Automated credit underwriting software
Move underwriting from manual review to automated credit assessment, keeping the criteria explicit, versioned and reconstructable decision by decision.
Automated underwriting fails for a predictable reason: the criteria get buried. Rules end up in application code, a scorecard lives in an analyst's file, and nobody can say with certainty what the institution actually applied last quarter. uFlow automates credit assessment while keeping the policy as an explicit, versioned object that the risk team owns, so automation increases throughput without costing you the ability to explain a decision.
From manual analysis to automated credit assessment
The engine receives the application, queries the credit bureaus and internal sources it needs, runs the eligibility rules, executes the scorecard or model, applies the cutoffs and returns an approval, a rejection or a routing to manual review, with the assigned limit and conditions where those apply.
Manual review does not disappear — it gets aimed. Instead of every file passing through an analyst, only the cases your policy explicitly routes there arrive, with the data already gathered and the reason for the referral stated. Analysts spend their time on the files where judgment actually changes the outcome. The mechanics of how the evaluation is assembled are covered in the decision engine overview.
- Automatic data gathering before any human sees the file.
- Rules, decision tables, scorecards and models in a single evaluation.
- Explicit routing to manual review, with the reason recorded.
- Individual online evaluation or batch processing for portfolio rescoring.
Your existing models keep running
Institutions arrive at this evaluation with models they have already built and validated, and understandably do not want to rebuild them. The engine executes machine learning models without your team maintaining the underlying infrastructure, so a model your data science team owns becomes one more component the credit policy calls.
That also means the boundary stays clean: the model produces a score, the policy decides what to do with it. When a cutoff moves, that is a policy change with its own version and its own history — not a model retraining, and not a release.
- Execution of machine learning models without maintaining your own infrastructure.
- Scorecards and models invoked as components of the credit policy.
- Cutoffs and segmentation versioned separately from the model itself.
Automation you can still explain
The reason a supervised institution hesitates to automate is not technical. It is that an automated decision still has to be defensible to internal audit, to a complaints process and to the regulator. An engine that decides but cannot show its work moves the problem rather than solving it.
Every transaction uFlow evaluates is recorded with its input variables, the policy version applied and the result, and can be located afterwards in the transaction explorer. Combined with automatic versioning, that is what makes it possible to state which criteria were in force on a given date and reconstruct an individual case. Our security page covers the controls around that data, including ISO/IEC 27001:2022 certification.
- Complete per-decision record: input, rules applied and result.
- Automatic policy versioning with a full change history.
- Transaction explorer for review, sampling and case reconstruction.
- ISO/IEC 27001:2022 certified, with encryption in transit and at rest.
Implementation and the path to autonomy
uFlow is delivered as SaaS and reaches production in weeks. The work is an API integration with your origination flow or channels, connection of the data sources you use, and configuration of your current policy alongside your risk team so that the starting point is what you already apply today, not a generic template.
The goal of the rollout is that your team leaves it able to operate on its own. After go-live, a policy change is made in hours by the risk area in self-service, rather than waiting on an IT release cycle. That is the difference worth testing in a pilot: not whether the engine can decide, but whether your people can change what it decides. Real examples are in our case studies.
- SaaS delivery, in production in weeks.
- Your current policy configured as the starting point.
- Policy changes in hours, in self-service, once you are live.
Everything you need to know
How long does implementation take?+
uFlow is SaaS and goes into production in weeks. The timeline depends mostly on your side: how many data sources need connecting, how complex the current policy is, and how quickly the API integration with your origination flow can be scheduled. We size it against your specific case before any commitment.
What happens to our existing scorecards and models?+
They keep running. The engine executes machine learning models without your team maintaining the infrastructure, and scorecards are invoked as components of the credit policy. Cutoffs and segmentation sit in the policy layer, versioned separately, so moving a cutoff is not a model change.
Does automated underwriting eliminate manual review?+
No, and it should not. Your policy defines which cases are routed to an analyst and records why. Those files arrive with the bureau and internal data already gathered and the referral reason stated, so review effort concentrates on the cases where judgment changes the outcome.
How do we prove an automated decision was correct?+
Each evaluated transaction is stored with its input variables, the policy version applied and the result, and can be found later in the transaction explorer. Because policies are versioned automatically, you can establish which criteria were in force on a given date and reconstruct an individual case.
Start growing with uFlow
Transform your credit assessment process with the decision engine.