Roll Rate Analysis python package. Both month over month and snapshot roll rate functionalities are supported. It utilizes Polars library for optimization and speed.
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Updated
Jun 17, 2024 - Python
Roll Rate Analysis python package. Both month over month and snapshot roll rate functionalities are supported. It utilizes Polars library for optimization and speed.
Credit Default Approximation for Unsecured Lending Built Machine Learning Classification models (Random Forest, LGBM, XGBoost) in Python to assess the probability of credit defaults.
This app identifies customer default behavior using machine learning as a client server model. This app uses full-stack-starter-template repo to quickly build and deploy data science (or any other) application in cloud.
Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset
RL-Credit-Optimizer is a project that leverages Reinforcement Learning (RL) techniques to optimize credit allocations. The project employs data-driven insights for better financial decision-making, making it a valuable resource for financial institutions and credit analysts.
Our underwriting python module for underwriting credit card accounts. For enterprise partners wanting to do their own underwriting in-house.
Supporting material for the Open Risk Academy course: "Managing Loan Portfolios Using MongoDB"
Source codes and plots for my paper "A Deep Learning Approach to Estimate Forward Default Intensities"
Ranked 10/7198 (Top 1%) One of the models used in Kaggle Home Credit Default Risk.
Classification and regression models for predicting the level of risk associated with extending credit to a borrower and the basic EPS amount respectively.
Supporting material for the Open Risk Academy course: "Concentration Measurement Using Python"
在完成机器学习课程后,自己针对GBDT,XGboost等在反欺诈/反洗钱领域常用的模型再进行了自学所做出的结果,对课上的作业项目代码进行了进一步的提升和优化。
Problem Statement: Based on the customer data, should we give a loan?
Streamlit app to calculate stats and monthly payments of a loan
A credit rating machine learning prediction API.
Reverse engineering of the FICO algorithm
Model calculation with macroeconomic influence - estimate of debtor's probability of going default
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