Credit Risk Scoring & Decision Making by Global Experts

سرفصل های دوره

Master Credit Risk Scoring with Real-World Data and Advanced Techniques with Sector Best Practices using Python

1 - Introduction

1 -Course Overview

2 -Setting Up Your Computer

3 -Overview of Credit Risk Models

4 -Applications in the Industry

2 - Course Material

1 -final project solution.zip

1 - Documents.html

1 - Python codes.html

3 - Fundamentals of Credit Risk Scoring

1 -Introduction to Probability of Default (PD) Models

2 -Example Case Presentation

3 -Application vs Behavioral Scorecards

4 - Dataset Description

1 -Dataset Information

2 -Loading data to the Python environment

5 - Data Preprocessing

1 -Data Quality Checks

2 -Data Cleaning

3 -Exploratory Data Analysis

4 -Exploratory Data Analysis - Based on Time

5 -Sector Best Practices

6 - Data Transformation

1 -Data Transformation Methods

2 -Data Transformation in Practice

3 -Sector Best Practices

7 - Data Splitting

1 -Data Splitting Methods

2 -Data Splitting In Practice

8 - Feature Selection Methods

1 -Overview and Sector Best Practices

2 -Correlation Elimination

3 -Correlation Elimination In Practice

4 -Information Value

5 -Information Value in Practice

6 -Univariate Gini

7 -Univariate Gini In Practice

9 - Classical Probability of Default Models

1 -Survival Analysis

2 -Survival Analysis In Practice

3 -Logistic Regression

4 -Logistic Regression In Practice

5 -Logistic Regression Model Explainability Methods

6 -Logistic Regression Model Explainability Methods In Practice

7 -Model Coefficients

8 -Logistic Regression - Max Gini Model

9 -Logistic Regression - Max Gini Model Predictions

10 -K Fold Cross Validation

11 -K Fold Cross Validation In Practice

12 -Sector Best Practices

10 - Feature Selection for Advanced Data Science Techniques

1 -Advanced Feature Importance Overview

2 -Random Forest Feature Selection

3 -Shapley Values Feature Selection

4 -Permutation Feature Importance Selection

11 - Advanced Data Science Techniques

1 -XGBoost Overview

2 -XGBoost

3 -Approximate Coefficients for XGBoost

4 -Parameter Tuning for XGBoost

5 -Neural Networks Overview

6 -Neural Networks

7 -Parameter Tuning for Neural Networks

8 -Model Ensembling

9 -Model Ensembling In Practice

10 -Sector Best Practices

12 - Model Selection

1 -Model Selection Methodology

2 -Model Selection In Practice

13 - Rating Scale Development

1 -Rating Scale Overview

2 -Rating Scale Generation

3 -Score Generation and Scaling

4 -Sector Best Practices

14 - Model Calibration

1 -Why Model Calibration Needed

2 -Bayesian Calibration

3 -Regression Calibration

4 -Sector Best Practices

15 - Model Evaluation and Validation

1 -Model Validation Basics and Sector Best Practices

2 -Validation Metrics for Credit Scoring Models

3 -AUC ROC

4 -Time Series Gini

5 -Kolmogorov-Smirnov Test

6 -Confusion Matrix

7 -Stability Tests - PSI & SSI

8 -Variance Inflation Factor

9 -Herfindahl-Hirshman Index and Adjusted Herfindahl-Hirshman Index

10 -Anchor Point

11 -Chi-Square Test

12 -Binomial Test

13 -Adjusted Binomial Test

14 -Model Validation Thresholds

16 - Advancements in the Industry

1 -Case Study 1 - U.S. based Financing Company

2 -Case Study 2 - UK based Fintech Startup

17 - Final Project and Test

1 -Final Project Using Real-World Data

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تولید کننده: Udemy