Approve the right credits and catch the risky ones with credit risk decisioning trained on how your equipment finance portfolio actually behaves.
Built for your business means exactly that: not a generic model, not an off-the-shelf score, not SaaS dropped on you at go-live.
It's intelligence embedded in your operation and sharpened with every deal you fund.
In equipment finance, credit underwriting still leans on manual reviews and subjective judgment, using scores built on pooled data that doesn't reflect your own borrower mix or how your portfolio actually behaves. As a result, good applicants get missed, and weaker credits get funded. And as you grow, that inconsistency doesn't scale. It compounds.
Tools built to fit every lender miss the nuances of yours. They're built to capture the common cases, not to understand your unique book. And software that's handed over at go-live never learns the edge cases that make your credit operation uniquely yours.
Credit teams aren't short on tools. What's missing is decision intelligence built for how their business operates.
Three layers. One capability.
Built for equipment finance and leasing lenders who need credit decisions they can defend.
Every application that enters your pipeline (broker-submitted, email, portal, or handwritten) lands in your CRM already captured, extracted, and structured. By the time your team opens it, the data is there and clean. The decision starts from information they can trust.
Now the data is structured. The next question is who it actually belongs to. Counterparty checks run in one place before anything reaches decisioning: OFAC, Secretary of State, FMCSA, TIN, address validation, digital footprint. Whatever enters the scoring layer has already been verified.
The first two layers hand you data that's clean, structured, and verified. The third is where the intelligence earns its keep. Decisions get made on how deals in your portfolio have actually performed, read across asset class, origination mix, and obligor history. Speed isn't the point. Getting the call right is.
Application intake is the work of getting the documents from your application into your system as clean, usable data. Automating that step only moves it faster. Intake integrity goes further: it makes sure the data behind the decision can be trusted before scoring begins. Counterparty verification (KYB) confirms the deal is real before it reaches the model. The risk decision gets made on clean, verified data, using a scoring model trained on your portfolio's historical performance: your own borrower mix, your asset classes, and how those deals actually performed. Every layer exists to improve the quality of the risk decision, not to chase speed metrics or treat AI as a checkbox feature. That sequence is what produces smarter credit decisions.
We embed our engineers and credit risk specialists inside your operation, working alongside your team from day one to solve real business and operational problems with solutions built for your business.
We onboard like a new hire. We get system access, a seat in the conversations that matter, and our hands on the real process. Not to observe from the outside, but to learn how the decisions actually get made inside your operation.
We don't build anything until we understand how your credit operation runs day to day. That knowledge gets embedded directly into the solution, into how it scores, flags, and decides. It's the difference between intelligence that performs and software that just gets installed.
Remote calls and video are convenient, and we use them. But nothing replaces being in the room, so we show up in person, not just dial in. That proximity is what builds trust, surfaces the insights you'd otherwise miss, and makes the work stick.
A project doesn't end when the solution is delivered. We define the success metrics with you up front, default rates, decision quality, the numbers your business lives by, then track and monitor measurable impact against them over time. Delivering that value consistently takes ongoing advisory and support, not a one-time handoff. We hold ourselves accountable to those metrics.
Show us how your underwriting process works
Talk to an expertInside a bank, very decision has to survive scrutiny: CECL, OCC regulations, model risk governance, SOC 2 data controls, the internal audit team. So we build to your risk appetite and your asset mix, and we make every step traceable. Nothing in the decision is a black box.
Captives carry risks a generic model never sees. Dealer concentration. Manufacturer incentives. Residual value that shifts by product line. So we build to your product mix and the channel that brings the deals in.
Independents win by knowing their book better than anyone else. We turn your origination patterns into credit intelligence, and that surfaces the deals bigger players misprice simply because they don't know your niche the way you do.
Fintech and alternative lenders underwrite the business, not the collateral. They read cash-flow data, payment history, bank activity, and sector-specific metrics instead of financial statements. Generic equipment scoring was never built for those signals. So we train on your data and your instruments, and we model how they actually predict repayment, not on asset-backed benchmarks.
A North American lender came to us underwriting every deal by hand. No automated decisioning, and no scoring framework that held steady from one analyst to the next. We embedded in the credit operation, trained an intelligence on their own historical portfolio, and put it to work inside the LOS they already used.
Four years and three calibrations later, the results are auditable and tracked straight from the lender's own data platform.
Underwriting intelligence is a three-layer decisioning architecture designed to improve credit quality at every stage of underwriting. It begins with intake integrity, ensuring every application is captured, extracted, and structured into reliable, decision-ready data. It continues with counterparty verification, where OFAC, Secretary of State, FMCSA, TIN, and digital footprint checks validate the identity behind the application before it reaches scoring. It culminates in risk decisioning, using a scoring engine trained on the actual performance of deals across your portfolio, segmented by asset class, origination channel, and obligor history.
The guiding principle is risk first. The objective is not simply to make decisions faster—it is to make better decisions. Accelerating a flawed credit decision only accelerates the wrong outcome. Underwriting intelligence changes that dynamic by combining clean intake data, verified counterparties, and a portfolio-trained scoring engine configured around the realities of your credit operation.
Tell us about your portfolio and the credit problems you're wrestling with. We'll show you what underwriting intelligence, built around your book, actually looks like.
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