R-codebase for a scientific research article, titled "Defining and comparing SICR-events for classifying impaired loans under IFRS 9"

R-codebase for a scientific research article, titled "Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework"

End-to-end Credit Risk engine using Python. Achieved 93.04% Cross-Validated Recall and 0.98 ROC-AUC. Implemented advanced preprocessing (Log/Robust Scaling) and SMOTEENN to handle class imbalance. Champion model (Logistic Regression) provides full interpretability for strategic financial risk mitigation. 🏦📈

Discover a comprehensive approach to constructing credit risk models. We employ various machine learning algorithms like LightGBM and CatBoost, alongside ensemble techniques for robust predictions. Our pipeline emphasizes data integrity, feature relevance, and model stability, crucial elements in credit risk assessment.

R-codebase for a scientific research article, titled "Deriving the term-structure of loan write-off risk under IFRS 9 by using survival analysis: A benchmark study"

🎯 Machine Learning Credit Risk Model Advanced credit risk assessment model using logistic regression with WoE transformation. Achieves 0.85 AUROC and 0.71 Gini coefficient for accurate loan default prediction. 📊 Key Metrics: 85% AUROC 98% PR-AUC 0.56 KS Statistic 🛠️ Built with Python, scikit-learn, pandas & imblearn Tags: #MachineLearning

A data generator for credit risk data. The generator creates a dataset with dependent and independent variables.

ML-powered Credit Risk Assessment System with Streamlit deployment and real-time default prediction

Built a Logistic Regression model for loan risk prediction, focusing on credit risk and improving high-risk loan detection.

Full toolkit for credit risk monitoring/validation

Predicting loan defaults using exploratory analysis and logistic regression.

This model predicts if a borrower will pay back a loan or not. Lending institutions use this to make informed decisions on whether to approve a loan or not, manage credit risks, and reduce default related losses.

Credit Risk Modeling using Logistic Regression with imbalance handling

Main tools to lead a credit risk study of a financial institution

A dual-part finance and retail analytics project covering credit default prediction for companies using machine learning (Logistic Regression & Random Forest) and market risk analysis of a five-stock Indian equity portfolio using historical price and return data.

The objective of this project is to build a model to predict probability of a client defaulting a loan.

R-codebase for a scientific research article, titled "The TruEnd-procedure: Treating trailing zero-valued balances in credit data"