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Course Curriculum

Introduction
1.1 What is credit risk and why is it important Details 00:00:00
1.2 Expected loss (EL) and its components PD, LGD and EAD Details 00:00:00
1.3 Capital adequacy, regulations, and the Basel II accord Details 00:00:00
1.4 Basel II approaches SA, F-IRB, and A-IRB Details 00:00:00
1.5 Different facility types (asset classes) and credit risk mod Details 00:00:00
Setting up the working environment
2.1 Setting up the environment – Do not skip, please Details 00:00:00
2.2 Why Python and why Jupyter Details 00:00:00
2.3 Installing Anaconda Details 00:00:00
2.4 Jupyter Dashboard – Part 1 Details 00:00:00
2.5 Jupyter Dashboard – Part 2 Details 00:00:00
2.6 Installing the sklearn package Details 00:00:00
Dataset Description
3.1 Our example consumer loans. A first look at the dataset Details 00:00:00
3.2 Dependent variables and independent variables Details 00:00:00
General Preprocessing
4.1 Importing the data into Python Details 00:00:00
4.2 Preprocessing few continuous variables Details 00:00:00
4.3 Preprocessing few continuous variables: Homework Details 00:00:00
4.4 Preprocessing few discrete variables Details 00:00:00
4.5 Check for missing values and clean Details 00:00:00
4.6 Check for missing values and clean: Homework Details 00:00:00
PD Model- Data Preparation
5.1 How is the PD model going to look like? Details 00:00:00
5.2 Dependent variable: Good/ Bad (default) definition Details 00:00:00
5.3 Fine classing, weight of evidence, and coarse classing Details 00:00:00
5.4 Information value Details 00:00:00
5.5 Data preparation. Splitting data Details 00:00:00
5.6 Data preparation. An example Details 00:00:00
5.7 Data preparation. Preprocessing discrete variables: automating calculations Details 00:00:00
5.8 Data preparation. Preprocessing discrete variables: visualizing results Details 00:00:00
5.9 Data Preparation. Preprocessing Discrete Variables: Creating Dummies (Part 1) Details 00:00:00
5.10 Data preparation. Preprocessing discrete variables: creating dummies (Part 2) Details 00:00:00
5.11 Data preparation. Preprocessing discrete variables. Homework. Details 00:00:00
5.12 Data preparation. Preprocessing continuous variables: automating calculations Details 00:00:00
5.13 Data preparation. Preprocessing continuous variables: creating dummies (Part 1) Details 00:00:00
5.14 Data preparation. Preprocessing continuous variables: creating dummies (Part 2) Details 00:00:00
5.15 Data preparation. Preprocessing continuous variables: creating dummies. Homework Details 00:00:00
5.16 Data preparation. Preprocessing continuous variables: creating dummies (Part 3) Details 00:00:00
5.17 Data preparation. Preprocessing continuous variables: creating dummies. Homework Details 00:00:00
5.18 Data preparation. Preprocessing the test dataset Details 00:00:00
PD Model Estimation
6.1 The PD model. Logistic regression with dummy variables Details 00:00:00
6.2 Loading the data and selecting the features Details 00:00:00
6.3 PD model estimation Details 00:00:00
6.4 Build a logistic regression model with p-values Details 00:00:00
6.5 Interpreting the coefficients in the PD model Details 00:00:00
PD model validation (test)
7.1 Out-of-sample validation (test) Details 00:00:00
7.2 Evaluation of model performance: accuracy and area under the curve (AUC) Details 00:00:00
7.3 Evaluation of model performance: Gini and Kolmogorov-Smirnov. Details 00:00:00
Applying the PD Model for decisoion making
8.1 Calculating probability of default for a single customer Details 00:00:00
8.2 Creating a scorecard Details 00:00:00
8.3 Calculating credit score Details 00:00:00
8.4 From credit score to PD Details 00:00:00
8.5 Setting cut-offs Details 00:00:00
8.6 Setting cut-offs. Homework Details 00:00:00
PD model monitoring
9.1 PD model monitoring via assessing population stability Details 00:00:00
9.2 Population stability index: preprocessing Details 00:00:00
9.3 Population stability index: calculation and interpretation Details 00:00:00
9.4 Homework: building an updated PD model Details 00:00:00
LGD & EAD models
10.1 LGD and EAD models: independent variables Details 00:00:00
10.2 LGD and EAD models: dependent variables Details 00:00:00
10.3 LGD and EAD models: distribution of recovery rates and credit conversion factors Details 00:00:00
LGD model
11.1 LGD model: preparing the inputs Details 00:00:00
11.2 LGD model: testing the model Details 00:00:00
11.3 LGD model: estimating the accuracy of the model Details 00:00:00
11.4 LGD model: saving the model Details 00:00:00
11.5 LGD model: stage 2 – linear regression Details 00:00:00
11.6 LGD model: stage 2 – linear regression evaluation Details 00:00:00
11.7 LGD model: combining stage 1 and stage 2 Details 00:00:00
11.8 Homework: building an updated LGD model Details 00:00:00
EAD model
12.1 EAD model estimation and interpretation Details 00:00:00
12.2 EAD model validation Details 00:00:00
12.3 Homework: building an updated EAD model Details 00:00:00
Calculating expected loss
13.1 Calculating expected loss Details 00:00:00
 13.2 Homework: calculate expected loss on more recent data Details 00:00:00

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