Credit Risk Scoring & Decision Making by Global Experts

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1 - Introduction

1 -Course Overview.mp4

03:02

2 -Setting Up Your Computer.mp4

00:59

3 -Overview of Credit Risk Models.mp4

09:18

4 -Applications in the Industry.mp4

01:10

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.mp4

02:17

2 -Example Case Presentation.mp4

03:52

3 -Application vs Behavioral Scorecards.mp4

05:06

4 - Dataset Description

1 -Dataset Information.mp4

04:34

2 -Loading data to the Python environment.mp4

02:30

5 - Data Preprocessing

1 -Data Quality Checks.mp4

07:40

2 -Data Cleaning.mp4

07:38

3 -Exploratory Data Analysis.mp4

05:30

4 -Exploratory Data Analysis - Based on Time.mp4

05:42

5 -Sector Best Practices.mp4

02:59

6 - Data Transformation

1 -Data Transformation Methods.mp4

04:00

2 -Data Transformation in Practice.mp4

04:55

3 -Sector Best Practices.mp4

08:56

7 - Data Splitting

1 -Data Splitting Methods.mp4

04:28

2 -Data Splitting In Practice.mp4

04:38

8 - Feature Selection Methods

1 -Overview and Sector Best Practices.mp4

05:29

2 -Correlation Elimination.mp4

03:45

3 -Correlation Elimination In Practice.mp4

03:50

4 -Information Value.mp4

01:21

5 -Information Value in Practice.mp4

01:44

6 -Univariate Gini.mp4

04:28

7 -Univariate Gini In Practice.mp4

04:27

9 - Classical Probability of Default Models

1 -Survival Analysis.mp4

05:11

2 -Survival Analysis In Practice.mp4

04:45

3 -Logistic Regression.mp4

03:25

4 -Logistic Regression In Practice.mp4

05:00

5 -Logistic Regression Model Explainability Methods.mp4

01:30

6 -Logistic Regression Model Explainability Methods In Practice.mp4

01:30

7 -Model Coefficients.mp4

02:55

8 -Logistic Regression - Max Gini Model.mp4

03:50

9 -Logistic Regression - Max Gini Model Predictions.mp4

02:13

10 -K Fold Cross Validation.mp4

04:08

11 -K Fold Cross Validation In Practice.mp4

04:35

12 -Sector Best Practices.mp4

15:56

10 - Feature Selection for Advanced Data Science Techniques

1 -Advanced Feature Importance Overview.mp4

08:19

2 -Random Forest Feature Selection.mp4

03:05

3 -Shapley Values Feature Selection.mp4

03:10

4 -Permutation Feature Importance Selection.mp4

02:35

11 - Advanced Data Science Techniques

1 -XGBoost Overview.mp4

09:58

2 -XGBoost.mp4

04:27

3 -Approximate Coefficients for XGBoost.mp4

02:45

4 -Parameter Tuning for XGBoost.mp4

02:05

5 -Neural Networks Overview.mp4

12:08

6 -Neural Networks.mp4

02:28

7 -Parameter Tuning for Neural Networks.mp4

03:37

8 -Model Ensembling.mp4

03:29

9 -Model Ensembling In Practice.mp4

02:40

10 -Sector Best Practices.mp4

02:26

12 - Model Selection

1 -Model Selection Methodology.mp4

03:03

2 -Model Selection In Practice.mp4

01:54

13 - Rating Scale Development

1 -Rating Scale Overview.mp4

03:32

2 -Rating Scale Generation.mp4

03:32

3 -Score Generation and Scaling.mp4

03:42

4 -Sector Best Practices.mp4

05:00

14 - Model Calibration

1 -Why Model Calibration Needed.mp4

05:39

2 -Bayesian Calibration.mp4

04:03

3 -Regression Calibration.mp4

04:26

4 -Sector Best Practices.mp4

02:25

15 - Model Evaluation and Validation

1 -Model Validation Basics and Sector Best Practices.mp4

06:12

2 -Validation Metrics for Credit Scoring Models.mp4

31:25

3 -AUC ROC.mp4

02:35

4 -Time Series Gini.mp4

03:21

5 -Kolmogorov-Smirnov Test.mp4

02:53

6 -Confusion Matrix.mp4

03:25

7 -Stability Tests - PSI & SSI.mp4

03:00

8 -Variance Inflation Factor.mp4

03:30

9 -Herfindahl-Hirshman Index and Adjusted Herfindahl-Hirshman Index.mp4

02:59

10 -Anchor Point.mp4

02:57

11 -Chi-Square Test.mp4

03:03

12 -Binomial Test.mp4

03:09

13 -Adjusted Binomial Test.mp4

03:13

14 -Model Validation Thresholds.mp4

04:15

16 - Advancements in the Industry

1 -Case Study 1 - U.S. based Financing Company.mp4

05:27

2 -Case Study 2 - UK based Fintech Startup.mp4

05:25

17 - Final Project and Test

1 -Final Project Using Real-World Data.mp4

02:32

Description

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

What You'll Learn?

Build a Comprehensive Credit Risk Model: Participants will learn to construct a complete credit risk model using Python
