5 End-to-end credit scoring for financial applications: a real-world AI approach

In previous chapters, we laid out the BFSI domain constraints—strict compliance rules, partial or missing data, HPC cost concerns, and the need for transparent credit decisions. Now, we shift from theory to practice, constructing an end-to-end workflow that transforms raw BFSI logs into a stable data mart, trains a credit risk model (XGBoost), and converts probabilities into an industry-standard BFSI credit score. Along the way, we’ll address challenges like daily ingestion, negative amounts or sentinel codes, and producing disclaimers in each step. Although real production systems can be far larger and more complex, our approach highlights the fundamental building blocks: ingestion, transformations, binning, modeling, and final deployment checks.

5.1 Data pipeline: from raw data to credit-ready marts

5.1.1 High-level BFSI data ingestion

5.1.2 Constructing a “credit data mart” for lending

5.1.3 A simple batch pipeline (Airflow)

5.1.4 Feature stores: evolving from batch pipelines to reusable assets

5.2 Setting the stage: realistic data and an end-to-end credit modeling flow

5.2.1 Why tabular modeling dominates in BFSI

5.2.2 Which datasets we’ll use—and why

5.2.3 Our end-to-end approach

5.3 Implementing a basic credit model from scratch

5.3.1 Starting our BFSI pipeline

5.3.2 Data import and quick exploration

5.3.3 WOE & IV calculation

5.3.4 Executing WOE & IV, reviewing results

5.3.5 XGBoost modeling with K-Fold

5.3.6 Visualizing ROC & confusion matrix

5.3.7 Credit score conversion

5.3.8 Packaging and deploying the final BFSI model

5.3.9 Beyond Deployment: What 'Production-Ready' Really Means

5.4 Summary