Basel, IRB, IFRS 9 , Stress Testing, CCAR, DFAST, Loss given default, Exposure at default, Prepayments, Scorecards, Margin of Conservatism, Age Period Cohort analysis, Survival analysis, Segmentation

1. BOOTCAMP IN CREDIT RISK
MODELLING
BY Peaks2tails
COURSE CONTENT
W W W . P E A K S 2 T A I L S . C O M

2. W W W . P E A K S 2 T A I L S . C O M
1. Data Preparation-
Regression Pipeline
3. Building Application
Scorecards
2 Data Preparation
Classification Pipeline
4. Variable Clustering
5. Reject Inferencing
6. Segmentation
7. Master Rating System
9. Behavioural
Scorecards
8. Vintage & Roll Rate
Analysis
10. LGD Modelling
11. CCF & EAD
Modelling
12. IFRS 9- Staging, ETC
To PIT PD
13 . CECL Aggregate
Models
15. PIT LGD and EAD
14. . Wholesale Models
16. Prepayment
Modelling
17. Low Default
Portfolio
18. Actuarial Credit Risk
Models
19. CCAR and PPNR
Modelling
20. Model Validation
Techniques
22. Machine Learning
for Credit Risk
23. Advanced
Regression Model
24. Corporate Credit
Risk Models
INDEX
21. Margin of
Conservatism

3. W W W . P E A K S 2 T A I L S . C O M
1.1 Regression Master Pipeline

4. 1.2 Regression Master Pipeline
W W W . P E A K S 2 T A I L S . C O M

5. 1.3 Exploratory Data Analysis
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6. 1.4 Regression Missing Value Imputation
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7. 1.5 KNN Missing Value Imputation
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8. 1.6 Cardinality Reduction
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9. 1.7 Categorical Encoding
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10. 1.8 Box Cox Transformation
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11. 1.9 Outlier Engineering
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12. 1.10 Scaling
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13. 1.11 Variable Selection
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14. 1.12 Backward Selection
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15. 1.13 Forward Selection
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16. 1.14 Ridge Regression
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17. 1.15 Model Building
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18. 1.16 Hyperparameter Tuning
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19. 2.1 Classification Master Pipeline
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20. W W W . P E A K S 2 T A I L S . C O M
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2.2 Classification Master Pipeline

21. W W W . P E A K S 2 T A I L S . C O M
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2.3 Classification Master Pipeline

22. W W W . P E A K S 2 T A I L S . C O M
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2.4 SMOTE Over Sampling

23. W W W . P E A K S 2 T A I L S . C O M
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2.5 Adaptive Synthetic Method

24. 2.6 Random Under Sampling
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25. 2.7 Condensed Nearest Neighbours
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26. 2.8 Tomek Links
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27. 3.1 Information Value
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28. 3.2 Weight Of Evidence Tool
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29. W W W . P E A K S 2 T A I L S . C O M
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3.3 Weight Of Evidence

30. 3.4 Stepwise Logistic Regression
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31. 3.5 Scores Normalisation
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32. 3.6 Checking Discriminatory Power
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33. 3.7 Bayes Decision Rule
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34. 3.8 Minimax Decision Rule
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35. 3.9 Neyman Pearson Rule
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36. 4.1 Variable Clustering PCA
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37. W W W . P E A K S 2 T A I L S . C O M
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4.2 Variable Clustering PCA

38. W W W . P E A K S 2 T A I L S . C O M
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4.3 Variable Clustering PCA

39. 5.1 Reject Inference KGB Model
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40. 5.2 Hard Cut-Off
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41. 5.3 Parcelling
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42. 5.4 Fuzzy Augmentation
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43. 5.5 KNN Inferencing
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44. 6.1 Segmentation OLTV
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45. W W W . P E A K S 2 T A I L S . C O M
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6.2 Segmentation OFICO

46. W W W . P E A K S 2 T A I L S . C O M
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6.3 Segmentation ILTV

47. W W W . P E A K S 2 T A I L S . C O M
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6.4 Segmentation Geography

