In the highly complex and data-driven world of asset-backed loans (ABL), even the smallest errors can lead to massive overstatements of collateral—sometimes exceeding a million dollars. These "phantom" borrowing bases can distort risk assessments and lead to unintended exposures, all stemming from tiny data discrepancies.
Most service providers, whether treasury management firms or loan administrators, do not perform the core calculations themselves. Instead, they depend on third-party data providers and automated systems to supply the critical figures: the outstanding principal balance of receivables and the days past due (DPD). Given the sheer volume of daily data—transactions, reversals, updates—manual validation is practically impossible. That’s why specialized data companies are essential, using sophisticated validation processes to maintain accuracy.
Why Small Errors Are So Dangerous
With thousands or millions of data points flowing daily, tiny inaccuracies can slip through unnoticed. Over time, these discrepancies can compound, creating seriously inflated collateral values that aren’t rooted in reality. And in large, long-term facilities, these small errors can go undetected for months or even years.
Consider a facility that operated for over a year, generating more than 15 gigabytes of raw data files. Every day, the originator sent two files to the lender:
The automated system that produced these files had a subtle flaw:
However, instead of reversing the split, the system mistakenly inserted the full $100 into principal during the reversal. This incorrect entry increased the outstanding balance by $100, and because the reversal didn't properly split the payment, an extra $10 of principal remained unadjusted.
Over thousands of transactions, these small errors accumulated. After over a year and countless files, the false collateral value—based on these inflated balances—surpassed $1 million. Neither the lender nor the originator recognized the discrepancy until a thorough reconciliation uncovered the error.
This example underscores the critical need for dedicated data validation and reconciliation services. Relying on manual checks or simple pivot tables isn’t enough to catch systemic errors in large, complex data sets. Automated validation tools like Cascade can identify these discrepancies immediately, making errors obvious before they grow into significant financial risks.
The bottom line: Tiny data errors can create massive, hidden risks. Employing advanced validation systems is essential to ensure data integrity and prevent phantom collateral from slipping through unnoticed.
Don’t let tiny data errors inflate your borrowing base. See how Cascade’s automated validation tools catch discrepancies before they cost you millions.
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