Why Insurance Chief Investment Officers Must Transform Investment Data Management Amid Rising Regulatory Pressure

Regulators seek to modernize capital adequacy frameworks

Regulators are striving to modernize how they marshal the financial health of insurance companies to keep pace with the radical evolution of capital markets that have been reformatted by legislation, regulations, digitization, and shifting geopolitical dynamics. They are concerned that certain investments, such as collateralized fund obligations (CFOs), collateralized loan obligations (CLOs), private-label asset-backed securities (ABS), and other ABS, are incorrectly structured to resemble bond instruments. National Association of Insurance Commissioners (NAIC) regulators are determined to gain transparency of opaque private asset classes, keenly focused on challenges in private securities reporting and valuations and security identifier gaps that distort liquidity perception. Insurance treasury and investment departments are trying to sensibly evolve their operating models and technology stacks accordingly.

NAIC to modernize risk-based capital (RBC) formulas 

The NAIC is determined to create consistency in its risk-based capital (RBC) formulas for capital adequacy, as evidenced by the single largest accounting guidance change in decades with its new principles-based bond definition (PBBD) that went live in January.ⁱⁱ NAIC’s RBC formulas measure the inherent riskiness of a company’s financial assets and operations by way of formulas assigning a level of risk to asset classes, no longer a simple task. The NAIC has very sound reasoning behind their efforts, since it found that more than 10,000 securities lacked valid identifiers, which collectively represented about $55 billion of book-adjusted carrying value.ⁱⁱⁱ Insurance companies file this information by way of a Quarterly Statutory Statement, NAIC Statutory Annual Statement, and Own Risk and Solvency Assessment Summary Report.

The trouble is: RBC formulas, with only six risk designations, were created for a bygone era before insurers diversified portfolios to drive better returns. Now, CIOs are striving to adapt and get much more granular in reporting, with 20 different risk ratings for fixed income securities. They must get the data right. Reporting templates via Excel are bound to introduce error-prone reporting, with no concept of change management, maker/checker workflows, data lineage, or bi-temporality. Insurance CIOs have an urgent need to harmonize data from transactional activity across multiple sources and fragmented architecture between entities.

Asset Adequacy Analysis framework in the US

The introduction of reserve adequacy requirements for life insurance companies (Actuarial Guideline 53) as part of the larger Asset Adequacy Analysis framework is very detailed and very nuanced, making compliance virtually impossible without precise asset class data modeling.^iv Further, there are very specific US-state related reporting requirements, for example, life insurance companies who do business in New York need to comply with New York’s asset adequacy Regulation 126.

UK standards call for look-through reporting

Insurers in the United Kingdom face even more rigorous transparency standards to close liquidity reporting gaps for major insurance firms with “significant exposure to derivatives or securities involved in lending or repurchase agreements.” The Prudential Regulation Authority (PRA) is introducing new liquidity reporting requirements in CP19/24 that will take effect in Q3 of 2026.^v These call for enhanced data oversight and require insurers to execute a look-through approach to gain clearer visibility into underlying holdings information. This standard relies on the smooth flow of investment data, including private markets data, which is typically unstructured.

Look-through reporting for private asset vehicles

The more that insurance CIOs allocate to private assets, the more documentation and reference data they will need. Additionally, they need to track every change, capturing when, where, why, and how data is getting updated as terms change. A simple refresh of a market data feed for publicly traded investment grade debt may update all the attributes; but for the world of privately negotiated investments, like structured credit vehicles with pools of private loans, it’s hard to track the daily changes that occur for hundreds or thousands of individual loans. Hence, the greater emphasis on data quality, accuracy, lineage, and documentation.

Capturing all the metadata on these private deals will be critical as it will help streamline reporting needs, both for various internal parties and external parties, including policyholders, regulatory agencies, and stockholders (for those that are publicly traded). They must also conquer the data conundrum to achieve improved visibility into private equity and private credit vehicle structures and to see what’s under the hood of a CLO, CBO, CMO, ABS or pass-through MBS. Insurers that can achieve true look-through capabilities to identify and report the actual underlying exposures of their structured vehicles will enjoy newfound confidence and control in their allocations.

