Credit Scoring & Underwriting Systems
Move from spreadsheet scores to production underwriting: feature stores from bank and card data, policy engines, human-in-the-loop, and explainability that regulators and customers expect.
Format: 2 weeks · instructor-led or self-paced options · certificate of completion · examples from our production builds (Meras, Infinipi, and others).
What you will be able to do
- Separate credit risk, fraud, and operational decline reasons in clean decision traces.
- Engineer features from account and transaction data: income proxies, volatility, obligations.
- Design scorecards + ML blends with champion/challenger and monitoring hooks.
- Implement limits, pricing, and line management with auditable policy versions.
- Map GDPR/Fair lending style duties: adverse action, explanations, disparate impact checks.
- Build case management for exceptions, document verification, and manual underwriter queues.
Syllabus
Week 1 — Data, features & model governance
- Credit products: term loans, BNPL, overdraft, revolving; loss definitions and labels.
- Bureau vs alternative data; open banking categorization and cash-flow features.
- Feature pipelines: PII segregation, encryption, retention, retraining datasets.
- Model families: logistic regression scorecards, gradient boosting, calibration.
- Validation: holdout, time-based splits, population stability, characteristic drift.
- Explainability: SHAP-style summaries vs regulator-friendly reason codes.
- Ethics & fairness: proxy variables, disparate impact testing basics.
Week 2 — Underwriting orchestration & production
- Application orchestration: stages, timeouts, idempotent decisions, retries.
- Policy DSLs vs rules engines; versioning; simulation before rollout.
- Income verification flows: employer APIs, open banking, doc OCR handoff.
- Fraud intersection: synthetic identity, mule patterns, device intelligence handoff.
- Servicing hooks: hardship, restructuring, collections data feedback loops.
- Monitoring: approval rate, vintage curves, early delinquency, data quality SLAs.
- Capstone: decision architecture for a digital lender with OB data and manual review.
Tools & concepts
Capstone
Produce a reference underwriting architecture: data diagram, policy version strategy, and a sample adverse-action explanation template.
Who should attend
Data scientists moving to production, credit PMs, and engineers building lending stacks.
Prerequisites
Intro statistics and SQL; no production ML requirement—we focus on system design.
Apply for this course
Fee $1,700 · 2 weeks. Students receive 50% off with valid ID. We will email payment instructions and next steps after you submit.
Corporate or bulk seats? Contact us. For other courses see all trainings.