Popular GitHub Repositories for Loan

Amazon SageMaker Solution for explaining credit decisions.

Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost

Applying machine learning to predict loan charge-offs on LendingClub.com

A binary classification model is developed to predict the probability of paying back a loan by an applicant. Customer previous loan journey was used to extract useful features using different strategies such as manual and automated feature engineering, and deep learning (CNN, RNN). Various machine learning algorithms such as Boosted algorithms (XGBoost, LightGBM, CatBoost) and Deep Neural Network are used to develop a binary classifier and their performances were compared.

Loan approval prediction is a popular machine learning project, especially in the banking and finance industry. The goal of this project is to build a predictive model that can determine whether a loan application will be approved or not based on the applicant's information such as income, credit history, and loan amount.

This demo provides an automated loan application that streamlines document submission, verification, underwriting and approval processes.

Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.

This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.

Building Classification & Prediction model to classify the Loan applicant request as approved or rejected and then predict the Interest rate for Loan Approval.

A demo pipeline of using Redis as an online feature store with Feast for orchestration and Ray for training and model serving

Python Data Analytics, Machine Learning & Natural Language Processing

An example of using Redis + RedisAI for a microservice that predicts consumer loan probabilities using Redis as a feature and model store and RedisAI as an inference server.

The objective of this project is to determine the risk of default that a client presents and assign a risk rating to each client. The risk rating will determine if the company will approve (or reject) the loan application

A comprehensive machine learning solution for assessing credit risk in the banking sector. This project leverages advanced algorithms to evaluate and classify the creditworthiness of customers, aiming to enhance decision-making in loan approvals and risk management.

Leveraging Federated Learning for Unsecured Loan Risk Assessment on Decentralized Finance Lending Platforms

This repository contains a machine learning-based predictive model for automating loan eligibility assessments. Using features such as demographic details, loan information, and credit history, the model predicts whether a loan should be approved or denied.

datawhale&科大讯飞举办的学习挑战赛————“车辆贷款违约预测挑战赛” Rank4 方案

This is our second project at neuefische DS Bootcamp. Silas Mederer (https://github.com/sls-mdr) and me applied different ML models and for credit default prediction of the P2P platform Lending Club.

AI-driven credit underwriting system combining Machine Learning (ML) & Reinforcement Learning (RL) to optimize loan approvals while managing risk: Credit Risk Prediction via Random Forest model; PPO & DQN for dynamic risk control; Custom OpenAI Gym Environment for simulating real-world lending scenarios & FastAPI real-time processing.

The goal of this project is to build a Deep Learning model using ANN to predict if a person will default on the loan based on the loan and personal information provided.