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Lending technology

Credit underwriting automation

Underwriters re-key the same data into three systems before making a decision, and two underwriters given the same file reach two different answers. We build the engine that makes it consistent.

Review underwriting architecture

What is credit underwriting automation?

Credit underwriting automation applies rules engines, scorecards, and machine learning models to make consistent credit decisions in seconds. Codiot builds underwriting engines that encode your actual credit policy, with the explainability auditors and regulators require, instead of a black box that can't justify its own decisions.
The moment you're probably in

You'll recognize one of these.

The inconsistent decision

Two underwriters look at the same borrower file and reach two different decisions, because the credit policy lives in people's heads, not in a system.

The re-keying tax

Underwriters manually re-enter the same borrower data into three systems before a decision can even be made.

The unexplainable model

A vendor's black-box scoring model makes decisions nobody at your company can actually explain to a regulator or a rejected applicant.

What we build

Credit underwriting automation, end to end.

Sketch: loan documents pooling into a securitization structure
IngestScoreDecisionExplain

Rules engine

Your written credit policy encoded as executable rules, applied the same way every time.

Scorecards and ML models

Statistical or machine learning models layered on top of rules where they add real predictive value.

Explainability and audit trail

Every decision traceable to the specific rules and data that drove it, for regulator and applicant transparency.

Data integration

Credit bureau, bank statement, and income data pulled automatically instead of re-keyed by hand.

Decision dashboards

Real-time visibility into approval rates, decision speed, and policy exceptions.

Fair lending compliance

Model monitoring built to catch disparate impact before a regulator does.

Systems we speak

Your stack, not our preferences.

PythonExperian / Equifax / TransUnionPlaidAWS
Is this right for you?

When it fits, and when it doesn't.

Signs you need this

  • ·Decisions vary by who reviews them, and the reasons are hard to reconstruct.
  • ·Policy changes take weeks because rules are buried in code or people's heads.
  • ·You want to use scores or models but need guardrails and explainability.
  • ·Manual review queues are overwhelmed and slow to clear.

What's included

  • ·A rules engine your risk team can read and change.
  • ·Integration of bureau data, alternative data, and model scores.
  • ·Automated, manual, and hybrid decision paths with review queues.
  • ·Explainability, override tracking, and audit trails.

What's not

  • ·The credit policy and risk appetite, which remain yours to set.
  • ·Model development where you already have a data-science function; we integrate it.
  • ·Final lending decisions and regulatory accountability.

When this isn't the right fit

  • ·Volumes are low enough that manual underwriting is still efficient and consistent.
  • ·Your policy is not yet written down clearly enough to encode.
  • ·You need a decision engine live this week with no integration work.
FAQ

Common questions, answered plainly.

How much does underwriting automation cost?
It depends on whether the engine is rules-based, model-based, or both. We quote a fixed scope after mapping your credit policy.
Can this work alongside our existing underwriters?
Yes, most implementations automate the consistent, rules-based decisions and route genuinely borderline cases to a human underwriter.
How do you ensure regulatory compliance?
Every decision is traceable to the specific rules or model inputs that produced it, which is what regulators and fair-lending audits require.
Rules engine or ML model, which do we need?
Most lenders start with a rules engine encoding their existing credit policy, then layer in ML scoring once there's enough decision data to train a model responsibly.
How long does implementation take?
A rules-based engine covering your core credit policy typically ships in 10-14 weeks; ML scorecards add additional time for model development and validation.
Related

The rest of the stack.

Loan origination system developmentDebt collections software developmentLending industry hubContact
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