Building a Credit Risk Scoring Model with Machine Learning

Introduction Credit risk assessment is one of the most critical applications of machine learning in the financial industry. Banks and lending institutions need to evaluate whether a loan applicant is likely to repay their debt or default. In this fourth episode of our Mastering Financial Data Science with Kaggle series, we’ll dive deep into building a robust credit risk scoring model using gradient boosting algorithms. Building on the feature engineering techniques we explored in episode 3, we’ll now apply advanced machine learning models—specifically XGBoost and LightGBM—to predict loan approval outcomes. We’ll also explore feature importance analysis and model evaluation metrics that are essential for production credit scoring systems. Understanding Credit Risk Modeling Credit risk modeling aims to quantify the probability that a borrower will default on their financial obligations. The default probability is typically expressed as: P(default)=11+e−(β0+β1x1+β2x2+⋯+βnxn)P(\text{default}) = \frac{1}{1 + e^{-(\beta_0 + \beta_1 x_1 + \beta_2 x_2 + \cdots + \beta_n x_n)}}P(default)=1+e−(β0​+β1​x1​+β2​x2​+⋯+βn​xn​)1​ Where: –

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