In short: Lenders exploring AI-powered credit risk models typically move through a predictable path, from recognizing the signs their current approach has fallen behind and working through common adoption concerns, to evaluating potential partners and understanding what the ongoing relationship looks like after go-live. Knowing this path in advance makes each stage easier to move through with confidence.
Lenders exploring a new approach to credit risk modeling often start with one specific question, and only later realize it's part of a longer sequence of decisions. Seeing the full path upfront, from first noticing a problem through to an ongoing partnership, makes it easier to know what stage you're actually at and what comes next.
This roadmap reflects the order most lenders naturally move through. Not every lender starts at the same point or moves through every stage in the same way, but you can use this as a guide for deciding how to take the next step in optimizing your lending outcomes.
For many lenders, the starting point isn't "we should look at AI credit models." Often, it's a set of smaller signals that something isn't working as well as it used to, with a noticeable impact on the bottom line.
Recognizing these signs is often what prompts a lender to start looking at alternatives in the first place, or addressing the underlying credit risk analytics gap.
Once a lender starts considering AI-powered credit models, a familiar set of concerns tends to surface.
These concerns are common, and each has a practical answer once a lender looks closely at how a genuinely lender-specific model and partner actually work.
The next decision is which path to take.
"Should we build a model in-house, buy an off-the-shelf score, or partner with a provider?"
When you compare building versus partnering options, this decision usually comes down to team size, timeline, how differentiated the lender's loan book is, and how much ongoing ownership the lender wants to carry themselves. There is an argument for each.
If you're a lender with a highly differentiated book, a specific niche, geography, or borrower segment, you potentially have the most to gain from a model built around your book specifically, rather than a generic score.
We explore this in detail in our article, Build, Buy, or Partner? A Decision Framework for Lenders Choosing a Credit Risk Model, helping you decide the best fit for your business.
Once a lender leans toward partnering, the focus shifts to evaluating specific providers. This is where the right questions matter more than a polished pitch. Questions worth asking include:
Lenders further along in a formal procurement process may also be working through a request for proposal at this stage. It's worth making sure the RFP is built around these ongoing-relationship factors, not just features and price.
Rather than committing to a full rollout immediately, many lenders choose to pilot a new model on a limited segment, or run it alongside their existing process, before deciding to expand further. This might mean applying the new model to one product line, one origination channel, or one borrower segment, and comparing outcomes against the existing model over a defined period.
This staged approach can help build confidence in the solution, using real evidence from performance outcomes based on the lender's own business, as opposed to requiring a leap of faith based on a vendor's claims alone.
A Carrington Labs proof of concept starts with one priority workflow, the place a lender-specific model can have the fastest impact, and runs entirely on de-identified transaction and performance data, so no PII changes hands. From there, the model is built and tested against historical outcomes, giving a direct comparison against the current approach before applying any policy or making a final decision. Once live, the model moves onto an ongoing managed service, with performance monitored and retraining scheduled around the portfolio as outcomes mature, rather than re-quoted as a new build each time.
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Choosing a partner isn't the end point, it's the start of an ongoing relationship. Lenders at this stage benefit from understanding what onboarding involves, what regular support and reporting should look like, and why that ongoing relationship matters as much as the model itself.
This typically includes regular model monitoring and performance reporting, a clear process for retraining or recalibrating the model as the lender's book changes, and a defined point of contact for questions that come up after go-live. Understanding model governance and implementation ensures a partner won't disappear after go-live, leaving a lender carrying the weight the model was meant to reduce in the first place.
Most lenders don't start at Stage 1 and move through every stage in strict order. A lender already deep into a formal procurement process might be squarely at Stage 4. A lender who's piloted with one vendor and is now reconsidering might be somewhere between Stages 3 and 5. The value of seeing the roadmap laid out is less about following it exactly, and more about recognizing where you actually are, and what tends to come next.
Carrington Labs works with lenders at every stage of this roadmap, from those just starting to notice their current model has fallen behind, through to lenders ready to pilot or fully commit to a lender-specific model. Wherever you're starting from, we're glad to help you think through the next stage clearly.
