Tape cracking is a core diligence function focused on transforming raw originator data into a reliable, decision-ready dataset. It occurs before pricing, structuring, or capital commitment, and its purpose is not optimization or enhancement. The objective is simple but critical: establish confidence in the data itself.
At its core, tape cracking answers a single diligence question: Can we trust this tape to reflect real portfolio behavior, based not only on the data itself, but on the quality, structure, and consistency of how that data is presented?
Before any modeling assumptions or yield projections are applied, investors need assurance that the underlying loan-level data is complete, internally consistent, and representative of how the portfolio has performed.
The first step in tape cracking is standardizing loan tapes into a consistent schema. This includes normalizing dates, balances, statuses, identifiers, and relationships across files that are often delivered in inconsistent or ad hoc formats.
Common tape issues are addressed at this stage, including missing or duplicated loans, inconsistent status logic, and invalid balances or timelines that violate basic loan mechanics. Loan, customer, and transaction data are aligned so that entities reconcile correctly, ensuring that loan-level records can support downstream analysis without distortion.
The goal is not to reshape the data to fit expectations, but to ensure it is structurally sound and analytically usable.
Once data is normalized, a configurable suite of validation tests is applied to assess data integrity. These tests are categorized by severity and risk impact.
Validation checks span missing data, formatting errors, and logical inconsistencies. Tests can be customized by client, asset class, or deal structure, allowing diligence teams to align validation rigor with risk tolerance and investment strategy.
Every pipeline run produces a data health snapshot that is stored historically. Validation results can be downloaded by date or exported in aggregate for internal engineering, risk, or diligence teams.
Reports clearly document what data was excluded, what data was processed with warnings, and where assumptions or gaps exist. This creates audit-style visibility into data quality and eliminates ambiguity around how the dataset was constructed.
For investors, validation reporting provides a defensible record of diligence decisions and data tradeoffs made before capital deployment.
Tape cracking extends beyond static loan attributes into transaction-level behavior. Collections data is mapped directly to individual loans and validated for timing, consistency, and completeness.
This process identifies mismatches between expected amortization and actual borrower cash behavior. Trends in gross collections, net collections, and volatility over time are monitored to surface structural or operational issues that may not be visible at the loan header level.
Reliable cash flow mapping is essential for understanding performance dynamics and for building accurate forward-looking assumptions.
Delinquency buckets are standardized across the portfolio to ensure consistent treatment of roll rates, cures, and charge-offs. This normalization prevents artificial performance distortion caused by inconsistent status definitions or reporting practices.
Observed cash flows are used to build CPR and CDR curves, allowing investors to identify front-loaded risk, seasoning effects, and shifts in borrower behavior over time. These insights are grounded in actual performance rather than modeled expectations.
Loans are grouped by origination vintage and age to enable cohort-based analysis. Comparing vintages helps isolate changes in underwriting quality, macro-driven performance shifts, and operational changes at the originator.
Divergence between early and recent vintages often provides early warning signals that aggregate portfolio metrics can obscure. Cohort analysis supports investment committee diligence by grounding risk discussions in empirical evidence rather than anecdotal explanations.
Flexible filters allow portfolios to be sliced by geography, loan size, term, risk tier, and product type. Investors can stress specific subsets, identify concentrations, and run “what-if” diligence scenarios quickly.
These filters remain reusable post-close, supporting ongoing monitoring and enabling consistency between pre-close diligence and post-close surveillance.
Validation-driven tape cracking surfaces data risk before capital is deployed, reducing diligence friction and eliminating avoidable back-and-forth with originators. It prevents post-close surprises by ensuring that investment decisions are based on verified data, not assumptions.
Most importantly, it establishes a clean foundation for investment committee decisions, ongoing portfolio monitoring, and the ability to scale exposure over time with confidence.
Cascade acts as an independent data and validation layer during diligence, bridging the gap between raw originator data and investor-grade analysis. The focus is not on improving optics, but on transparency, early risk identification, and repeatable, defensible diligence workflows.
By systematizing tape cracking and validation, Cascade enables investors to move faster without compromising data integrity, and to build long-term confidence in both their datasets and their decisions.
