CREDIT RISK MODELLING Using SAS

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RISK MODELLING Using SAS Training Module Gurgaon (Head Office) M. G. Road, Gurgaon 122 002, Delhi NCR.

1 Basic Modelling Concepts Advance Credit Risk Model Development Scorecard Model Development Credit Risk Regulatory Guidelines 70 HOURS Practical Learning Live Online Classroom Weekends DexLab Certified CREDIT RISK MODELLING Using SAS Training Module Gurgaon (Head Office) M. G. Road, Gurgaon , Delhi NCR / DexLab Solutions Corporation hello@dexlabanalytics.com

2 Section1: Basics of Statistics and Predictive modeling Basics of Descriptive statistical measures Measures of central tendency (Absolute measures mean, median and mode and relative measures measures of location such as percentile, deciles, quartiles) Measures of dispersion (Absolute measures Range, mean deviation, standard deviation & variance and Relative measures coefficient of range, coefficient of variation, coefficient of quartile deviation, inter-quartile range) Measures of skewness (concept of Skewness and kurtosis, left skewed and right skewed distribution, Karl Pearson s measures of skewness) Application of descriptive measures in risk analytics Population Stability Index, Variable Deviation index, Gini Coefficient. Basic Concepts of Probability and distributions Classic definition of Probability Concepts of Random variables, probability mass function, probability density function and Probability distribution function. Some important probability distributions Bernoulli distribution, Poisson distribution, Normal distribution, chi-square distribution, exponential distribution, uniform distribution, Gamma distribution, Beta distribution, F- distribution, Log-normal distribution, Weibull distribution Moment Generating Function Basics of Sampling and Statistical Inference Idea of Population and sample Relevance of sampling in credit risk model development training and validation datasets, out of sample validation, out of time validation samples. Simple Random Sampling Simple Random Sampling with Replacement and Simple Random Sampling without Replacement, Simple Stratified Sampling, Comparison of Simple Random Sampling with and without stratification. Estimation and Testing of Hypothesis Classical procedures of estimation and properties of a good estimator Unbiased ness, Consistency, efficiency and sufficiency, Concept of a Best Linear Unbiased Estimator (BLUE) and its relevance to predictive modeling.

3 Testing of Hypothesis basic idea of a statistical hypothesis, Type-1 error and Type-2 error, p-value and level of significance, decision rules. Statistical test procedures Parametric tests (Single mean t-test, two independent sample and dependent sample t-test, Analysis of Variance (ANOVA), F-test) Non-Parametric test Wilcoxon mean difference test, Mann Whitney U test, Kolmogorov Smirnov tests, Anderson Darling test, and Carl Vonn Misses test. Predictive Modeling Techniques Modeling Continuous Variables Linear Regression model with single explanatory variables, Multiple Linear Regression Model (Assumptions, estimation (OLS) and testing for the goodness of fit-rsq and Adj Rsq) Modeling Categorical Variables Logistic Regression model (Concept of Linear Probability Models, Odds, Odds Ratio, Maximum likelihood estimation, Log-Likelihood statistics, Wald Chi-square statistics, Hosmer-Lemeshow test, Assessing the model accuracy with KS and Gini) Section 2: Credit Risk Model Development Basics of Risk Scorecard development Basic overview of credit risk scorecards Types of Scorecards Application Scorecards, Behavior Scorecards and Collection scorecards Basics of scorecard development Default Definition, Dependent Variable definition, Snapshot Data, Observation Period, Performance Period, Out-ofsample validation, Out-of-time validation, Information Value, Weight of Evidence, Fine Classing, Coarse Classing, Validation metrics (Population Stability Index, Variable Density Index, Characteristics Stability Index, Divergence Index), Concepts of Data Bureaus, Use of scorecards in making business decisions, Credit ratings and rating agencies. Basics of Banking Products Retail Banking Products Basic understanding of Secured and Unsecured lending products, Basic understanding of Open-ended and close ended lending products, Credit Cards (as business products and the relevant sources of risk), Personal Loans, Current Accounts, Mortgage and Vehicle loans. Commercial Banking Products Asset Based Loans, Cash Flow Loans, Working Capital, Factoring Loans, Equipment Financing loans etc.

