Hook
The most revealing part of Moody’s intervention was not the warning about private credit. It was the audience. The ratings company did not simply publish a market note or adjust a model. It urged the National Association of Insurance Commissioners to treat private credit ratings more strictly, placing a competitive dispute inside the machinery that helps insurers decide which assets qualify for investment.
That move matters because insurance portfolios are no longer built only from easily priced public bonds. Insurers are reaching further into private credit, structured finance, and other less liquid assets in search of yield. In those markets, a rating is not just a number attached to a security. It can determine capital treatment, internal approval, and whether an investment fits a regulated portfolio. A request for tougher recognition rules therefore reaches beyond risk management. It asks who gets to translate uncertainty into an official financial language.
Context
The NAIC is not a securities exchange or a lending platform. It is a coordinating body for United States insurance regulation, and its frameworks influence how state regulators evaluate insurers and their investments. When an insurer relies on an external credit assessment, the regulatory treatment of that assessment can affect how much capital the insurer must hold and how its investment team measures risk.
Moody’s operates as a traditional, nationally recognized rating institution. Private rating firms occupy a more varied landscape. Some focus on private debt, structured products, or specialized borrowers. Others offer faster, more customized assessments to clients whose assets do not fit neatly into the public bond market. Their methods may combine conventional credit analysis with proprietary data, machine learning, and direct access to borrower information.
The disagreement is consequently about more than whether one rating is accurate. It concerns the conditions under which a rating is considered reliable enough for an insurance portfolio. Moody’s position, as reported, is that stronger oversight would stabilize insurers’ investments, reduce systemic risk, and improve market integrity. Those are serious objectives. Yet the same rule can serve two purposes at once: protecting policyholders and raising the cost of competition.
Core Insight
Private credit creates a difficult technical problem because the market often lacks the continuous stream of public information that supports ratings for listed companies and widely traded bonds. A public issuer may provide audited statements, earnings calls, covenant disclosures, and observable market prices. A private borrower may provide detailed information to a small group of lenders, but that information is less standardized and harder for outsiders to verify.
A rating model must then fill the gaps. It may estimate default probability from cash flow, leverage, industry conditions, collateral, repayment history, and the strength of contractual protections. A machine-learning model might detect relationships across large datasets that a human analyst would miss. But predictive power is not the same as explainability. If an insurer cannot understand why a model assigned a rating, it cannot easily challenge the result when conditions change.
The important distinction is between a rating that is difficult to produce and a rating that is difficult to audit. Private firms can be highly sophisticated and still create model risk if their assumptions, training data, overrides, and error rates remain opaque. Traditional agencies can be more transparent in some areas and still fail when a familiar model underestimates a new kind of borrower. Regulatory recognition should therefore test the process behind a rating, not merely the age or reputation of the institution issuing it.
This is where the insurance connection becomes consequential. An insurer does not hold credit risk in isolation. It holds a portfolio whose assets mature at different times, respond differently to interest rates, and may become illiquid during stress. If multiple insurers rely on similar external ratings, a common error can travel through the system. A downgrade can force reassessment, raise capital requirements, or encourage several institutions to sell at once. The danger is not only an incorrect score. It is synchronized behavior built on that score.
The reverse danger also deserves attention. If regulators respond by recognizing only a narrow class of established rating providers, insurers may lose useful information about specialized assets. A private firm that knows a niche industry deeply may identify deterioration earlier than a broad agency with a slower review cycle. A smaller evaluator may also negotiate direct access to management, granular borrower data, and loan-level covenants that are invisible in a standard public filing.
That is why the language of "private ratings" can mislead. It makes the market sound like a single category when it is really a collection of methods, incentives, and disclosure practices. One private provider may rely on transparent, repeatable analysis. Another may sell a confidential opinion whose methodology is difficult to reproduce. Treating both alike could be as blunt as treating every public rating as inherently sound.
Based on my audit experience during the 2017 initial coin offering boom, the most dangerous phrase in a technical review is often "the model is proprietary." I examined more than forty early Ethereum projects and encountered contracts whose public branding suggested decentralization while decisive governance powers remained concentrated. In one case, the apparent exchange structure concealed a scheme that could have directed tens of millions of dollars toward insiders. The lesson was not that opacity always means fraud. It was that a system cannot earn trust merely by calling its internal logic sophisticated.
Credit models have a similar moral and operational boundary. Insurers, regulators, and policyholders do not need every line of code exposed, but they do need evidence that the model is stable, independently challenged, and capable of admitting error. That evidence could include documented methodology, historical performance across economic cycles, validation by parties separate from model development, disclosure of material assumptions, and clear procedures for handling conflicts of interest.
Technology can strengthen this process. Explainable artificial intelligence can show which variables influenced an outcome. Federated learning can help institutions improve models without pooling sensitive borrower data into one central repository. Timestamped audit records can preserve the history of model changes and analyst overrides. None of these tools magically creates truth. They create a trail through which truth claims can be questioned.
The regulatory debate should also examine incentives on the insurer side. External ratings are convenient because they allow investment committees to translate complex risk into a recognized benchmark. But convenience can become dependence. If an institution uses an outside grade as a substitute for its own analysis, it may satisfy a process while weakening its judgment. Internal risk teams should be capable of comparing external ratings with cash-flow scenarios, collateral quality, concentration limits, and stress outcomes.
In this respect, the market is approaching a fork. One path makes regulatory acceptance a badge controlled by a small group of established providers. The other creates a demanding but open framework in which any provider can qualify by demonstrating data quality, independence, validation, governance, and performance. The second path is harder to administer. It is also more consistent with the idea that competition should be earned through evidence rather than inherited through institutional status.
Contrarian Angle
The contrarian view is that Moody’s warning may be useful even if its commercial motive is defensive. An incumbent can recognize a genuine weakness in a changing market while also benefiting from rules that limit its competitors. Dismissing the warning because it comes from a powerful rating agency would be as careless as accepting it because the words "systemic risk" sound responsible.
The sharper question is what problem tougher regulation would solve. If it improves disclosure, model validation, conflict controls, and ongoing surveillance, it could protect insurers without freezing the market in its old shape. If it simply makes private providers bear the same fixed costs as global agencies, it may reduce choice without proving that the surviving ratings are better. A regulatory perimeter that protects incumbents can look stable right up until concentration turns one shared mistake into everyone’s mistake.
There is also a blind spot in the assumption that traditional ratings are the natural benchmark. The financial crisis showed that established methodologies can become dangerously confident when incentives, data, and market structure align. Private firms should face scrutiny, but legacy should not be confused with immunity. Democracy isn’t a transaction where every voice holds weight; it requires institutions that make room for evidence from voices outside the established circle.
Takeaway
NAIC’s eventual response should be measured against outcomes, not the prestige of the petitioner. The useful standard is simple to state and difficult to evade: can an insurer explain where a rating came from, test whether it remains valid, and act before a downgrade becomes a stampede?
The next generation of credit assessment may belong to partnerships between specialist analysts, independent validators, and transparent data systems. That future will not be secured by rejecting oversight. It will be secured by designing oversight that rewards verifiable competence wherever it appears. As private credit grows, the real test is whether regulation can protect trust without monopolizing the right to define it.