48. W W W . P E A K S 2 T A I L S . C O M
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6.5 Segmentation Delinquent

49. W W W . P E A K S 2 T A I L S . C O M
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6.6 Segmentation MOB

50. W W W . P E A K S 2 T A I L S . C O M
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6.7 Segmentation Loan Size

51. W W W . P E A K S 2 T A I L S . C O M
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6.8 Segmentation Using Decision Trees

52. W W W . P E A K S 2 T A I L S . C O M
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6.9 Segmentation Using Decision Trees

53. 7.1 Master Rating System
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54. W W W . P E A K S 2 T A I L S . C O M
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7.2 Validating Master Rating System

55. W W W . P E A K S 2 T A I L S . C O M
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7.3 Mapping Internal & External Ratings

56. 8.1 Roll Rate Analysis
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57. 8.2 Vintage Analysis
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58. 8.3 Seasoning Analysis
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59. 9.1 Data Preparation Under Instant Cure & Probationary Period Method
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60. 9.2 Behavioural Scorecards
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61. W W W . P E A K S 2 T A I L S . C O M
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9.3 Behavioural Scorecard Logistic Regression

62. W W W . P E A K S 2 T A I L S . C O M
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9.4 Behavioural Scorecards WOE & IV

63. 9.5 Out Of Sample Validation
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W W W . P E A K S 2 T A I L S . C O M

64. 9.6 Out Of Time Validation
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65. 10.1 LGD Workout
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66. 10.2 LGD Decision Tree
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67. 10.3 LGD Cooling Off Period
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68. 10.4 LGD Bimodal
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69. 10.5 LGD Tobit
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70. 10.6 LGD NLS Regression
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71. 10.7 LGD Fractional Logit
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72. 10.8 LGD Fractional & Beta
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73. 10.9 LGD Logit & Simplex
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74. 10.10 RR Extrapolation- Chain Ladder
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75. 11.1 CCF Calculation
W W W . P E A K S 2 T A I L S . C O M

76. 11.2 EAD Bimodal
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77. 11.3 CCF- ULF
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78. 11.4 CCF- LF
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79. 11.5 CCF- BF
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80. 11.6 CCF- AUF
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81. 12.1 IFRS Low Risk
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82. 12.2 IFRS Staging
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83. 12.3 Absolute VS Relative Threshold
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84. 12.4 Vasicek Model
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85. 12.5 Extracting Z
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86. 12.6 PIT Default Rates
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87. 12.7 PD Curve Smoothening
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88. 12.8 TTC To PIT Calibration
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89. 12.9 Extracting Z Scores From Transition Matrix
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90. 12.10 TTC TM To PIT TM
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91. 13.1 Aggregate Models- Flow Rates
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92. W W W . P E A K S 2 T A I L S . C O M
13.2 Aggregate Models- Snapshot/Open Pool Methods

93. W W W . P E A K S 2 T A I L S . C O M
13.3 Aggregate Models- Warm Method

94. W W W . P E A K S 2 T A I L S . C O M
13.4 Aggregate Models- Vintage Methods

95. W W W . P E A K S 2 T A I L S . C O M
13.5 Aggregate Models- State Transition Models

96. 14.1 Wholesale Models- Building TM Using Cohort
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97. W W W . P E A K S 2 T A I L S . C O M
14.2 Wholesale Models- Building TM Using Duration Approach

98. 14.3 TM Smoothening
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99. 14.4 TM Central Tendency
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100. 14.5 TM Mobility Index
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101. 14.6 TTC To PIT TM
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102. W W W . P E A K S 2 T A I L S . C O M
14.7 TTC To PIT TM in Vasicek Space

