Lenders face a constant trade-off: approve more applicants to grow the book, or tighten criteria and miss opportunities. A UK-based financial services firm wanted to improve that balance using data they already held—without sacrificing explainability or regulatory compliance. They engaged YASHICOM to design and deploy a predictive credit risk solution that could integrate with their existing decisioning and reporting systems.
The challenge
The client had legacy scorecards that were stable but rigid: they struggled to capture changing economic conditions and non-linear patterns in borrower behaviour. Manual overrides and policy rules had grown complex, making it hard to explain decisions to customers and regulators. The business wanted a modern ML-based approach that could deliver higher precision, faster time-to-decision, and full auditability for the FCA and internal risk committees.
YASHICOM’s approach
YASHICOM ran a structured discovery to map data sources, existing models, and decision workflows. We then built a risk prediction pipeline using gradient-boosted models trained on historical application and outcome data, with careful feature engineering to align with business logic and regulatory expectations. Model outputs were designed to slot into the client’s existing decision engine, so that underwriters could see both the score and the main drivers behind it.
Explainability was non-negotiable: we implemented SHAP-based explanations and standardised reason codes so that every decline or referral could be clearly justified. YASHICOM also helped the client establish ongoing monitoring for model drift and performance, with a retraining cadence that kept the system aligned with current portfolio behaviour.
Results
After a phased rollout and A/B testing against the legacy scorecard:
- 94% precision on default prediction in the validation cohort, enabling the client to approve more borderline cases with confidence while reducing bad debt.
- Faster lending decisions — automated scoring and reason codes cut average time from application to decision, improving customer experience and operational efficiency.
- Reduced default rates — the new model identified high-risk segments more accurately, allowing targeted pricing and limits without blanket tightening.
Why YASHICOM
YASHICOM specialises in production-grade ML for regulated industries. We delivered not only the models but also the governance, documentation, and monitoring that risk and compliance teams need. If your organisation is looking to modernise risk analytics with machine learning while maintaining control and explainability, we’d be glad to discuss how we can help.