CFA Institute

While financial services firms continue to accelerate AI adoption, governance maturity is lagging. Legacy frameworks around models, data, and technology were not designed for today’s AI landscape: probabilistic models, opaque third-party dependencies, and, increasingly, autonomous agentic systems. As a result, firms attempting to scale AI using traditional governance approaches may find themselves exposed to risks that are difficult to detect, quantify, or control. Weak AI governance can translate directly into misinformed investment decisions, security vulnerabilities, and ultimately, financial and reputational losses. Conversely, firms that build effective governance frameworks can better align AI with business objectives, manage downside risks, and create a more durable competitive advantage. To address this challenge, I propose a two-tiered AI governance framework that integrates program-level oversight with use-case-specific controls. Much like the complementary top-down and bottom-up approaches in investing, this structure enables both consistency at scale and precision in execution. The program-level component centers on three core actions: Discover your AI assets in order to govern them effectively Establish enterprise-level governance structures and mechanisms Focus enterprise-level governance on a few critical domains Discover: A foundational step is establishing comprehensive inventories of AI assets, use cases and agents. These will serve as the building blocks for governance processes at both the program level and the use case level and should be linked into enterprise’s overarching governance and risk management mechanisms and tools. As we look to the future, it’s becoming critical to apply some of the same institutional and organizational processes to managing AI agents that we commonly apply to managing people, which is near impossible without these inventories in place. Establish: Oversight mechanisms fall into this category including policy and procedures, risk appetite statements, chain of authority and escalation, and the creation of an enterprise AI literacy program. These elements define the “rules of the road” and act as a first line of defense against internal and external pressures that will inevitably arise during AI implementation. Focus: The rapid proliferation of AI governance frameworks and controls can create the impression that effective governance requires a “boil the ocean” approach. In practice, this is neither feasible nor necessary. AI governance should instead be deliberately scoped and aligned with an organization’s specific risk profile, operating model, and strategic priorities. The objective is not completeness, but effectiveness. source

FIMI does not predict what a government will do. It classifies what it has done, and directs the analyst toward the correct transmission mechanism, the correct positioning horizon, and the correct risk assessment. The South Korean case was not a crisis. It was a clean, verifiable example of a mechanism that is becoming more common as governments in post-QE environments seek tools that produce monetary-scale demand effects without requiring central bank action. The same logic applied in three jurisdictions over six years. The probability of recurrence is non-zero, and the precedents make it not a hypothesis. When the next case emerges practitioners who have a name for it and a checklist to verify it will be positioned ahead of those who reach for the nearest available label. The label determines the position. The wrong label means the wrong trade. References [1] Bank of Korea. Monetary Policy Decision, April 10, 2026.https://www.bok.or.kr/eng/bbs/E0000634/view.do?nttId=10097454&menuNo=400069 [2] Ministry of Economy and Finance, Republic of Korea. 2026 Supplementary Budget to Overcome the Middle East War Crisis. March 31, 2026.https://www.khan.co.kr/en/article/202603311234007/ [3] Korea Herald. Gov’t proposes W26.2tr extra budget, including W4.8tr for cash handouts. March 31, 2026.https://www.koreaherald.com/article/10706553 [4] Seoul Economic Daily. Korea Passes 26.2 Trillion Won Supplementary Budget. April 10, 2026.https://en.sedaily.com/politics/2026/04/10/korea-passes-262-trillion-won-supplementary-budget-payments [5] FocusEconomics. Korea Central Bank Meeting: Central Bank Stands Pat in April. April 11, 2026.https://www.focus-economics.com/countries/korea/news/monetary-policy/korea-central-bank-meeting-11-04-2026-central-bank-stands-pat-in-april/ [6] Brookings Institution. What did the Fed do in response to the COVID-19 crisis? Updated January 2024.https://www.brookings.edu/articles/fed-response-to-covid19/ [7] Brightman, C. Too Soon? Pandemic Policy Response Raises Risk of Inflation. Research Affiliates. April 2020.https://www.researchaffiliates.com/insights/publications/articles/802-too-soon-pandemic-policy-response-raises-risk-of-inflation [8] Bank of England. HM Treasury and Bank of England announce temporary extension to Ways and Means facility. April 2020.https://www.bankofengland.co.uk/news/2020/april/hmt-and-boe-announce-temporary-extension-to-ways-and-means-facility [9] Hausman, J. and Wieland, J. Abenomics: Preliminary Analysis and Outlook. Brookings Papers on Economic Activity, 2014.https://www.brookings.edu/bpea-articles/abenomics-preliminary-analysis-and-outlook/ [10] Federal Reserve Bank of San Francisco. Assessing Abenomics: Evidence from Inflation-Indexed Bonds. Working Paper 2019-15.https://www.frbsf.org/economic-research/publications/working-papers/2019/15/ [11] Feltmate, T. Assessing the Feasibility of President Trump’s Tariff Dividend Checks. TD Economics. December 5, 2025.https://economics.td.com/us-assessing-the-feasibility-of-President-Trump-Tariff-dividend-checks [12] Sargent, T.J. and Wallace, N. Some Unpleasant Monetarist Arithmetic. Federal Reserve Bank of Minneapolis Quarterly Review, 1981.https://www.minneapolisfed.org/research/quarterly-review/some-unpleasant-monetarist-arithmetic [13] Leeper, E.M. Equilibria under ‘Active’ and ‘Passive’ Monetary and Fiscal Policies. Journal of Monetary Economics, 27(1), 1991.https://uva.theopenscholar.com/eric-leeper/publications/equilibria-under-%E2%80%98active%E2%80%99and-%E2%80%98passive%E2%80%99monetary-and-fiscal-policies [14] Bernanke, B.S. Deflation: Making Sure “It” Doesn’t Happen Here. Federal Reserve Board. November 21, 2002.https://www.federalreserve.gov/boarddocs/speeches/2002/20021121/default.htm [15] Turner, A. Between Debt and the Devil: Money, Credit, and Fixing Global Finance. Princeton University Press, 2015. ISBN 978-0691165856.https://books.google.com/books/about/Between_Debt_and_the_Devil.html?id=D26YDwAAQBAJ [16] Cochrane, J.H. The Fiscal Theory of the Price Level. Princeton University Press, 2023.https://www.hoover.org/research/fiscal-theory-price-level [17] Hooley, J., Khan, A., Lattie, C., Mak, I., Salazar, N., Sayegh, A., and Stella, P. Quasi-Fiscal Implications of Central Bank Crisis Interventions. IMF Working Paper No. 23/114. June 2023.https://www.imf.org/en/publications/wp/issues/2023/06/02/quasi-fiscal-implications-of-central-bank-crisis-interventions-534076 source

