Credit Risk Scoring
Faster, fairer lending decisions your regulator can follow.
Credit scoring and risk models that combine bureau, transaction and alternative data with modern machine learning. Each model is fully explainable, monitored, and ready for validation.
01 · Overview
Scorecards leave value on the table
Traditional scorecards miss thin-file customers, react slowly to changing conditions, and ignore the behavioural signal in transaction data. Black-box ML isn't the answer either: lenders have to explain every decline, satisfy model validators and prove fair treatment.
Built with
- Gradient boosting
- Alternative data
- Stress testing
Deployment
On-premise, private cloud or in-region.
02 · How it works
Accurate and explainable
- 1
Use more of the signal
Bureau data, account and transaction behaviour and, where permitted, alternative data. Features capture trends in utilisation, income stability and repayment.
- 2
Model for accuracy and clarity
Gradient-boosted ensembles benchmarked against your current scorecard, with monotonic constraints that keep behaviour intuitive.
- 3
Explain every decision
Ranked reason codes for every score, ready for adverse-action notices and underwriter review.
- 4
Stress-test and monitor
Scenario analysis under adverse macro conditions supports IFRS 9 and capital planning. Drift monitoring triggers recalibration.
- Bureau + transactionsApplication, alt data
- Feature pipelineTrends, stability
- Model ensembleXGBoost · LightGBM · NN
- Score + reasonsDecision engine
- MonitoringDrift ↺ recalibration
03 · Capabilities
Where it’s used
- Application scoring for consumer and SME lending
- Behavioural scoring and dynamic credit-limit management
- Thin-file and new-to-credit assessment
- Early warning and collections prioritisation
- Portfolio stress testing and IFRS 9 ECL inputs
04 · Outcomes
What we measure
- Better risk separation
- Gini / AUC and KS vs. incumbent scorecard on out-of-time data
- Higher approval at equal risk
- Approval rate at a fixed bad rate
- Faster decisions
- Share of applications auto-decided; time to decision
- Fair, explainable outcomes
- Reason-code coverage; fairness metrics across groups
Example scenarios
Illustrative- Retail bankAn ML application score running as a challenger to the existing scorecard.
- SME lenderRisk assessment using cash-flow and transaction data.
- Card issuerBehavioural scoring for proactive limit increases and reductions.
- InsurerRisk-based pricing inputs and portfolio stress scenarios.
05 · Governance
Explainability & governance
Controls built in
- Reason codes for every decision; global and local explanations (SHAP)
- Model development and validation documentation
- Fairness testing and bias monitoring
- Champion/challenger framework and versioned model registry
Technology
- Python
- XGBoost
- LightGBM
- CatBoost
- PyTorch
- MLflow
- Airflow
- PostgreSQL
- Snowflake
06 · FAQ
Common questions
Can it run alongside our current scorecard?
Yes. We recommend a champion/challenger set-up, so the new model proves itself on live applications before it takes over.
Will our model validation team accept ML models?
We build with validation in mind: monotonic constraints, stability analysis, documentation and reason codes.
Do we need alternative data?
No. Many gains come from better use of the data you already hold. Alternative data is optional and always subject to your regulatory guidance.
Benchmark your scorecard against modern ML.
We can back-test on your historical data and show you the lift before you commit to anything.
Start a project
