Webinar: Custom Credit Scoring: Aligning AI with your Lending Strategy

Most lenders already have a credit model. The harder question is whether that model still matches the portfolio it decides on. New verticals, a changed ticket mix and new broker relationships arrive faster than most scoring models get retrained, and the gap that opens is not a data problem. It is a decisioning problem.

This session walks through what it takes to align a credit model with the lending strategy it is meant to serve, and where generic scoring quietly stops working on an equipment finance book.

What the session covers

Why off-the-shelf scoring underperforms on equipment finance portfolios, and the specific signals it cannot see
How a model trained on a lender's own funded-deal history differs from one trained on industry averages
What model decay looks like in practice, and how often retraining actually needs to happen
Keeping every decision explainable and defensible as the model carries more of the work
Where a custom model sits alongside bureau data rather than replacing it

Key takeaways

A model is only as current as the portfolio it learned from. A scoring model built two or three years ago was trained on a book that has since changed shape. It keeps producing confident answers about a portfolio it no longer recognises, which is more dangerous than producing none.

The signals are already in the lender's own data. In transportation and asset-based portfolios, the patterns that precede a default typically surface six to nine months before a missed payment. A generic model cannot see them, because it never trained on a book like that one.

Explainability is not a compliance tax. A plain-language rationale on every decision is what makes an adverse action defensible, and it is also what lets a credit team trust the score enough to act on it at volume.

Start with the decision, not the technology. Speeding up a flawed credit decision does not make it right. It just makes it wrong faster. Smarter first, then faster.

Frequently asked questions

How is a custom credit scoring model different from a bureau score?
A bureau score is anchored in industry averages across a very broad population. A custom model is trained on how deals in one specific portfolio have actually performed, so it reflects that lender's verticals, ticket sizes and risk appetite. The two work together: enriched bureau data feeds the custom model rather than competing with it.

How often does a credit model need retraining?
It depends on how fast the book is changing, but the trigger is portfolio drift rather than the calendar. When a lender enters a new vertical, shifts ticket mix or takes on new broker relationships, the model's training data stops describing the deals it is scoring.

Can a lender do this without fixing their data first?
Yes. Legacy portfolio data almost never arrives clean. Organising, mapping and structuring historical deal data is part of the engagement, not a prerequisite for starting one.