UnderwritingIntelligence
Approve the right credits and catch the risky ones with credit risk decisioning trained on how your equipment finance portfolio actually behaves.
Risk-first credit decisioning, built for your business.
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.
The gap nobody fills
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.
Underwriting intelligence
Three layers. One capability.
Built for equipment finance and leasing lenders who need credit decisions they can defend.
Intake Integrity
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.
Counterparty Risk, Verified
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.
Risk Decisioning
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.
The credit decision is a risk decision
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.
Forward deployed
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 join the team.
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.
Deep business understanding.
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.
We show up.
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.
We're accountable for the outcome.
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 expertEvery lender is different. So is every engagement.
Inside 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 decade of expertise inside the industry
Trusted & certified
now, they know
We have been extremely satisfied with the quality of the services provided by Kin as well as the speed with which they complete their work. Crossroads views Kin Analytics as an essential partner to our business, and we value their services and partnership.”
We engaged Kin Analytics in a series of engagements including a design and implementation of a scoring model […]. Each team member we have dealt with at Kin Analytics has been highly intelligent, professional, attentive, thoughtful, and trustworthy.”
I’ve been working with Kin Analytics for 2 years for decision rule book and scorecard development for Deutsche Leasing North America. Cooperation and synergy have been great with fruitful weekly meetings. Deliverables have been always presented on time in a professional and pragmatic manner. Kin Analytics team members are skilled and seasoned, but also very caring and flexible leading us to have a phenomenal customer experience.”
Talk to an expert
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.
Questions credit teams ask
AI improves credit underwriting by making decisions on data that's clean, verified, and specific to your portfolio rather than industry averages. Kin's approach works in three layers: structured intake data, verified counterparties, and a scoring model trained on how deals in your own portfolio have actually performed. The goal isn't faster decisions, it's better ones.
An off-the-shelf score is calibrated to someone else's book; a custom model is trained on yours. Kin builds a credit decisioning capability around your asset classes, borrower mix, and obligor history, configured inside your credit operation. You leave with a working capability built for your business, not a generic tool you plug in.
Kin builds through forward deployment: our engineers and credit risk specialists embed in your operation from day one. Instead of handing over software, we learn how your team makes decisions, organize your legacy portfolio data, map your workflows, and build a model configured to your risk appetite then stay accountable to the success metrics we set together. You leave with a working capability, not a vendor relationship that ends at go-live.
Yes. Kin builds models that meet fair-lending and regulatory requirements, with full model documentation and governance so decisions stay consistent and defensible over time. For CECL specifically, we build PD, LGD, and EAD models on your own loss history, with macroeconomic scenarios for forward-looking expected loss. Kin is also SOC 2 Type 2 certified.
There's no single model that fits every lender. The right framework depends on your data, volumes, growth goals, risk tolerance, and how much explainability you need. Kin builds advanced machine-learning models, expert-judgment scorecards, or statistical scores whichever fits, and always around your credit policy, so every decision is fast, explainable, and defensible. Compared with a generic, off-the-shelf score, a model built on your own portfolio typically delivers around 30% more approvals and 20% fewer defaults.
Yes. Kin integrates seamlessly with the systems and workflows you already use, adapting to your processes and policies instead of forcing you onto a platform. And if your system can't ingest a custom model, we run it on a decision engine.
Kin's KYB checks run real-time verification across sources like OFAC, Secretary of State (SOS), FMCSA/SAFER, TIN verification, address validation, satellite imagery, and digital footprint, with additional sources integrated to match your due diligence process. On intake, we capture and extract applications from your portal, API, email (Gmail/Google Workspace, Outlook), or manual upload, and structure them straight into your system.
You can start with a single layer or deploy all three. Kin's underwriting intelligence is built as three independent layers intake integrity, counterparty verification, and risk decisioning and each can work on its own or together. Most lenders start where the pain is greatest and expand as the value compounds, so you adopt at the pace that fits your operation.