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The modern litigation finance market expanded rapidly from a niche practice into a multi-billion-dollar asset class. Early funders deployed non-recourse capital into individual cases in exchange for a share of any recovery, often bearing the full downside risk in pursuit of a portion of proceeds. This approach proved effective in establishing the market. It enabled claimants without financial resources to pursue litigation, extending beyond the traditional contingency-based model used by law firms, while offering capital providers the prospect of uncorrelated, potentially high and repeatable returns. However, the structure of that model, shaped by the industry’s origins, also embedded many of the challenges now coming to the surface. Early underwriting emphasized case merits and probability of success. While necessary, this approach often placed less emphasis on portfolio construction, capital allocation across cases, and the pricing of duration. In practice, investment decisions frequently resembled legal analysis rather than institutional underwriting. A related question historically was why law firms themselves did not become the primary risk transferees. While some smaller firms operated on contingency, larger firms were generally not structured to absorb sustained downside risk, given overhead and business models. This gap helped give rise to dedicated litigation funders, entities combining legal expertise with capital provision, but often retaining a legal, case-by-case approach to risk. The case-by-case, venture-style model reinforced these dynamics. Returns depended heavily on binary outcomes, and duration, the time required for cases to resolve, was not systematically incorporated into return expectations. As the market scaled, these design choices came under pressure.Courts have increasingly scrutinized funding arrangements. The UK Supreme Court’s PACCAR decision determined that litigation funding agreements entitling funders to a percentage of damages could fall within damages-based agreement regulations, rendering many existing agreements unenforceable.  Subsequent rulings in the Competition Appeal Tribunal, including the refusal to certify collective proceedings in Riefa v. Apple and Amazon, highlighted concerns that success fees could generate excessive returns for funders, that payment structures could prioritize funders over claimants, and that confidentiality provisions could limit transparency. These developments reflect underlying structural tensions. Funding arrangements can create misalignment between funders seeking higher returns and claimants seeking timely resolution. Courts, recognizing these dynamics, have shown a willingness to intervene. Duration risk has also become more visible. Litigation timelines frequently extend beyond expectations, tying up capital without additional compensation under traditional models. Taken together, these factors are reshaping how litigation finance is evaluated by allocators, structured by fund managers, and supported by insurers. source

Joop Huij, Dries Laurs, Philip Stork, and Remco C. J. Zwinkels Carbon beta measures a stock’s sensitivity to climate transition risk using a pollutive-minus-clean factor. It measures climate exposure, aligns with forward looking risk indicators, and shows that high-carbon-beta firms underperform when climate shocks occur. source

