How Loan-Level Data Quality Impacts Deal Structuring and Ratings

In receivables-backed finance, deals don’t succeed because the structure looks good on paper. They succeed because the underlying data is correct and supports the transaction.

Loan-level data quality is not “someone else’s problem.” It’s the foundation for confidence: confidence in cash flow, in reporting, in the protections built into the deal, and in the transaction performing as modeled.

Just as important, these deals work best when data quality is treated as a shared responsibility, like a three-legged stool: the borrower, the lender, and the independent reviewer. When all three are engaged, the deal remains stable. When one disengages, issues don’t just surface—they persist.

Why Partnership Matters and What Happens if it Breaks Down

Securitizations and similar financings are straightforward. A company turns future customer payments into cash today, and a lender or investor advances funds based on the expected performance of those receivables. Because repayment depends on the asset pool and the cash flow it generates, everyone is relying on the same thing: accurate, consistent loan-level data.

Each party plays a role:

• Borrowers want reliable funding with minimal disruption.
• Lenders want predictable performance and reporting they can defend internally.
• Independent reviewers validate the data, test what the documents require, and flag exceptions so they can be addressed.

When that partnership is active, the process runs smoothly. When it breaks down, the reason is often that the data is unchecked. An annual review surfaces issues that may have been present for months. The results can be that borrowers get defensive, lenders end up asking questions too late, and everything slows down with more back-and-forth, oftentimes leading to a more conservative outcome than is necessary. The fix is simple: treat data quality and reporting as a shared, ongoing discipline, not a once-a-year fire drill.

Why High-Quality Data is Better for Lenders

Better data helps lenders in practical ways, not theoretical ones.

• Faster decisions: Clean loan-level data reduces follow-up questions, rework, and review time.
• More precise structures: If the collateral performance is clear, lenders can set eligibility, reserves, and advance rates based on facts, not caution.
• Fewer surprises: Issues are addressed before they create covenant concerns, reporting disputes, or credibility problems with credit committees.
• Stronger defensibility: When internal stakeholders ask, “How do we know this is right?” lenders have documentation and validation to support the answer.

The alternative is a structure built with more padding (i.e., lower advances, tighter rules, more restrictions) simply because uncertainty forces conservatism.

Common Loan-Level Data Pitfalls

Many issues aren’t dramatic; they’re operational. Nonetheless, they can still change what is being reported correctly and how a deal should be structured.

Debits and Credits in the Wrong Places

In trade receivables, sales should typically create receivables. But credits often appear in “sales” due to rebates, allowances, or other offsets, reducing the amount that can actually be collected. If a lender advances on the gross balance and customers pay only the net amount, cash flow can fall short of expectations.

Hidden Credits in Aging

A customer may have a credit balance sitting in their account. That credit can offset what they owe, even if the receivable shows as $10,000 at the eligible level. Without loan-level visibility, lenders can be lending against amounts that may be offset by an aged credit that could be used to offset the eligible amount.

Delinquency and Aging Inconsistencies

Days past due, delinquency buckets, and charge-off timing are core drivers of structure and credit protection. If aging reporting is incorrect, rules are applied inconsistently, or policies change without being reflected in the reporting, lenders can’t rely on trend analysis.

Eligibility and Concentration Misses

Deal documents often limit which collateral can be included (asset-type exclusions, APR ranges, geographic concentrations, government receivables, and more). Even minor eligibility exceptions can create problems if they accumulate or go unnoticed.

These are the kinds of items you only see when you get into the weeds of the data tape — sometimes tens or hundreds of thousands of rows — and ask, “Is this behaving the way we think it should be?”

What “Paying Closer Attention” Looks Like

This doesn’t require lenders to become loan-level analysts; it just requires a stronger habit of ownership.

• Read and challenge the monthly reporting. If something looks odd, ask early; don’t wait until the annual review to be the first to notice.
• Make sure the scope matches the deal. The review should test what matters in the transaction and in the deal reporting.
• Support responsiveness. When borrowers are slow to answer questions or provide support, lenders should lean in. Silence is not neutral; it increases uncertainty.
• Treat reviewers as part of the team. The goal is not to “find problems.” The goal is to ensure the structure reflects reality and the data supports the lender’s risk position.

The Takeaway

These transactions are built on trust, but data must validate that trust.

When the borrower, lender, and reviewer work as partners, data interpretation improves, issues are resolved faster, and structures can be set with confidence. When collaboration breaks down, uncertainty grows, and uncertainty is expensive. It shows up in tighter terms, slower timelines, more friction, and more risk for everyone involved.

High-quality loan-level data isn’t just good practice; it’s a competitive advantage for borrowers who want reliable funding and for lenders who want to deploy capital with clarity and control.

Connect with CBIZ Credit Risk Services to validate your loan-level data, surface issues early, and strengthen deal confidence.