Automated credit analysis
The decision engine that applies your credit analysis to every application with the same criteria, the same sources and the detail of why each result was reached.
Credit analysis defines how much a lender approves and how much it loses. Done by hand it depends on which analyst reviews the file, how much time they have and which sources they manage to check: two similar applications can end in different decisions. uFlow turns that analysis into an executable policy: it queries the data sources, applies your risk criteria and returns the result along with the detail of how it got there, on every application. The risk team stops resolving case by case and moves to designing and tuning the policy.
From manual review to a policy that runs itself
Manual credit analysis does not scale: every application consumes an analyst's time and the criteria vary between people and between days. Automating it does not mean giving up control over risk, it means writing down once how the analysis works and having that definition applied to every application alike.
- Analysis rules are designed in a visual editor, with no code.
- The same policy applies to every application, with no analyst-to-analyst variation.
- Cases that need human judgement are routed to manual review with the evidence already gathered.
- The risk team adjusts the policy without depending on IT or on the vendor.
Which variables go into the analysis
A credit analysis is only as good as the sources it queries. The engine orchestrates those queries inside the same flow and runs your criteria on top of them, including your own scoring models.
- Credit bureaux and local sources for each country, integrated into the flow.
- Alternative data and open finance to assess applicants with no credit history.
- Internal behaviour: delinquency, payments and active products of your own customer.
- Scorecards and in-house models executed within the same decision.
Why each application was approved or declined
Every application evaluated is recorded with the variables that went in, the policy version that was applied and the result it returned. That is what lets you answer a complaint, hold up to an audit and understand why a portfolio's delinquency moved.
- Per-application record: input data, policy applied and result.
- Policy versioning: it is clear which criteria were in force on any given date.
- An explanation of the result, not just a score.
- Transaction explorer for sampling, complaints and audit.
Test a policy change before it reaches the portfolio
Tightening or loosening credit analysis is a business decision with direct impact on approval rate and delinquency. A new policy can be run against real applications before publishing it, and can coexist with the current one to compare results.
- Simulation of the new policy over applications already evaluated.
- Champion/challenger: two policies in parallel, comparing real results.
- Publishing and rollback of versions with no maintenance window.
What to evaluate before automating your credit analysis
Before comparing features it is worth answering a few questions that define the outcome two years out. Most of them are not about the software, but about who ends up owning the policy.
- Can my risk team change the analysis criteria without the vendor?
- Which data sources for my country are already integrated?
- Can I reconstruct why an application was approved six months ago?
- Can I test a policy change without exposing the portfolio?
- How long does a decision take in the most demanding channel I have?
Everything you need to know
Does automating credit analysis replace the risk team?+
No. It changes what they work on: instead of reviewing application by application, the team designs the policy, tests it and tunes it against portfolio results. Cases that need human judgement are routed to manual review, with the evidence already gathered.
Does it work for consumer and business credit analysis?+
Yes. You configure separate flows per product and segment, each with its own data sources and criteria. The same institution can analyse consumer, SME and corporate lending with independent policies on the same engine.
What if today I run the analysis on a spreadsheet?+
That is the most common starting point. The matrix already describes the policy you have; the work is moving it into the visual editor and, from there, versioning, testing and auditing it, which a spreadsheet cannot do.
How long does analysing an application take?+
It depends on the sources queried, because the time is dominated by each bureau's response. The engine queries in parallel where possible and lets you define what to do if a source does not answer within the expected window.
Let's talk about your decision process
We review how you decide today and show you how it would work in the engine, with your own sources and policies.