4 Risk & compliance – credit risk fundamental

This chapter covers

Exploring the credit lifecycle
Organizing data and domain layers
Employing metrics and transformations
Tracking time-based credit performance
Integrating domain knowledge with AI

Finance, in many languages, conveys the essence of settling debt or circulating capital for future repayment. In Latin, the root term can be traced to finis, denoting an arrangement to “conclude” a debt, and in East Asian contexts, “金融” (finance) signifies the circulation and management of money. At its heart, every financial system hinges on extending credit—an organized mechanism of lending funds today with the promise of repaying tomorrow, often with interest. Whether one is dealing with consumer loans or sophisticated capital structures, the central question remains: How can we be confident we’ll get repaid?

4.1 From trust to scoring: how credit took shape

4.1.1 The Evolution from Local Judgments to Standardized Scoring

4.1.2 Core Pillars: Willingness, Capacity, and the Concept of Interest

4.1.3 Modern Channels: BNPL, Micro-Lending, and Merchant Cash Advances

4.2 Core Domain Concepts and the Credit Lifecycle

4.2.1 The Loan Lifecycle: Personal and Corporate

4.2.2 Time-Based Performance and Survival Analysis

4.2.3 Regional and Product Nuances

4.3 A Domain-Driven 4-Layer Framework

4.3.1 Data Assets Layer

4.3.2 Model Layer

4.3.3 Strategy & Monitoring Layer

4.3.4 Application Layer

4.3.5 Why This Matters and Where We’re Going Next

4.4 Essential Metrics and Techniques in Credit Scoring

4.4.1 Revisiting Core ML Metrics—KS, AUC, and PSI

4.4.2 Binning and Discretization: Fine Classing vs. Coarse Classing

4.4.3 Weight of Evidence (WoE) and Information Value (IV)

4.4.4 Handling Special Values, Missing Data, and Outliers

4.5 Why AI Is Now Indispensable—and How It Ties Back to Building Finance Applications