ml4frm

Credit Risk analysis by using Python and ML

scikit-learn compatible tools for building credit risk acceptance models

Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management using TensorFlow.

Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.

A python framework for risk scoring

Credit Risk Analysis with Machine Learning

Credit Card Fraud Detection Project

Measure market risk by CAViaR model

Manuel Touyaa's porfotlio of Python projects/assignments for Finance Market Risk.

A mirror of the Open Risk white paper collection