AI Credit Risk Analysis
Explainable ML for application scoring, behavioural scoring and early-warning signals — with model-risk governance aligned to SBV expectations.
Overview
BancoOS combines bureau, bank and alternative data to score credit risk at origination and continuously across the portfolio, with feature-level explainability.
Industry challenges
- Thin-file borrowers underserved by bureau-only scorecards.
- Behavioural and early-warning signals stuck in silos.
- Manual data prep slows model iteration.
- Regulators expect explainability and MRM discipline.
The BancoOS solution
A unified feature store, ML pipeline and explainability layer that runs alongside your existing scorecards and IFRS 9 staging.
Workflow diagram
Business outcomes
- Score lift over bureau-only models.
- Earlier identification of stressed exposures.
- Faster credit-committee cycles.
- MRM-ready documentation for every model.
Integrations
CIC, core banking, GL, alt-data providers, and BancoOS AP/reconciliation data as risk signals.
Security & compliance
Tenant isolation, encryption, RBAC, model documentation and in-region hosting.
Frequently asked questions
›How does BancoOS improve on bureau-only scores?
By adding behavioural, transactional and alternative data, plus feature-store discipline that lets models pick up early signals bureau data misses.
›Is the model explainable?
Yes. Every score carries feature attributions and the models ship with MRM documentation.
›Does it work for thin-file borrowers?
Yes. Alternative data — cash-flow patterns, mobile signals, supplier data — extends coverage to thin-file segments.
›How is IFRS 9 staging supported?
Behavioural and early-warning signals feed staging inputs; changes are logged for audit.
›Can we run our own models on the platform?
Yes. Bring your own models or use BancoOS templates; both share the feature store and monitoring.
›How is model drift monitored?
Continuous drift, PSI and calibration monitoring with alerts to model owners.
›Which regulatory expectations does BancoOS meet?
Aligned to SBV model-risk guidance, with documentation, versioning and controls.
›How long to deploy?
6–10 weeks for the first product; additional models roll out in weeks.
›Is customer data used to train foundation models?
No. Models are tuned per-tenant with your data staying in your tenant.
›What ROI do banks see?
Typical results: score lift over bureau-only models, earlier detection of stressed exposures and faster credit-committee cycles.
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