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Credit Risk Modeling in Python
Introduction
What is credit risk and why is it important? (4:43)
Expected loss (EL) and its components: PD, LGD and EAD (4:12)
Capital adequacy, regulations, and the Basel II accord (4:32)
Basel II approaches: SA, F-IRB, and A-IRB (9:32)
Different facility types (asset classes) and credit risk modeling approaches (9:21)
Setting up the working environment
Setting up the environment - Do not skip, please! (0:49)
Why Python and why Jupyter (4:53)
Installing Anaconda (3:02)
Jupyter Dashboard - Part 1 (2:27)
Jupyter Dashboard - Part 2 (5:14)
Installing the sklearn package (1:29)
Dataset description
Our example: consumer loans. A first look at the dataset (3:11)
Dependent variables and independent variables (6:26)
General preprocessing
Importing the data into Python (4:24)
Preprocessing few continuous variables (13:28)
Preprocessing few continuous variables: Homework
Preprocessing few discrete variables (7:09)
Check for missing values and clean (3:20)
Check for missing values and clean: Homework.
PD model: data preparation
How is the PD model going to look like? (3:50)
Dependent variable: Good/ Bad (default) definition (5:18)
Fine classing, weight of evidence, and coarse classing (6:24)
Information value (4:59)
Data preparation. Splitting data (8:27)
Data preparation. An example (8:20)
Data preparation. Preprocessing discrete variables: automating calculations (5:56)
Data preparation. Preprocessing discrete variables: visualizing results (9:35)
Data Preparation. Preprocessing Discrete Variables: Creating Dummies (Part 1) (7:12)
Data preparation. Preprocessing discrete variables: creating dummies (Part 2) (11:15)
Data preparation. Preprocessing discrete variables. Homework.
Data preparation. Preprocessing continuous variables: automating calculations (4:35)
Data preparation. Preprocessing continuous variables: creating dummies (Part 1) (7:20)
Data preparation. Preprocessing continuous variables: creating dummies (Part 2) (14:01)
Data preparation. Preprocessing continuous variables: creating dummies. Homework
Data preparation. Preprocessing continuous variables: creating dummies (Part 3) (12:31)
Data preparation. Preprocessing continuous variables: creating dummies. Homework
Data preparation. Preprocessing the test dataset (4:11)
PD model: data preparation notebooks
PD model estimation
The PD model. Logistic regression with dummy variables (8:21)
Loading the data and selecting the features (5:31)
PD model estimation (3:44)
Build a logistic regression model with p-values. (10:44)
Interpreting the coefficients in the PD model (5:57)
PD model validation (test)
Out-of-sample validation (test). (6:56)
Evaluation of model performance: accuracy and area under the curve (AUC) (11:00)
Evaluation of model performance: Gini and Kolmogorov-Smirnov. (9:59)
Applying the PD model for decision making
Calculating probability of default for a single customer (4:31)
Creating a scorecard (12:54)
Calculating credit score (6:03)
From credit score to PD (3:06)
Setting cut-offs (8:38)
Setting cut-offs. Homework
PD model: logistic regression notebooks
PD model monitoring
PD model monitoring via assessing population stability (4:59)
Population stability index: preprocessing (11:42)
Population stability index: calculation and interpretation (10:46)
Homework: building an updated PD model
LGD and EAD models
LGD and EAD models: independent variables (6:22)
LGD and EAD models: dependent variables (4:51)
LGD and EAD models: distribution of recovery rates and credit conversion factors (5:35)
LGD model
LGD model: preparing the inputs (3:26)
LGD model: testing the model (5:28)
LGD model: estimating the accuracy of the model (5:02)
LGD model: saving the model (3:05)
LGD model: stage 2 – linear regression (4:15)
LGD model: stage 2 – linear regression evaluation (3:37)
LGD model: combining stage 1 and stage 2 (3:14)
Homework: building an updated LGD model
EAD model
EAD model estimation and interpretation (6:08)
EAD model validation (4:27)
Homework: building an updated EAD model
Calculating expected loss
Calculating expected loss (16:26)
Homework: calculate expected loss on more recent data
Our example: consumer loans. A first look at the dataset
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