutual understanding, the alleviation of alienation, and opportunities for redemption. It also represents a democratization of power in financial relationships, requiring those with funds to lend to do a bit more to understand at least some of those applying for credit on their own terms, rather than forcing applicants into Procrustean beds of data analytics.

Alessio Azzutti (U Glasgow Law) has posted “AI Governance in Algorithmic Trading: Some Regulatory Insights from the EU AI Act” on SSRN. Here is the abstract:

The frenzied race toward Artificial Intelligence (AI) adoption is causing profound transformations within the financial sector, rendering capital markets an increasingly complex system. These dramatic and sweeping changes are most pronounced in data-intensive and high-performance computing domains, such as algorithmic trading. While AI-powered trading offers numerous benefits to financial firms, markets, and society, it also raises significant concerns regarding potential risks to market quality, integrity, and stability. Recent studies underscore the dangers posed by AI advancements, particularly when not accompanied by robust governance and regulatory frameworks, which could lead to new and heightened risks of market abuse. Amidst this risk-prone environment, there is growing recognition among policymakers and financial regulators of the pressing need to regulate AI deployment. This emerging awareness is crucial, as effective AI governance is essential to ensure that the benefits of technological innovation are not overshadowed by its inherent risks. In this very direction, the EU AI Act stands out as a landmark effort in establishing comprehensive AI regulation. Hence, this Article critically examines this fundamental piece of (global) legislation and compares it to sectoral regulation on algorithmic trading. By focusing on key legal provisions, the analysis demonstrates the potential superiority of the EU AI Act’s regulatory requirements for providers of “high-risk” AI systems over those for deployers of algorithmic trading systems under MiFID II. The Article concludes with some ideas for future risk-based regulation of AI applications in financial trading.

John Taskinsoy (Universiti Malaysia Sarawak) has posted “The Great Silent Crash of the 21st Century” on SSRN. Here is the abstract:

A mysterious creator under the alias Satoshi Nakamoto (a pseudonym) launched the world’s first successful cryptocurrency in early January 2009 which, not only was a historic moment, but was one that cultivated a technology revolution and money’s evolution into a digital form. However, technical issues (inherent flaws) in the design of Bitcoin blockchain, non-technical issues (political backlash, regulatory hurdle, and environmental hazard), plus the opaqueness surrounding the launch of Bitcoin have opened the door for an endless debate, incessant criticism, spurious claims, heated arguments, plethora of articles, and media frenzy contemplating what Bitcoin really is (crypto-asset, commodity, or investment vehicle) or it is not (currency). On the technical side, blockchain that made Bitcoin a household name fails miserably; high latency (8 minutes or more), low transaction throughput (7 per second), low-scalability (mining, proof-of-work based validation using consensus and cryptography), and high energy cost make Bitcoin unfit to compete with new and fast-scalable cryptocurrencies such as Solana’s transaction speed (50,000 per second) or XML’s $0.00001 fee per transaction. Although technical problems are not without a solution, non-technical issues are not easy to resolve because their resolution depends on politicians, law makers, regulators, and various government agencies (central banks, the Fed and the ECB in particular) who choose to run headlong into backlash to Bitcoin and other cryptocurrencies. On Tuesday (October 8, 2022), prices of cryptocurrencies tanked, citing the industry-shaking collapse of FTX (second largest after Binance), some even dubbed the event as “Crypto’s Lehman moment”.

But erratic price movements is not something new in the crypto industry which has been on a roller-coaster since December of 2021, i.e. after Bitcoin price hit almost $68,000 and its market cap $1.24 trillion, jittery investors in a hurry began to cash out their hefty gains. The inability of FTX’s CEO Sam Bankman-Fried to handle his plan to sell his company (which was regarded as one of crypto’s “blue chip” companies) to the rival crypto exchange Binance set off a widespread selling panic, as a result, cryptocurrency market shed a mindboggling $236.7 billion ($81 billion by Bitcoin) in just two days (Tuesday and Wednesday), which by any standard was insanely bonkers.

Lawrence J. Trautman (Prairie View A&M University – College of Business; Texas A&M University School of Law (By Courtesy)) has posted “The FTX Crypto Debacle: Largest Fraud Since Madoff?” on SSRN. Here is the abstract:

In her letter to Treasury Secretary Janet Yellen dated September 15, 2022, U.S. Senator Elizabeth Warren requests “the Treasury Department’s (Treasury’s) comprehensive review of the risks and opportunities presented by the proliferation of the digital asset market, which ‘will highlight the economic danger of cryptocurrencies in several key areas, including the fraud risks they pose for investors.” Senator Warren warns, “It is crucial that Treasury “create the analytical basis for very strong oversight of this sector of finance because cryptocurrency poses grave risks to investors and to the economy as a whole.”

Just weeks later, during November 2022 reports emerge that “In less than a week, the cryptocurrency billionaire Sam Bankman-Fried went from industry leader to industry villain, lost most of his fortune, saw his $32 billion company plunge into bankruptcy and became the target of investigations by the Securities and Exchange Commission and the Justice Department.” The demise of FTX and its’ many related crypto entities created contagion and collateral damage for other participants and investors in the cryptocurrency community. The U.S. bankruptcy proceedings of many FTX related entities, scattered across many jurisdictions worldwide, will likely take years to sort out.

Shortly after the Chapter 11 filing, post-bankruptcy FTX new CEO John J. Ray III characterizes the collapse of FTX as the result of “the absolute of concentration of control in the hands of a very small group of grossly inexperienced and unsophisticated individuals who failed to implement virtually any of the systems or controls that are necessary for a company that is entrusted with other people’s money.”

In just a few years Bitcoin and other cryptocurrencies have had a major societal impact, proving to be unique payment systems challenge for law enforcement, policy makers, and financial regulatory authorities worldwide. Rapid introduction and diffusion of technological changes, such as Bitcoin’s crypto foundation the blockchain, thus far continue to exceed the ability of law and regulation to keep pace. The story of FTX and potential consequences for investors and the global financial system is the subject of this paper.

This paper proceeds in thirteen parts. First, is a discussion of the history and growth of crypto currencies. Second, crypto and national security risks is examined. Third, the failure of FTX is introduced. Fourth, bankruptcy. Fifth, the collateral damage thus far to the crypto ecosystem is described. Sixth, the FTX demise is examined in terms of threshold questions that may help to understand what has transpired and how productive policy may be crafted for the future. Seventh, the role of the SEC is explored. Eighth, the CFTC is discussed. Ninth, crypto and the federal Reserve is addressed. Tenth, features the role of Congressional inquiries. Eleventh, explores regulatory implications. Twelfth, focuses on the failure of corporate governance. Thirteenth discusses prosecution and litigation. And last, I conclude.