Insurance Company Investment Portfolio

Here’s the thing about running an insurance company investment portfolio: you can’t afford surprises, because that book is what pays your policyholders. So when software loan prices plunged in early 2026, a large insurer holding CLO and CMBS needed to know exactly what it was exposed to, and fast. With Valitana Analytics, the team pulled its aggregate exposure and stress-tested the whole book in real time, while the event was still unfolding. 

Insurance Company Investment Portfolio: Case Study At a Glance

The Firm

The firm is a large insurer writing life insurance and annuities, with liabilities that run decades into the future. To support them, it holds a substantial structured credit portfolio concentrated in AAA-rated CLOs and non-agency CMBS, with selective positions further down the capital structure. Its CLO allocation alone spans more than 80 active deals and over 1,500 obligor exposures in the underlying loans.

That shape fits the business. Long-dated liabilities demand reliable income, so the firm emphasizes highly rated tranches where capital preservation comes before yield. It also carries a measured allocation to mezzanine CLO and CMBS positions, where greater credit risk raises the stakes on understanding the underlying exposures.

Everything in the insurance company investment portfolio traces back to a promise: the income it generates is what funds decades of policyholder payouts. If credit deterioration goes undetected and cash flows fall short, the consequences bleed past any single investment. Catching that deterioration early takes a clear, current view across issuers, industries, property types, and regions, and that visibility is what lets the team manage risk and protect the income the business depends on.

The Challenge

Those mezzanine positions came with a limited risk budget and no tolerance for surprises. The team had to understand the risk it held before a macroeconomic event tested it, not after. That was difficult, because the risk lived several layers down inside each deal.

• ‍Aggregate exposure across deals. The same issuer could sit in the collateral pools of many CLOs at once. Without look-through to the underlying loans and a way to aggregate across deals, the team could not easily answer a question as basic as its total exposure to a single issuer or industry.‍
• Historical manager and collateral behavior. Evaluating a CLO meant understanding how its manager had behaved over time: what concentrations it had held, how they had shifted across cycles, and which past events had moved the collateral pool. For CMBS, the parallel questions were about property types, regions, and each deal's exposure to them.‍
• Speed when the market moves. When an event hit, the team had to reassess the book immediately, while the situation was developing. A process that took days to compile exposure and rerun the numbers delivered its answer after the moment to act had passed.

Deep look-through, historical context, and real-time speed, all at once, were beyond what manual, deal-by-deal spreadsheet analysis could deliver for a book of any size.

The Solution

Valitana Analytics gave the insurer's team the collateral-level look-through and flexible modeling it needed to answer these challenges across the entire portfolio. Rather than analyzing each deal in isolation, the team could evaluate the whole book using its own assumptions and measures of risk.

• ‍Portfolio-wide look-through. The platform aggregates the collateral underlying every CLO and CMBS position. From a single portfolio view, the team measures total exposure by issuer, industry, property type, and region rather than reviewing each deal separately.‍
• Custom metrics built around the firm's questions. Working with the Valitana team, the firm creates metrics tailored to its own risk priorities, so the analysis reflects the concentrations and risk factors it actually tracks rather than vendor defaults.‍
• Real-time scenario modeling. Analysts adjust prices, ratings, and default assumptions for individual loans and see the effects immediately, from marking an entire industry's loans to zero to assigning higher default probabilities to selected issuers.‍
• Deal- and tranche-level impact. Each scenario flows through to the relevant deal-level and tranche-level metrics, showing how valuations and projected cash flows change under each set of assumptions.

Real-Time Analysis During a Software Sector Shock

The value of that capability became concrete in early 2026. Rapid advances in AI agents and workflow automation led investors to question the durability of traditional software business models, with concerns centered on companies that depended on manual workflows, seat-based licensing, or products that newer AI tools might replace. The selloff spread from software equities into the leveraged loan market, pushing many software credits lower. LSEG research found that software-related loans fell by more than seven points between early January and the end of February, against a decline of roughly two points in the broader loan market.

The pressure ran across both broadly syndicated and private credit CLOs, though it varied by portfolio. Software made up roughly 10 to 13 percent of assets in US CLO portfolios, with higher concentrations in some middle-market deals. CLOs holding more software loans, particularly lower-rated or distressed credits, saw greater strain on their junior overcollateralization cushions, mezzanine tranches, and equity, while senior AAA tranches were better insulated by structural protections.

For the insurer, an urgent question followed: how much aggregate exposure did the portfolio have to software issuers, and what would happen if conditions worsened?

Because the firm was already using Valitana Analytics, its team answered in real time rather than through days of deal-by-deal review. Working with the Valitana team, the firm built custom metrics showing the percentage of loans in each deal tied to software and related industries. In under ten minutes, and across every CLO it held, the insurer had a portfolio-wide view of its software exposure, aggregated by issuer and industry rather than trapped inside individual deals.

From there, the team ran scenarios live. At the extreme, analysts modeled every software loan falling to zero. They also built more measured cases in which selected issuers carried higher default probabilities. For each, the platform flowed the assumptions through to deal-level and tranche-level metrics, showing how valuations moved and how projected cash flows changed, the exact information the insurer needed to judge whether the portfolio could keep supporting its liabilities under stress. Analysis that would otherwise have taken days of deal-by-deal work was done while the market was still moving.

The Results

Valitana Analytics changed how the team saw and stress-tested its book. The impact showed up in four areas.

Aggregate exposure on demand

The team could measure exposure to any issuer or industry across more than 80 CLO deals and its CMBS book, looking through to over 1,500 underlying positions. A question that once meant days of manual compilation became a portfolio-wide answer available in real time.

Analysis built around the firm's own view of risk

The team defined custom metrics with the Valitana team, so the numbers reflected the concentrations and industries the firm actually cared about, not generic outputs. When a risk emerged that no standard metric captured, the team could build one and get a portfolio-wide answer the same day.

Real-time stress testing

Analysts changed prices, ratings, and default status on the fly and saw the impact instantly, across dozens of scenarios from a single issuer default to a full software-sector wipeout.

Deal- and tranche-level detail

Every scenario flowed straight through to valuations and projected cashflows at both levels, giving the team what it needed to judge the book against its liabilities.

Hear From The Insurance Company

More Than Analytics: Confidence in the Book

The firm adopted Valitana Analytics to see its structured credit exposure clearly. What it gained was the ability to interrogate its own book on demand, define risk on its own terms, and stress-test the insurance company investment portfolio against the events that matter most, in real time rather than in retrospect.

Trusted by 100+ institutional firms, Valitana is the top choice for CLO investors, hedge funds, and asset managers replacing legacy systems with a permanent financial technology platform for structured product analysis and multi-asset class OMS/PMS.

If your team needs to understand its structured credit exposure across every deal it holds, Valitana can bring the whole book into view. Schedule a demo to see Valitana Analytics on a portfolio like yours.

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