Credit ScoringModels
Risk scores for lending decisions that you can explain to a regulator, a loan officer and a declined applicant, built on your own repayment data.
Credit Scoring Models: what the work involves
Small lenders, microfinance firms, leasing companies and buy-now-pay-later shops often approve credit with a loan officer's judgement and a few fixed cut-offs. Decisions vary between officers, good borrowers with thin files are turned away, and defaults surface months later with little insight into what went wrong. Thin credit bureau coverage in many markets makes the problem harder.
We build a scorecard from your own history of approved loans and how they performed. After careful definition of default and a time-based split, we compare logistic regression with binning, which is easy to explain, against gradient boosting, which may find more signal. If the gain is small, we recommend the simpler one. Inputs may include application data, repayment behaviour, mobile or bank statement features where consent exists, and bureau data. The output is a score, a probability band and reason codes. A decision policy turns it into approve, refer or decline with limits. Model documentation, fairness checks and monitoring reports are delivered so your risk team can review them.
Core features
Default definition and labelling
We agree exactly what counts as default and build clean outcome labels from your repayment records.
Explainable scorecard option
Binned logistic models give point-based scores that officers and auditors can follow line by line.
Reason codes
Every score lists the main factors lowering or raising it, supporting declined-applicant explanations.
Decision policy layer
Score bands combine with affordability checks and hard rules into approve, refer or decline outcomes.
Fairness and stability checks
Outcomes are compared across groups, and population stability is tracked so changes in applicants are noticed.
Model documentation pack
You receive data definitions, validation results and limitations written for your risk and compliance people.
What we get right before launch
Regulation and responsibility
Lending decisions fall under central bank rules and consumer protection law, which differ by country. The model supports your policy but does not replace compliance review or human accountability.
Selection bias in history
You only know repayment for applicants you approved, so the model never sees how rejected people would have fared. We note this limit and suggest controlled expansion to learn safely.
Sensitive and proxy variables
Gender, religion or ethnicity must not drive decisions, and proxies such as postcode can leak them. We exclude protected data, test for proxy effects, and justify each retained variable.
Tools and technology
- Python
- scikit-learn
- XGBoost
- statsmodels
- FastAPI
- PostgreSQL
- SHAP
- Metabase
Common questions, answered
Can you build a scoring model with no credit bureau data?
Often, using your own repayment history and application data, plus alternative data where applicants consent. Performance depends on the data you hold, and we measure it honestly on a later period before you rely on it.
Will you use a complex model or a simple scorecard?
We test both. If a complex model offers only a small gain, we recommend the scorecard because it is easier to explain, audit and monitor. Lenders usually need that clarity more than marginal lift.
Does the model make final lending decisions?
It can drive straight-through decisions for clear cases if you choose, with borderline applications referred to an officer. The policy and the final accountability remain yours.
How is the model monitored after launch?
We track score distributions, approval rates and repayment performance against expectations, and flag drift. Models are reviewed on a schedule and retrained when the applicant population changes.
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Ready to start your Credit Scoring Models project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.