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Course - Credit Risk Modeling

https://365datascience.teachable.com/p/credit-risk-modeling-in-python

https://365datascience.teachable.com/courses/629877

https://www.dropbox.com/sh/7oslws1xhsm1zbf/AABkdWDKqpdcGmY1NbXAnkrBa?dl=0&lst=

Introduction​

  • What is credit risk and why is it important?
  • Expected loss (EL) and its components: PD, LGD and EAD
  • Capital adequacy, regulations, and the Basel II accord
  • Basel II approaches: SA, F-IRB, and A-IRB
  • Different facility types (asset classes) and credit risk modeling approaches

Dataset description​

  • Our example: consumer loans. A first look at the dataset
  • Dependent variables and independent variables

General preprocessing​

  • Importing the data into Python
  • Preprocessing few continuous variables
  • Preprocessing few continuous variables: Homework
  • Preprocessing few discrete variables
  • Check for missing values and clean
  • Check for missing values and clean: Homework.

PD model: data preparation​

  • How is the PD model going to look like?
  • Dependent variable: Good/ Bad (default) definition
  • Fine classing, weight of evidence, and coarse classing
  • Information value
  • Data preparation. Splitting data
  • Data preparation. An example
  • Data preparation. Preprocessing discrete variables: automating calculations
  • Data preparation. Preprocessing discrete variables: visualizing results
  • Data Preparation. Preprocessing Discrete Variables: Creating Dummies (Part 1)
  • Data preparation. Preprocessing discrete variables: creating dummies (Part 2)
  • Data preparation. Preprocessing discrete variables. Homework.
  • Data preparation. Preprocessing continuous variables: automating calculations
  • Data preparation. Preprocessing continuous variables: creating dummies (Part 1)
  • Data preparation. Preprocessing continuous variables: creating dummies (Part 2)
  • Data preparation. Preprocessing continuous variables: creating dummies. Homework
  • Data preparation. Preprocessing continuous variables: creating dummies (Part 3)
  • Data preparation. Preprocessing continuous variables: creating dummies. Homework
  • Data preparation. Preprocessing the test dataset

PD model estimation​

  • The PD model. Logistic regression with dummy variables
  • Loading the data and selecting the features
  • PD model estimation
  • Build a logistic regression model with p-values.
  • Interpreting the coefficients in the PD model

PD model validation​

  • Out-of-sample validation (test).
  • Evaluation of model performance: accuracy and area under the curve (AUC)
  • Evaluation of model performance: Gini and Kolmogorov-Smirnov.

Applying the PD model for decision making​

  • Calculating probability of default for a single customer
  • Creating a scorecard
  • Calculating credit score
  • From credit score to PD
  • Setting cut-offs
  • Setting cut-offs. Homework

PD model monitoring​

  • PD model monitoring via assessing population stability
  • Population stability index: preprocessing
  • Population stability index: calculation and interpretation
  • Homework: building an updated PD model

LGD and EAD models​

  • LGD and EAD models: independent variables
  • LGD and EAD models: dependent variables
  • LGD and EAD models: distribution of recovery rates and credit conversion factors

LGD model​

  • LGD model: preparing the inputs
  • LGD model: testing the model
  • LGD model: estimating the accuracy of the model
  • LGD model: saving the model
  • LGD model: stage 2 -- linear regression
  • LGD model: stage 2 -- linear regression evaluation
  • LGD model: combining stage 1 and stage 2
  • Homework: building an updated LGD model

EAD model​

  • EAD model estimation and interpretation
  • EAD model validation
  • Homework: building an updated EAD model

Calculating expected loss​

  • Calculating expected loss
  • Homework: calculate expected loss on more recent data

https://www.openriskmanual.org/wiki/Main_Page

https://www.openriskmanual.org/wiki/Category:Credit_Portfolio_Management

Analytics​

Monitoring or KPIs & flag anything > 5% deviation

Daily​

  • Payment rates (elev8, libr8, sentinel, DSA)
  • FCP rates
  • Fresh collection cases
  • FPD , SPD (7 15 30)
  • Daily dashboard

Weekly​

  • Weekly --> Collection bucket performance trend
  • Weekly --> Flow rates into 90+
  • Weekly --> 0 5 10 15 30 day payment rates
  • Weekly --> FCP % disbursement
  • Weekly --> 0-1 Roll rates
  • Weekly --> Origination Mix
  • Weekly --> Collection efforts (Dialer data)

Monthly​

  • Monthly → Credit committee pack
  • Monthly → Static pools
  • Monthly → Roll rates
  • Monthly → Resolution Rates
  • Monthly → Total expected payment
  • Monthly → Origination Mix
  • Monthly → Loan vintage MIS
  • Monthly → ECM MIS
  • Monthly → BB Collections
  • Monthly → SMS Model MOnitoring
  • Monthly → PSI CSI Monitoring
  • Monthly → Bank Model Monitoring
  • Monthly → BB Collections
  • Monthly → 90+ Recovery

https://www.youtube.com/playlist?list=PLhViQpMavSBgxcLV34bRrJY-rwHqeml2i