Automated Underwriting Software
Automated underwriting software replaces manual credit review with a model that returns a decision in milliseconds. Underwrite.ai builds that model on your own loan history — not a generic score — and every decision comes with the reasons behind it.
Start for FreeAUC figures reflect typical results for custom models built on a lender's own repayment history versus traditional scorecards on the same portfolio. Actual performance depends on your data.
What automated underwriting software does
At minimum, automated underwriting software takes a credit application, combines it with bureau and alternative data, estimates the probability that the loan is repaid, applies your lending policy, and returns an approve, decline, or refer decision — without a human reading the file.
The difference between systems is what produces the estimate. Legacy platforms encode thresholds a credit officer wrote: decline below a score, cap debt-to-income, require a minimum tenure. Those rules are static and blind to how variables interact. A trained model derives the relationships from outcomes you have already observed, and updates as more of them accumulate.
What to look for when evaluating
Custom model, not a generic score. A score sold to every lender in your segment cannot capture what is distinctive about your borrowers. Models built on your own loan tape can.
Explainability that satisfies a regulator, not just a data scientist. If the system cannot produce the reasons for a decline, it cannot generate compliant adverse-action notices. Ask to see reason codes on real declines.
Fair-lending controls. The model should exclude variables that proxy for protected classes, and the vendor should provide disparate-impact analysis so you can review outcomes for bias.
Pricing that tracks usage. Per-decision pricing scales with volume; per-seat or annual licences charge you the same whether you decision a thousand applications or a million.
Time to first decision. Ask how long from handing over data to a working model, and whether there is anything to install.
Accuracy: what the numbers mean
Model quality is usually quoted as AUC — the probability the model ranks a randomly chosen defaulting borrower as riskier than a randomly chosen performing one. Traditional scorecards typically sit around 0.72. Custom nonlinear models regularly reach 0.82–0.85 on the same portfolio.
AUC in isolation is the wrong scoreboard, though. What matters commercially is lift over the score you use today at the approval rate you actually run: how many more applicants you can approve without increasing losses, or how much loss you avoid at your current approval rate. Ask any vendor to demonstrate that on your data before you buy.
Our comparison of AI underwriting and traditional scorecards works through the arithmetic.
Compliance and explainability
Automated decisioning is regulated decisioning. Under FCRA and ECOA, a declined applicant is entitled to the principal reasons for that decision, which means the model must be able to attribute its output to specific inputs.
Underwrite.ai produces adverse-action reason codes for every decision, excludes data that proxies for protected classes, and supplies disparate-impact analysis so lending teams and examiners can review outcomes. We do not deploy black-box models — explainability is a design constraint, not a report generated afterwards.
The platform runs on SOC 2 Type II, HIPAA-eligible infrastructure (Amazon Bedrock).
How deployment works
You provide a loan tape and an application tape. We build a custom model on that anonymized data and return an initial data study showing what the model found and how it performs against your current process.
The model is delivered behind an API that returns a decision in milliseconds. There is no software to install and no infrastructure to run. Evaluation is free for the first 30 days, and pricing afterwards is $2 or less per consumer application or $100 per SMB blended report, with no setup fees or monthly minimums.
Lenders with no historical data can start on industry models — see start-ups and new lenders.
Frequently asked questions
What is automated underwriting software?
Automated underwriting software evaluates a credit application and returns a decision without manual review. It ingests the application and bureau data, scores the probability of default with a statistical or machine-learning model, applies the lender's policy rules, and returns an approve, decline, or refer decision along with the reasons behind it.
How is AI-based underwriting software different from a rules engine?
A rules engine applies thresholds a human wrote — decline below a bureau score, cap debt-to-income at a fixed ratio. It cannot learn from outcomes. A machine-learning model derives the relationships from your own repayment history and captures nonlinear interactions between variables that fixed cutoffs miss. Traditional scorecards typically land around 0.72 AUC, while custom models regularly reach 0.82-0.85 on the same portfolio.
Can automated underwriting software comply with fair lending rules?
Yes, provided the model is explainable. Underwrite.ai generates adverse-action reason codes for every declined application to support FCRA and ECOA requirements, excludes data that proxies for protected classes, and provides disparate-impact analysis so lenders can review outcomes for bias. We do not produce black-box models.
What does automated underwriting software cost?
Underwrite.ai is priced per decision rather than per seat or per year: $2 or less per consumer loan application, and $100 per SMB blended credit assessment report. There are no setup fees and no monthly minimums, and the platform is free to evaluate for the first 30 days.
How long does implementation take?
You send a loan tape and an application tape. We build a custom model on that anonymized data and return an initial data study, then deliver the model behind an API that returns decisions in milliseconds. There is no software to install.
What if we have no historical loan data?
Lenders entering a new product or market can start on industry models built for that segment, then transition to a custom model as their own repayment history accumulates.
See it run on your loan tape
We build a custom model on your anonymized data and show you the lift before you commit. Free to evaluate for 30 days.
Start for Free