103. W W W . P E A K S 2 T A I L S . C O M
14.8 TTC To PIT TM in Probit Space

104. W W W . P E A K S 2 T A I L S . C O M
14.9 TTC To PIT TM in Logit Space

105. W W W . P E A K S 2 T A I L S . C O M
14.10 Yearly TM To Quarterly TM

106. W W W . P E A K S 2 T A I L S . C O M
15.1 PIT LGD- Jacob Frye

107. W W W . P E A K S 2 T A I L S . C O M
15.2 PIT LGD- Jacob Frye

108. 15.3 PIT CCF Regression
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109. 15.4 EAD Term Structure
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110. 15.5 EAD For Credit Cards
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111. 16.1 Logistic Regression for Prepayment Modelling
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112. 16.2 Competing Risk Modelling
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113. 17.1 LDP Binomial Approach
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114. 17.2 LDP Pluto Tasche Approach
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115. 17.3 LDP Bayesian Approach
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116. 17.4 LDP CAP Approach
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117. 17.5 LDP Comparisons
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118. W W W . P E A K S 2 T A I L S . C O M
18.1 Actuarial Models- Lee Carter

119. W W W . P E A K S 2 T A I L S . C O M
18.2 Actuarial Models- APC Analysis

120. W W W . P E A K S 2 T A I L S . C O M
18.3 Actuarial Models- Kaplan Meir

121. W W W . P E A K S 2 T A I L S . C O M
18.4 Actuarial Models- Cox Regression

122. 18.5 Actuarial Models- AFT
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123. W W W . P E A K S 2 T A I L S . C O M
19.1 CCAR Variable Transformation

124. W W W . P E A K S 2 T A I L S . C O M
19.2 CCAR Variable Selection

125. W W W . P E A K S 2 T A I L S . C O M
19.3 CCAR Exhaustive Model Search

126. W W W . P E A K S 2 T A I L S . C O M
19.4 CCAR Champion Model

127. W W W . P E A K S 2 T A I L S . C O M
19.5 CCAR Challenger Model

128. W W W . P E A K S 2 T A I L S . C O M
19.6 CCAR K-Fold CV

129. W W W . P E A K S 2 T A I L S . C O M
19.7 CCAR Coefficient Stability Test

130. W W W . P E A K S 2 T A I L S . C O M
19.8 CCAR Variable Sensitivity Analysis

131. W W W . P E A K S 2 T A I L S . C O M
19.9 CCAR Scenario Analysis

132. 19.10 CCAR Forecast Error
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133. 19.11 Vector Error Correction Models
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134. 20.1 Model Validation- Building Logistic Regression Model
W W W . P E A K S 2 T A I L S . C O M

135. 20.2 Model Validation- CAP & AR
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136. 20.3 Model Validation- ROC & KS
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137. 20.4 Model Validation- Confusion Matrix
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138. 20.5 Model Validation- IV & KL Divergence
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139. 20.6 Model Validation- Somer’s D & Kendall Tau
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140. 20.7 Model Validation- Stability Test
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141. 20.8 Model Validation- Calibration Tests
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142. 20.9 Model Validation- Brier Score
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143. 20.10 Jeffrey’s Prior Test
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144. 21.1 Margin of Conservatism type A
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145. 21.1 Margin of Conservatism type B
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146. 21.1 Margin of Conservatism type C – rank ordering error
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147. 21.1 Margin of Conservatism type C – general estimation error
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148. 22.1 ML Discriminant Analysis
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149. 22.2 ML Fisher’s LDA
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150. 22.3 ML Support Vector Machine
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151. 22.4 ML K Nearest Neighbour
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152. 22.5 ML Neural Networks
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153. 22.6 ML Naive Bayes
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154. 23.1 Multinominal Logit
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155. 23.2 Multi-ordinal Logit
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156. 23.3 Gibbs Sampling
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157. 23.4 Metrapolis Hastings
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158. 23.5 Kalman Regression
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159. 24.1 Merton & KMV Model
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160. 24.2 Credit Risk Plus Model
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161. 24.3 Reduced Form Models
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162. 24.4 Credit Metrics
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163. 24.5 Default Correlation
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164. 24.6 Asset Correlation
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165. 24.7 Credit Portfolio VAR
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166. 1. Scorecard
Building
2. Transition
Matrices
3. Stress
Testing
W W W . P E A K S 2 T A I L S . C O M
END TO END PROJECTS IN PYTHON ON:

167. W W W . P E A K S 2 T A I L S . C O M
9780564549