By Kate Lee, Global Head of Enterprise Regulatory Data, Bloomberg.
Insurers have now spent much of 2026 applying the National Association of Insurance Commissioners’ (NAIC) principles-based bond definition through its second reporting cycle and are quickly approaching year three of implementation. This rule was introduced to modernize statutory bond classification, replacing an approach that leaned heavily on legal form. Under the prior framework, a security structured as debt could qualify as a bond even when its cash flows and credit support did not behave like traditional debt.
Principles-based classification asks a different question by looking at economic substance, and how a security generates and protects its returns, rather than how the paperwork describes it.
That distinction is easy to state. Applying it consistently across a portfolio is another matter. Year one taught the industry how the rule works in specific cases and year two has been where that knowledge has had to hold up at scale and under audit, across portfolios that keep adding private credit and structured securities that do not sit neatly in either bucket.
From a Downstream Question to an Earlier, Shared One
Under the prior rules-based framework, classification was largely an accounting matter that could wait until quarter-end. That is no longer true. The same security can now land in different places on the balance sheet depending on how its cash flows, collateral, and credit support are read, which means investment, accounting, and reporting teams must answer the same question, frequently before a trade settles rather than after.
When a classification decision can shift a security’s accounting treatment, working that out for the first time at reporting means rebuilding the analysis under pressure. That’s why more insurers are moving the logic earlier, building it into the investment process itself, so the question gets settled before a trade rather than reconstructed after one.
For traders and portfolio managers, the practical implication is pre-trade screening, allowing classification policy to become an important input into the investment decision rather than something reconciled against it after the fact.
Increasing the stakes, there’s also a capital dimension: an asset reported on Schedule D can carry different capital treatment than the same asset reported on Schedule BA. That means a single gap in the data behind a classification decision doesn’t stay a compliance question, as it can change what an insurer is required to hold in capital.
Data Is Doing More of the Work
Quality data underpins all of this. Insurers need reliable, complete, and timely information on cash flows, collateral, credit support, and structural features to support classification decisions that are repeatable and explainable. That need is most acute for complex assets, where the information required to assess economic substance is not always readily available in downstream accounting systems.
Reaching a classification used to be the hard part. Now, evidencing it is. A principles-based decision must be reconstructable. The data behind it, the logic applied, and how exceptions were handled all need to hold up when an auditor asks why a security landed where it did. Many insurers underestimated that lift heading into year one.
That bar is highest for asset-backed securities assessed under the rule’s practical expediency test, which asks firms to show that a security’s return comes from the meaningful cash flows of the underlying assets, rather than from sale or refinancing. Meeting that standard is a data problem as much as an accounting judgment. It depends on reliable, security-level information on cash flows and collateral, available early enough to inform the decision rather than just document it afterward.
Credit support analysis for asset-backed positions shows where this gets genuinely hard. The rule asks whether credit support puts an investor in a “substantially different economic position” than direct ownership of the underlying collateral. That is a judgment call, and market practice has not converged on a single answer. The variation reflects how open that standard is to interpretation, and how much the outcome depends on the data and reasoning a firm can point to behind its own read.
What Future Success Requires
None of this is limited to legacy or unusually complex holdings. These classification and data challenges are increasingly showing up earlier and more often, rather than staying at the edges of a portfolio. Investment, accounting, and reporting teams need to be working from the same underlying data, not reconciling three different versions of it after the fact.
The first reporting cycle gave the industry a working baseline of practical experience. Year two is when insurers have gotten to refine what they learned, test it against real audits, and grow portfolio complexity, with real capital consequences. The insurers best positioned for success are treating the framework as an operating model question, one that reaches into data infrastructure, cross-functional governance, and audit defensibility. What that takes in practice is regulatory data that delivers consistent, explainable, data-driven bond classifications across investment, accounting, reporting, and audit workflows.
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