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

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

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

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.

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.

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

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.

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.

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

A group assignment on 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.

Python

Amex Default 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.

Builds predictive models to estimate borrower default probability

Comparative credit default prediction study using logistic regression, decision trees, neural networks, and gradient boosting with class-imbalance-aware evaluation.

Credit risk scoring and limit engine for digital lending. MLP neural network scoring 891K bank customers with risk categorization, proxy income estimation, and credit limit determination.

Predictive analytics project for loan default risk using deep learning. Compares ANN with Random Forest and XGBoost on LendingClub data, achieving high accuracy in identifying potential defaulters.