4 Assessing a Business problem A case study each for the Retail and Commercial portfolios, exploring the scope of scorecards, Portfolio analysis, Data extraction for both dependent and independent variables from databases and identifying the challenges of an accurate data extraction, Variable comparison and reliability check for the raw data, Challenges of identifying unique merging keys and merging fields across databases. Basics of Scorecard Model Development Data Cleaning - Analysis of missing observations, Missing imputations, Univariate analysis and analysis of frequency distribution, Outlier detection techniques (box plots, z-scores, truncation etc.), Source to target variable flow Data Segmentation Regulatory and Risk based segmentation, Decision trees (CHAID, CART), Simple mean difference tests, KS Test Sampling and Model methodology selection Data structure and choice of appropriate sampling procedures, Important assumptions about the sampling procedure, Sampling Bias, variable selection procedures, Tests for multicollinearity, Variable Reduction (Factor Analysis, Principal Component Analysis), Variable Transformation (Binning, IV, WOE) Data Segmentation Business & Regulatory Segmentation techniques, Decision trees techniques. Model Development Parameter estimation for logistic regression, Wald Chi- Square tests, Log Likelihood statistic, Gini, KS Statistic, Rank Ordering, Score Generation, Points of Doubling odds, Score calibration etc. Model Validation Concepts of model monitoring and model validation, Measures of Model stability, Discriminatory power, Model accuracy. Probability of Default (PD) model development: Overview of PD models PD model for the retail sector, PD model for corporate credit. Basic concepts of rating philosophies Through the Cycle (TTC) and Point-in- Time (PIT) PD models. Classification techniques for PD models Logistic Regression, Linear and Quadratic discriminant Analysis, Linear Programming, Survival Analysis, Hazard Rate models, Decision trees, Memory Based Reasoning, Random Forest, Gradient boosting. Derivation of Bad definition and identification of optimal performance period, input selection methods (IV, Forward, Backward selection methods and Stepwise selection methods)

5 Development and Validation of a PD model in real time data Loss given Default (LGD) model development: Concept of Loss given Default and Recovery Rates Overview of LGD for Retail and Corporate Credit Materiality of Default analysis Basel Regulations for LGD models (Retail and commercial Portfolios) Modelling Loss given defaults. LGD definitions LGD using Market approach, LGD using workout approach (Choosing the work out period, dealing with incomplete workouts, setting the discount factor, calculating indirect costs), basic understanding of default weighted, exposure weighted and time weighted LGD models. Economic Variables and LGD, Economic downturn and LGD estimation Statistical methods for modelling LGD Ordinary Least Squares (Linear Regression), OLS with Beta-transformation, Beta Regression, Ordinary Least squares with Box-Cox transformations, Regression trees, Neural Networks, Logistic Regression and Non-linear Regression, Modelling LGD using two stage models (Ordinal logistic regression with nested linear regression), Decision tree approach, Survival Analysis approach, Downturn LGD calculation Performance metrics for LGD models - Root Mean Square error, Mean Absolute error, Area under Receiving Operation Characteristic curve, Area under Regression Error Characteristic curve, R-square, Pearson s Correlation coefficient, Kendall s Correlation coefficient, LGD Ratings and Calibration Development of a LGD model (end-to-end) Exposure at Default (EAD) Models Overview of EAD Credit Conversion Factors Definition of CCF, Time horizon for CCF, CCF distributions and Transformations, cohort/ fixed time horizon/momentum approach for CCF, risk drivers for CCF, modelling CCF using segmentation and regression approaches. Types of EAD models Credit Conversion Factor (CCF) models, Loan Equivalent (LEQ) models.

6 Concepts of Utilization, Headroom outstanding (For revolving products). Techniques of EAD model development Survival techniques, Decision tree approach, conditional balance and limit equation, Unconditional limit and balance equation, Regression models. Validation of EAD models Out of Sample validation, Out of time Validation, Important Validation metrics PSI, VDI, Accuracy Ratio, KS statistic, Rank Ordering Section3: Regulatory Requirements BASEL II: Basic Understanding of the three pillars of BASEL II Asymptotically Single Risk Frontier (ASRF) model Capital Tiers in BASEL BASEL guidelines for Commercial lending and Retail portfolio Calculation of PD, LGD and EAD estimates for RWA calculation Short and the Long run implications of BASEL. REGULATORY STRESS TESTING GUIDELINES (DFAST AND CCAR): Concepts of stress testing and its relevance to the banking system, Relevance of Stress testing to financial institutions, Main principles of Dodd Frank Act Stress Testing (DFAST) and Comprehensive Capital Analysis and Review (CCAR), Differences between CCAR and DFAST, Impact of the Regulatory guidelines on Systematically Important Financial Institutions (SIFIs), Stress test scenario design (historical v/s hypothetical) Pillar1 v/s Pillar 2 stress testing Macroeconomic stress testing.

OPTIONAL PRE-REQUISITES MODULE: 30 HOURS

7 OPTIONAL PRE-REQUISITES MODULE: 30 HOURS (for participants who don t come with prior SAS skills) SAS programming (Base and Advanced) For INR plus taxes / USD 195 Fees structure* INR / USD 808 *plus Service Tax as applicable * Easy Loan Available

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