Start with pension allocation. Nigeria’s pension assets reached ₦26.66 trillion as of October 2025, with roughly 60%, or about ₦16 trillion, invested in government securities. If the real return on government paper has been negative for most of the past 15 years, then millions of retirement savers were not just earning low returns. They were losing purchasing power while their nominal balances increased. This is not unique to Nigeria. The OECD’s 2024 pension report, using 2023 data, found that pension systems in Nigeria, Angola, and Egypt, where more than half of assets are allocated to bills and bonds, delivered negative real returns. Recent increases in Nigeria’s pension fund equity allocation limits are directionally positive. But they are modest relative to the scale of the problem. Under the old CPI methodology, a 91-day T-bill yielding 18% against inflation at 34.8% was clearly negative in real terms. Under the rebased CPI, a yield of 15% against inflation of 15.15% appears roughly neutral. Has the underlying reality improved, or has the measurement changed? The answer is both. Inflation has genuinely moderated. Monthly CPI increases fell below 1% for several consecutive months in the second half of 2025. But the rebase also lowered measured inflation by roughly 10 percentage points. Without a continuous series, it is difficult to separate these effects. What is clear is that the sign has shifted. From August 2025 through January 2026, real returns turned positive for six consecutive months. January 2026 was the strongest month, with a +4.39% real return, driven by a 2.88% month-on-month decline in CPI alongside a 1.38% nominal T-bill return. The real return index rose from 984 to 1,027, above its base level of 1,000 for the first time. After 15 years of negative returns, cash is no longer guaranteed to destroy purchasing power. Whether that shift proves durable remains an open question. source

Current accounting standards, including IFRS 7 and IAS 7, require disclosure of these programs, but disclosures remain inconsistent, difficult to compare across firms, and frequently buried in footnotes. As a result, investors and lenders may struggle to assess the true extent of leverage and liquidity risk. Most financial analysis tools—automated screening systems, trading algorithms, credit rating models, brokerage platforms, and standard dashboard summaries—rely primarily on headline data, not the detailed disclosures buried in the notes. As a result, supplier financing liabilities frequently escape detection in the very metrics that investors and lenders use to assess risk. In many cases, firms willingly accept financing costs that exceed those of traditional bank borrowing because these arrangements provide funding without increasing reported debt or weakening leverage-based performance measures. The incentive is therefore often not cheaper financing, but more favorable financial reporting. Given the central role of ratios such as Debt/Equity, Net Debt/EBITDA, and OCF in financial analysis, these metrics must be built on transparent, prominently reported classifications. They should not require forensic investigation into footnote disclosures to understand the extent to which operating metrics are being influenced by disguised financial liabilities. If a buyer extends payment terms specifically because a financing program makes such an extension possible, then the economic substance of the transaction is borrowing, not operational trade credit. Classifying these obligations as trade payables fails to reflect their underlying nature and undermines the usefulness and integrity of reported financial metrics. source

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Tokenized money market funds and blockchain-based interbank settlement have moved from pilot to production. Equities are emerging as the next frontier. Several regulated platforms are preparing to offer blockchain-based versions of publicly traded stocks in 2026. Some promise 24/7 trading. Others highlight compressed settlement cycles, fractional ownership, and global distribution. The narrative is familiar: faster, cheaper, more accessible markets. The key question is not technological feasibility, but structural viability. Are tokenized equities legally enforceable, operationally sound, and compatible with existing market safeguards—or simply new wrappers around familiar risks? Below I outline a set of practical tools institutional investment managers can use to evaluate these instruments. source

For decades, emerging markets traded as a macro asset class, a leveraged expression of the dollar cycle, domestic growth, and external balances (we discuss this further in 10 Rules of Country Selection in Emerging Markets). Today, the EM equity index looks very different. It has become increasingly dominated by a few mega-cap technology companies whose fortunes are tied more closely to AI investment and global supply chains than to traditional EM macro drivers. Yet many global allocators still approach EM as a macro asset class tied to currencies, domestic growth, and external balances. This creates a growing disconnect: in its current form, the EM index increasingly functions as an indirect play on global technology investment and US-led AI capital expenditure. As a result, investors seeking diversification away from US equities may not achieve the intended outcome through passive EM exposure alone. Furthermore, research by Arslanalp et al. (IMF, 2020) highlights that benchmark-driven allocations can amplify the role of external factors at the expense of domestic fundamentals, increasing the risk of flows that are disconnected from local economic conditions. For allocators aiming to express macro views, a more targeted approach may be required. Active strategies, in this context, offer the flexibility to align portfolios with underlying macro drivers rather than with the backward-looking composition of the index. source