Using Analytics Platforms for Structure Review (Why Your Spreadsheet Is Not Enough)

A single RMBS deal can contain 50+ tranches, each with different coupon types, credit enhancement levels, prepayment allocation rules, and trigger mechanisms. Trying to model the cash flow waterfall in a spreadsheet is how you get blown up by a structural feature you did not know existed (an interest-only strip that evaporates under fast prepayments, a shifting interest trigger that reallocates principal away from your tranche, a clean-up call that gets exercised when you were counting on residual cash flows).

The point is: structured product analysis requires purpose-built analytics platforms. This article walks through the major platforms, what each does well, how practitioners actually use them in deal review, and where the gaps are that still require your own judgment.

The Platform Ecosystem: Who Does What

The structured finance analytics market has consolidated around a handful of dominant platforms, each occupying a distinct niche. Understanding their roles is the first step to building an effective analytical workflow.

Intex Solutions (The Industry Standard for Cash Flow Modeling)

Founded in 1985, Intex Solutions maintains the world’s largest library of structured finance cash flow models, covering over 40,000 ABS, CDO, CMBS, and CMO deals (Intex Solutions, 2025). Every major buy-side and sell-side firm in the structured products market either uses Intex directly or accesses Intex models through a third-party integration.

What Intex does: At its core, Intex takes user-specified assumptions for prepayments (CPR), defaults (CDR), loss severity, delinquency rates, and interest rates, and projects the resulting cash flows for every tranche in a deal. The waterfall logic (the legal rules governing how cash gets distributed among tranches) is hard-coded from the deal’s prospectus supplement and pooling and servicing agreement.

How practitioners use it:

• Scenario analysis: Run a base case (say, 15 CPR, 2 CDR, 40% severity) and stress cases to see how your tranche’s yield, average life, and principal window shift
• Relative value: Compare two tranches across different deals under identical assumptions to identify mispricing
• Trigger analysis: Determine the collateral performance levels at which credit enhancement triggers, turbo provisions, or step-down tests activate or fail

The limitation you must understand: Intex models the deal structure perfectly (the waterfall is replicated from legal documents). But Intex does not tell you what assumptions to use. The CPR, CDR, and severity vectors you input are your responsibility. Garbage in, garbage out remains the binding constraint, even on a world-class platform.

Bloomberg Terminal (The Integration Hub)

Bloomberg’s structured products functionality sits within the broader terminal ecosystem, giving it two advantages that standalone platforms lack: real-time market data integration and cross-asset comparability.

Key Bloomberg functions for structured product review:

Bloomberg also integrates Intex cash flow models directly through the terminal, so you can run Intex scenarios without leaving the Bloomberg environment. The BVAL pricing service uses data from TRACE, MSRB, exchanges, and broker quotes, filtered and verified for quality, and provides a proprietary BVAL Score indicating the amount and consistency of market data behind each evaluated price (Bloomberg, 2025).

Why this matters: when you are pricing a thinly traded mezzanine CMBS tranche, the BVAL Score tells you whether that evaluated price is based on actual observed trades or interpolated from index-level data. A BVAL Score above 8 (on a 1-10 scale) means substantial market evidence; below 4 means the price is largely model-driven.

Thetica Systems (The Intex Power User’s Tool)

Thetica Systems occupies a niche as an Intex partner that builds custom front-end tools on top of Intex’s cash flow engine (Thetica Systems, 2025). Where Intex’s native interface can feel utilitarian, Thetica wraps it in purpose-built workflows for trading desks, portfolio managers, and risk teams.

Thetica’s value proposition:

• Custom dashboards that pull Intex cash flows and overlay them with market data, rating agency surveillance, and portfolio constraints
• Batch processing for running scenarios across hundreds of bonds simultaneously
• Report generation that automates monthly portfolio reviews

The practitioner insight: if you manage a portfolio of 50+ structured positions, the time savings from Thetica’s batch capabilities (compared to running individual Intex scenarios) often justifies the additional cost within the first quarter of use.

Rating Agency Surveillance Platforms

Moody’s, S&P, and Fitch each maintain surveillance platforms that provide ongoing collateral performance data, rating actions, and (critically) the analytical frameworks they use to evaluate deals.

What you get:

• Collateral performance tables: Monthly updates on delinquency, loss, and prepayment metrics by deal
• Rating rationale reports: The specific triggers, coverage tests, and performance thresholds that could lead to upgrades or downgrades
• Peer comparison tools: How your deal’s collateral performs relative to vintage, asset type, and originator cohorts

The test: Rating agency surveillance data is backward-looking. It tells you what happened. The analytics platforms (Intex, Bloomberg) let you project what will happen under different assumptions. You need both. Using surveillance data without forward-looking scenario analysis is driving while looking only in the rearview mirror.

