Lending
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
SQL / warehouse patterns
Python feature sketches
Rules engines
Model monitoring
OB transaction taxonomies
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.
Credit Scoring & Underwriting Pakistan — Frequently Asked Questions
Fintech credit scoring uses alternative data — mobile usage, utility payments, e-commerce history, and open-banking transaction data — to assess creditworthiness beyond traditional e-CIB scores. In Pakistan, SBP's consumer finance regulations and e-CIB credit bureau data define the compliance boundaries. Our course covers both the AI modelling and SBP regulatory framework.
Yes — SBP's Consumer Protection Framework and Prudential Regulations set requirements for transparent, fair credit decisioning. Fintechs using AI/ML models must be able to explain decisions to declined applicants and avoid discriminatory proxies. FintechPaa's course covers SBP-compliant model governance and explainability requirements.
Earned credit — including salary-linked lending and buy-now-pay-later — is growing fast in Pakistan. Fintechs integrate with employers via HR APIs to verify income and offer credit against verified salary. Our credit scoring course covers earned credit models, BNPL risk frameworks, and SBP digital lending guidelines for Pakistani fintechs.
Yes — FintechPaa's credit scoring course is designed for both product managers and technical teams. Business participants learn model concepts, risk strategy, and SBP compliance without coding. Technical participants get hands-on model building, feature engineering, and API integration exercises.