Credit Risk Scoring & Decision Making By Global Experts

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

Requirements

Basic Python Knowledge and Enthusiasm to Learn

Basic Math and Statistics

Description

Credit Risk Scoring & Decision Making CourseAre 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 you’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 PythonAspiring 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!

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 Setting Up Your Computer

Lecture 3 Overview of Credit Risk Models

Lecture 4 Applications in the Industry

Section 2: Course Material

Lecture 5 Python codes

Lecture 6 Documents

Section 3: Fundamentals of Credit Risk Scoring

Lecture 7 Introduction to Probability of Default (PD) Models

Lecture 8 Example Case Presentation

Lecture 9 Application vs Behavioral Scorecards

Section 4: Dataset Description

Lecture 10 Dataset Information

Lecture 11 Loading data to the Python environment

Section 5: Data Preprocessing

Lecture 12 Data Quality Checks

Lecture 13 Data Cleaning

Lecture 14 Exploratory Data Analysis

Lecture 15 Exploratory Data Analysis - Based on Time

Lecture 16 Sector Best Practices

Section 6: Data Transformation

Lecture 17 Data Transformation Methods

Lecture 18 Data Transformation in Practice

Lecture 19 Sector Best Practices

Section 7: Data Splitting

Lecture 20 Data Splitting Methods

Lecture 21 Data Splitting In Practice

Section 8: Feature Selection Methods

Lecture 22 Overview and Sector Best Practices

Lecture 23 Correlation Elimination

Lecture 24 Correlation Elimination In Practice

Lecture 25 Information Value

Lecture 26 Information Value in Practice

Lecture 27 Univariate Gini

Lecture 28 Univariate Gini In Practice

Section 9: Classical Probability of Default Models

Lecture 29 Survival Analysis

Lecture 30 Survival Analysis In Practice

Lecture 31 Logistic Regression

Lecture 32 Logistic Regression In Practice

Lecture 33 Logistic Regression Model Explainability Methods

Lecture 34 Logistic Regression Model Explainability Methods In Practice

Lecture 35 Model Coefficients

Lecture 36 Logistic Regression - Max Gini Model

Lecture 37 Logistic Regression - Max Gini Model Predictions

Lecture 38 K Fold Cross Validation

Lecture 39 K Fold Cross Validation In Practice

Lecture 40 Sector Best Practices

Section 10: Feature Selection for Advanced Data Science Techniques

Lecture 41 Advanced Feature Importance Overview

Lecture 42 Random Forest Feature Selection

Lecture 43 Shapley Values Feature Selection

Lecture 44 Permutation Feature Importance Selection

Section 11: Advanced Data Science Techniques

Lecture 45 XGBoost Overview

Lecture 46 XGBoost

Lecture 47 Approximate Coefficients for XGBoost

Lecture 48 Parameter Tuning for XGBoost

Lecture 49 Neural Networks Overview

Lecture 50 Neural Networks

Lecture 51 Parameter Tuning for Neural Networks

Lecture 52 Model Ensembling

Lecture 53 Model Ensembling In Practice

Lecture 54 Sector Best Practices

Section 12: Model Selection

Lecture 55 Model Selection Methodology

Lecture 56 Model Selection In Practice

Section 13: Rating Scale Development

Lecture 57 Rating Scale Overview

Lecture 58 Rating Scale Generation

Lecture 59 Score Generation and Scaling

Lecture 60 Sector Best Practices

Section 14: Model Calibration

Lecture 61 Why Model Calibration Needed?

Lecture 62 Bayesian Calibration

Lecture 63 Regression Calibration

Lecture 64 Sector Best Practices

Section 15: Model Evaluation and Validation

Lecture 65 Model Validation Basics and Sector Best Practices

Lecture 66 Validation Metrics for Credit Scoring Models

Lecture 67 AUC / ROC

Lecture 68 Time Series Gini

Lecture 69 Kolmogorov-Smirnov Test

Lecture 70 Confusion Matrix

Lecture 71 Stability Tests - PSI & SSI

Lecture 72 Variance Inflation Factor

Lecture 73 Herfindahl-Hirshman Index and Adjusted Herfindahl-Hirshman Index

Lecture 74 Anchor Point

Lecture 75 Chi-Square Test

Lecture 76 Binomial Test

Lecture 77 Adjusted Binomial Test

Lecture 78 Model Validation Thresholds

Section 16: Advancements in the Industry

Lecture 79 Case Study 1 - U.S. based Financing Company

Lecture 80 Case Study 2 - UK based Fintech Startup

Section 17: Final Project and Test

Lecture 81 Final Project Using Real-World Data

Banking Professionals,Finance and Risk Management Students,Aspiring Credit Risk Professionals,Credit Risk Auditors,Entrepreneurs and Business Owners,Data Scientists