SOLVE and KopenTech (Newer Entrants for CLO and Structured Credit)

The CLO market has spawned dedicated analytics platforms that focus on the specific needs of CLO investors:

• SOLVE integrates Intex models with BWIC data, dealer inventory monitors, and proprietary analytics to give CLO traders a unified view of the secondary market
• KopenTech provides electronic trading infrastructure and analytics specifically for CLO tranches, with BWIC processing and portfolio analytics

These platforms represent the electronification of structured credit (the market’s gradual shift from phone-and-email trading to screen-based workflows).

Worked Example: Reviewing a New-Issue CMBS Conduit Deal

Let’s walk through how you would use these platforms in practice when reviewing a new-issue CMBS conduit deal.

Step 1: Initial screening on Bloomberg (15 minutes)

Pull up the deal on SMBS. Review the capital structure: how many tranches, what credit enhancement levels, which rating agencies participated. Check the LTV distribution of the loan pool (you want the weighted average LTV below 65% for a conduit deal; above 70% demands additional scrutiny). Look at the property type concentration. If office exposure exceeds 25%, flag it immediately given the post-2020 distress in that sector.

Step 2: Cash flow modeling on Intex (45-60 minutes)

Load the deal into Intex. Run three scenarios:

For the tranche you are evaluating, record the yield, average life, and principal window under each scenario. The decision rule: if the yield under moderate stress falls below your cost of funding, the risk-adjusted return does not compensate you.

Step 3: Collateral deep dive using surveillance data (30 minutes)

Pull the originator’s historical performance data from Moody’s or S&P. You want to see:

• Historical default rates by vintage for this originator’s prior CMBS deals
• Property type performance in prior downturns (especially 2008-2010 and 2020)
• Loan modification and special servicing transfer rates on prior deals

Step 4: Relative value on Bloomberg (20 minutes)

Use YAS to compare your target tranche’s spread against:

• Other conduit CMBS deals of the same vintage and rating
• The AAM benchmark showing CMBS conduits pricing at approximately +225 basis points in late 2024 (AAM Company, 2025)
• Investment-grade corporate bonds of similar duration

If your tranche offers a spread premium of 30+ basis points over comparable CMBS with similar collateral quality, you have found potential value. If it trades tighter, ask yourself what you are missing.

Common Analytical Pitfalls (And How the Platforms Help You Avoid Them)

Pitfall 1: Ignoring interest rate paths in floating-rate structures

For CLOs and floating-rate ABS, the forward SOFR curve matters enormously for cash flow projections. Intex allows you to input custom interest rate vectors alongside collateral assumptions. The rule that survives: always run at least three rate paths (forward curve, +200 bps, -100 bps) when analyzing floating-rate structures.

Pitfall 2: Misunderstanding trigger mechanisms

Many ABS and CMBS deals contain performance triggers that reallocate cash flow when collateral deteriorates beyond specified thresholds. Intex’s waterfall models capture these triggers, but you must know to look for them. Common triggers include:

• Overcollateralization (OC) tests in CLOs that divert interest cash flow to pay down senior tranches
• Delinquency triggers in RMBS that end the “step-down” period and lock subordination in place
• Clean-up calls that allow the deal sponsor to collapse the trust when the remaining balance falls below a threshold (typically 10% of original)

Pitfall 3: Over-relying on evaluated pricing

Bloomberg BVAL and third-party pricing services are valuable for portfolio marks, but they have known limitations in structured products. For thinly traded tranches (especially mezzanine and subordinate pieces), evaluated prices can lag actual market levels by 2-5 points during periods of rapid spread movement. Cross-reference evaluated prices with actual BWIC color (the prices dealers share after a BWIC auction) whenever possible.

Platform Cost and Access Considerations

(Note: costs are approximate and vary significantly based on firm size, module selection, and negotiated contracts.)

Tiered Checklist for Platform-Based Structure Review

Essential (minimum analytical standard):

• Run Intex (or equivalent) cash flows under at least three collateral stress scenarios before purchasing any structured bond
• Verify deal waterfall mechanics including triggers, step-downs, and clean-up calls
• Cross-reference evaluated pricing with BVAL Score or equivalent confidence metric
• Review rating agency surveillance data for the originator’s historical vintage performance

High-Impact (materially improves accuracy):

• Build custom interest rate vectors that match your house view (do not just use the forward curve)
• Run batch scenario analysis across your entire structured portfolio at least quarterly
• Monitor trigger proximity (how close is the deal’s actual performance to tripping an OC test or delinquency trigger?)
• Compare BWIC auction results with evaluated marks to calibrate pricing accuracy

Optional (for large or concentrated portfolios):

• Integrate Intex outputs into your firm’s risk management system via API
• Build custom Thetica dashboards for top-of-house portfolio reporting
• Subscribe to multiple pricing services and track the dispersion across them as a liquidity indicator
• Automate trigger monitoring with alert systems for early warning

Your Next Step

Pick one structured bond in your portfolio that you have not run through Intex (or an equivalent cash flow platform) in the last six months. Load it up, run a moderate stress scenario (for RMBS: 6 CDR, 40% severity, 15 CPR; for CMBS: 5% cumulative default, 40% severity), and compare the resulting yield to your original purchase assumptions. If the numbers diverge by more than 50 basis points, your position sizing may need revision.