Behind Every Instant Loan Is Data Science: How Python Scorecards Decide Credit Risk
Speaker
Zafarzhon Irismetov
Senior Data Scientist at loan lending business.
Abstract
Modern digital lending demands instant decisions, and behind those decisions is a Data Science workflow powered by scorecard. This talk explains how scorecards calculates credit risk in a transparent and scalable way, from feature engineering to production deployment. Using real examples from our company, models that enable fast, reliable loan approvals.
Description
We will explore how to build credit risk scorecards that are backbones of instant loan decisions in modern financial systems. We’ll break down the full modeling workflow, including feature extraction from raw financial data and the use of binning techniques to create stable, interpretable predictors for credit scoring. A key part of the talk will focus on why logistic regression is the industry-standard algorithm for scorecards highlighting its transparency and robustness under real‑world constraints. Using practical examples from our company, we’ll walk through how scorecards are constructed, validated, and deployed in Python to support fast, explainable, and scalable lending decisions.