Regulators seek private markets transparency

In the aforementioned bygone, pre-securitization era, insurance company portfolios looked straight forward with investment-grade bonds and commercial mortgages and very little else. Their portfolios’ go-to asset class, public bonds, have slid from 70% to 60% allocations from 2010 to 2024. Now, equities are prominently included (13%), while the fastest-growing asset class falls under NAIC’s Schedule BA — alternative assets — which grew faster than U.S. insurers’ overall investment portfolio in 2024, increasing by 7.8% compared to the prior year.^vi Private placements account for 21.1% of total insurance AUM, up from 20% at the end of 2023; and 64% of insurance CFOs and CIOs in the Americas plan to increase their allocations to private credit over the next 12 months.^vii

The latest issues with First Brands and Tricolor paint a real-life picture of the challenges in understanding true private credit exposures. Even though there was a reported combined $180 million exposure,^viii was there in fact more? First Brands accumulated billions of dollars in debt that wasn’t reflected on its balance sheet. The majority of First Brands’ on-balance-sheet debt wasn’t supplied by private credit firms. This puts an exclamation point on the need for firms to understand other invested assets that may be exposed and thus see any decrease in value based on similar industry, geographic region, or supply chain dependencies. JPMorgan’s Jamie Dimon even acknowledged his bank’s failure to spot problems with subprime auto lender Tricolor ahead of that firm’s collapse, but also raised concerns about due diligence across the private credit world.^ix

Data challenges in private assets that cause reporting roadblocks

To keep precise books of record, insurance general account investment operations must grapple with different definitions of data points for risk, portfolio, asset classification, valuation, and capital management. They must solve their data conundrum to determine whether allocations meet their risk and collateral criteria for different types of reserves. Categorizing different assets into the correct collateral buckets can be onerous and time intensive, if done manually. Automated data workflows and data quality tools are key for comprehensive security mastering, to fully capture everything from public/vanilla securities to more esoteric securities and illiquid instruments.

Private markets data integration for visibility

Some insurance investment systems don’t handle the necessary look-through reporting or drill downs and are inflexible in investment analysis. Yes, it’s a data problem. Traditional data platforms don't do well in converging all portfolio data, entity reference, borrower and loan attributes, and lifecycle update data from multiple systems — an absolute plethora of data points around public, fixed income, and private debt — into one big, clean data set. Moreover, most systems get tripped up in ingesting and aggregating unstructured datasets from documents like loan agent notices, drawdown notices, and loan tapes, which are prevalent in private credit.

Further, workflows for reconciling market performance of portfolio holdings remain largely a manual exercise. This makes the reporting job tough when trying to satisfy increasing demands for timely, detailed performance metrics, especially as insurers increase private markets activity. For example, those new UK regulatory rules for reporting on liquidity under stress are exceedingly comprehensive, requiring data from multiple systems and segments of the investment business.

Insurers need one clean dataset, a centralized golden source of data that helps ingest, normalize, model, distribute data, and automate data quality management, for a holistic view of assets and liabilities. This allows users to drill-down, deftly moving from high-level “top of the house” summaries to progressively detailed data layers. The centralization and automation of reporting is impossible without a standardized, unified store of data.

Customizable, systematic, automated reporting

This modernized central data platform will give insurance CIOs the free-flowing, accurate investment data their teams need to shore up and speed up workstreams. Centralized infrastructure enables smooth data sharing across teams, preventing the annoying back and forth between departments trying to gather the right information. Investment and operations teams benefit from self-service tools, easy data discovery (including data cataloging), and customizable reporting tools for automated, systematic reporting, both internally and for regulators.

CTOs can then advance their AI strategies to help automate numerous functions. AI can subsequently help ingest unstructured data by processing information from PDFs, earnings call transcripts, emails, and other nonstandard reports. It can help automate data quality management, setting up quality rules, data validation, flagging data breaks/exceptions, and even fixing the exceptions. Investment operations teams often find themselves manually fixing data breaks because of disjointed datasets from upstream systems. This way of working is not only wasteful and inefficient, but it can also increase errors, delay decision making, weaken reporting and transparency, and increase the chances of noncompliance.

Insurance companies live at the epicenter of capital markets investing, with soaring AUMs and a penchant for private market asset classes, leading to intricate insurance and private credit tie-ups. Modern, integrated data solutions are no longer shiny new objects. They are a strategic imperative that transforms reporting and compliance from a burden into a competitive advantage and are a core factor in technology decision-making for insurance investment management.