By Jeffery Hartman, Managing Partner of Fitzgerald Advisors
The Fintech and “Buy Now, Pay Later” (BNPL) sectors have cracked the code on origination. They opened the floodgates, democratized credit, and put billions on the books. But now that those vintages are maturing, we are running into a massive wall in the secondary market: you can’t price modern, high-velocity debt using credit data from 1995.
We are seeing a structural break in how distressed assets are valued. The old static models are failing, and if you are holding Fintech paper, that failure is costing you millions. Here is why the market is shifting toward “Behavioral Liquidity Modeling,” and why it’s the only way to get paid what your portfolio is actually worth.
The “Thin-File” Paradox
For decades, the debt industry priced risk based on three things: the FICO score at origination, the balance size, and the statute of limitations. That model worked fine for credit cards and mortgages because the data was standardized. The borrowers had deep histories.
But the modern Fintech borrower is different. They often fit a “thin-file” profile.
To approve these loans, smart Fintech lenders don’t rely on a simple bureau pull. They use a sophisticated stack of Specialty Finance Data. They combine the power of DataX and Teletrack (both Equifax solutions) to see what the bureaus miss—utility payments, banking cash flow, and short-term lending history. That is how they find the “invisible” prime borrower.
The disconnect happens when it’s time to sell.
When these portfolios go delinquent and hit the secondary market, all that rich, alternative underwriting data usually gets stripped away. The buyers revert to their old habits: judging the asset based on a generic FICO or Vantage score. For a thin-file borrower, that score often comes back as “null” or artificially low.
So, you have a lender using Ferrari-level data to originate the loan, and a buyer using Horse-and-Buggy data to price it. By ignoring the specific attributes used to write the loan in the first place, the market undervalues the asset. Lenders end up taking pennies on the dollar for paper that actually has significant intrinsic liquidity.
Why Static Scores Break Down
If you look at the performance data, a static credit score loses almost all its predictive power once an account is 90+ days past due.
A credit score is a lagging indicator of capacity—can they pay? It tells you nothing about intent—will they pay?
For a BNPL borrower, a low credit score might be their baseline reality. It doesn’t mean they are broke; it means they don’t use credit cards. They might still be highly motivated to pay that $50 installment to keep their account active. Traditional models miss this nuance completely.
Behavioral Liquidity: The Real Metric
To fix this valuation gap, the smartest players are moving to Behavioral Liquidity Modeling. Instead of looking at a static number from six months ago, we use machine learning to look at the velocity of the borrower’s digital life.
In building valuation engines like Debt Catalyst, we found that engagement signals are far better predictors of recovery than bureau data.
- Engagement Latency: When was the last time they logged into the app? An active user is 3x more likely to cure a debt than a dormant one.
- Micro-Payment History: Did they try to pay $10 on a $50 balance? That proves intent, even if capacity is low.
- Sentiment Analysis: What are they saying in the chat logs? Are they asking for time, or are they disputing the charge?
When you ingest these hyper-local data points, you can assign a “Liquidity Score” to an account that a traditional buyer would have priced at zero.
RegTech is no longer optional
Integrating AI isn’t just about squeezing out more yield; it’s about not getting sued. With the CFPB cracking down via Regulation F, the old “spray and pray” collection model is dead.
Modern valuation has to act as a filter. Before a portfolio ever hits the market, we have to audit the Chain of Title and verify the media availability. You cannot sell what you cannot prove. This prevents the sale of “Zombie Debt”—accounts that are legally unenforceable.
When you embed these compliance checks into the valuation, you create a “Clean Data Premium.” Institutional buyers will bid aggressively because they know the assets have been vetted. They aren’t buying a liability; they’re buying a clean asset.
The Future of the Asset Class
Heading into 2026, the line between Banking Tech and Distressed Asset Management is going to disappear. The lenders who win the next cycle won’t just be the ones who originate the most loans; they will be the ones who can exit their non-performing positions efficiently.
We are moving toward total transparency. Platforms like Bank Watch Pro are opening up FDIC data, and valuation engines are exposing the real value of NPLs. The era of selling debt based on spreadsheets and averages is over. The era of selling debt based on algorithmic truth is here.
About the author
Jeffery Hartman is a Distressed Asset Market Architect and the Managing Partner of Fitzgerald Advisors. He is the founder of Debt Catalyst, an AI-driven valuation platform for the Fintech sector, and Bank Watch Pro, a banking intelligence tool. He advises institutional lenders on portfolio disposition and recovery strategy.
Editor’s note
This article is published as a Guest Insight. The views and opinions expressed are solely those of the author and do not necessarily reflect the views of FinTech Industry Examiner or its editors. This piece is published as provided by the author and FinTech Industry Examiner has not independently verified the underlying data, claims or product references. Nothing in this article should be construed as investment, legal or regulatory advice.
