Classification model to predict the probability that a customer defaults based on their monthly customer statements using the data provided by American Express.

NYU DS-GA 3001 Final Project

Machine learning model to identify customers that are more likely to default based on employment, bank balance and annual salary.

Logistic regression-based credit scoring model using public Kaggle data, designed for transparent PD estimation, performance evaluation, and teaching or regulatory use cases.

In this project, task is to help banking organization to identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of the project.

Finance and Risk Analytics Project: Predicting credit default risk using machine learning models (Logistic Regression, Random Forest) and assessing stock market risk through historical returns and volatility analysis to guide financial risk management and investment strategies.

End-to-end Credit Risk Analytics project using Home Credit data featuring default prediction, XGBoost modeling, customer risk segmentation, underwriting framework, and Power BI dashboard.

Working with an industrial scale data set to build a classification model to predict credit card default, and help creating a better customer experience for cardholders.

Machine learning project for credit card default prediction using CatBoost, probability calibration, SHAP explainability and cost-sensitive decision thresholds.

A program to take in loan level data and create a model which can predict probability of default

The goal of this project is to perform default prediction for commercial real estate property loans based on 17 variables.

Default-Risk Prediction & Screening at Loan Origination in P2P Consumer Lending, with a Double Machine Learning Extension of the Effects of Longer Terms and High Interest Rates

End-to-end credit risk modeling to predict loan default and support data-driven lending decisions.

Amex Default Prediction

A group assignment on Machine Learning.

Builds predictive models to estimate borrower default probability

AI-powered Loan Decision & Credit Risk Platform with Explainable AI, Risk Governance, Analytics Dashboard, and PDF Reporting built using Streamlit & Machine Learning.

Implementation of "Financial Default Prediction via Motif-Preserving Graph Neural Networks" - Demo application with synthetic financial network generation, structural pattern analysis, and GCN-based risk prediction.

Production-ready credit risk modeling platform built with Streamlit and scikit-learn to predict loan default probability, generate 300–900 credit scores, explain decisions with SHAP, run what-if simulations, batch-score CSV files, and export PDF assessment reports.

Class-imbalance-aware comparison of 5 ML models for predicting credit card default on the UCI Taiwan dataset — EDA, feature engineering, and evaluation.