Preprocess and Analyze Real-World Data: The course will teach how to preprocess and manage real-world datasets, preparing them for modeling and analysis.
Apply Advanced Data Science Techniques: Learners will gain knowledge of advanced data science techniques and how to apply them in the context of risk models
Evaluate and Validate Models: The course covers model evaluation and validation processes to ensure the effectiveness and reliability of credit risk models.
Practical Application and Real-Life Examples: Gain practical knowledge through real-life examples and case studies
Sector Best Practices: Learn industry standards for designing and implementing robust credit risk systems

Who is this for?

Banking Professionals

Finance and Risk Management Students

Aspiring Credit Risk Professionals

Credit Risk Auditors

Entrepreneurs and Business Owners

Data Scientists

What You Need to Know?

Basic Python Knowledge and Enthusiasm to Learn

Basic Math and Statistics

More details

Description
Credit Risk Scoring & Decision Making Course

Are you ready to enhance your career in the financial world by mastering credit risk management skills? Look no further! Our "Credit Risk Scoring & Decision Making" course is designed to equip you with the essential tools and knowledge needed to excel in this critical field.

Who is this course for?

Banking Professionals: If youâ€™re a credit analyst, loan officer, or risk manager, this course will elevate your understanding of advanced modeling techniques.
Finance and Risk Management Students: Gain practical skills in credit risk modeling to stand out in the competitive job market.
Data Scientists and Analysts: Expand your portfolio by learning how to apply your data science expertise to the financial sector using Python
Aspiring Credit Risk Professionals: New to the field? This course will provide you with a solid foundation and prepare you for work life.
Entrepreneurs and Business Owners: Make informed lending or investment decisions by understanding and managing credit risk effectively.

What will you learn?

Build a Comprehensive Credit Risk Model: Construct a complete model using Python, covering key aspects like Probability of Default and scorecards.
Preprocess and Analyze Real-World Data: Learn to handle and prepare real-world datasets for modeling and analysis.
Apply Advanced Data Science Techniques: Understand and apply cutting-edge data science techniques within the context of credit risk management.
Evaluate and Validate Models: Gain skills in model evaluation and validation to ensure reliability and effectiveness.
Practical Application and Real-Life Examples: Engage with real-life case studies and examples to apply your learning directly to your work.
Master Risk Profiling: Accurately profile the risk of potential borrowers and make confident credit decisions.

Why choose this course?

Expert Instruction: Learn from industry experts who have worked on global projects and developed software used on a global scale. Their real-world experience and academic credentials ensure you receive top-quality instruction.
Comprehensive Content: From theory to practical applications, this course covers all aspects of credit scoring models.
Real-World Data: Work with actual datasets and solve real-life data science tasks, not just theoretical exercises.
Career Advancement: Enhance your resume and impress interviewers with your practical knowledge and skills in a high-demand field.
Sector Best Practices: Understand industry standards for designing robust credit risk systems, including data flows, automated quality checks, and advanced reporting mechanisms.

Join us and take the next step in your career by mastering the skills needed to excel in credit risk scoring and decision making. Enroll now and start your journey towards becoming a credit risk expert!
Who this course is for:
Banking Professionals
Finance and Risk Management Students
Aspiring Credit Risk Professionals
Credit Risk Auditors
Entrepreneurs and Business Owners
Data Scientists

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