Credit Decisioning Modernisation for a UK Lender

Business Context

The lender processed high monthly application volumes across direct and broker channels. Risk policy interpretation varied by team, and case-level documentation was often inconsistent, creating review delays and quality variance.

Core Challenges

1. Throughput constraints: Manual checks created queue backlogs during monthly demand peaks.
2. Decision variance: Different underwriters could produce different outcomes for similar applications.
3. Governance pressure: Audit and compliance teams required clearer traceability for automated decisions.

Delivery Approach

We delivered a production-ready decisioning framework with four components:

1. Feature pipeline: Automated ingestion, transformation, and quality checks from application and bureau data.
2. Explainable scoring model: Transparent feature contribution outputs for risk team oversight.
3. Decision orchestration: Policy-driven score bands with automated approve/refer/review routing.
4. Monitoring and controls: Drift monitoring, challenger-model testing, and monthly governance reporting.

Implementation Timeline

• Weeks 1-3: Data quality baseline, risk-policy mapping, acceptance criteria.
• Weeks 4-8: Model training, validation, explainability calibration, and scorecard design.
• Weeks 9-11: Integration with underwriting system and controlled shadow mode.
• Weeks 12-14: Gradual go-live, policy tuning, and handover to internal model-risk stakeholders.

Operational Outcomes

The underwriting team improved responsiveness to brokers and direct applicants while reducing avoidable manual reviews. Risk and compliance leaders gained better visibility into decision rationale, model behaviour, and portfolio health movements.

Next-Phase Roadmap

Next phase includes affordability stress modelling, cross-sell propensity signals, and automated adverse-action explanation workflows aligned to compliance requirements.