DexLab Certified
CREDIT RISK MODELLING Using SAS
Training Module
DexLab Solutions [email protected]
www.dexlabanalytics.com
70 HOURSPractical Learning
Gurgaon (Head Office)M. G. Road, Gurgaon 122 002, Delhi NCR.+91 852 787 2444 / +91 124 450 2444
Basic Modelling ConceptsAdvance Credit Risk Model DevelopmentScorecard Model DevelopmentCredit Risk Regulatory Guidelines
Live Online Classroom Weekends
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.
• Testing of Hypothesis – basic idea of a statistical hypothesis, Type-1 error andType-2 error, p-value and level of significance, decision rules.
• Statistical test procedures – Parametric tests (Single mean t-test, twoindependent 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 ofLinear 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 andCollection scorecards
• Basics of scorecard development – Default Definition, Dependent Variabledefinition, Snapshot Data, Observation Period, Performance Period, Out-of-sample validation, Out-of-time validation, Information Value, Weight ofEvidence, Fine Classing, Coarse Classing, Validation metrics (PopulationStability Index, Variable Density Index, Characteristics Stability Index,Divergence Index), Concepts of Data Bureaus, Use of scorecards in makingbusiness 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 endedlending products, Credit Cards (as business products and the relevant sourcesof 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.
Assessing a Business problem – A case study each for the Retailand 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 detectiontechniques (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 ofappropriate sampling procedures, Important assumptions about the samplingprocedure, Sampling Bias, variable selection procedures, Tests formulticollinearity, Variable Reduction (Factor Analysis, Principal ComponentAnalysis) , 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, ScoreGeneration, 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 forcorporate 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 andQuadratic discriminant Analysis, Linear Programming, Survival Analysis,Hazard Rate models, Decision trees, Memory Based Reasoning, RandomForest, Gradient boosting.
• Derivation of Bad definition and identification of optimal performance period,input selection methods (IV, Forward, Backward selection methods andStepwise selection methods)
• 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 thediscount factor, calculating indirect costs), basic understanding of defaultweighted, exposure weighted and time weighted LGD models.
• Economic Variables and LGD, Economic downturn and LGD estimation
• Statistical methods for modelling LGD – Ordinary Least Squares (LinearRegression), OLS with Beta-transformation, Beta Regression, Ordinary Leastsquares with Box-Cox transformations, Regression trees, Neural Networks,Logistic Regression and Non-linear Regression, Modelling LGD using twostage models (Ordinal logistic regression with nested linear regression),Decision tree approach, Survival Analysis approach, Downturn LGDcalculation
• Performance metrics for LGD models - Root Mean Square error, MeanAbsolute error, Area under Receiving Operation Characteristic curve, Areaunder Regression Error Characteristic curve, R-square, Pearson’s Correlationcoefficient, 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, CCFdistributions and Transformations, cohort/ fixed time horizon/momentumapproach for CCF, risk drivers for CCF, modelling CCF using segmentationand regression approaches.
• Types of EAD models – Credit Conversion Factor (CCF) models, LoanEquivalent (LEQ) models.
• Concepts of Utilization, Headroom outstanding (For revolving products).
• Techniques of EAD model development – Survival techniques, Decision treeapproach, conditional balance and limit equation, Unconditional limit andbalance equation, Regression models.
• Validation of EAD models – Out of Sample validation, Out of time Validation,Important Validation metrics – PSI, VDI, Accuracy Ratio, KS statistic, RankOrdering
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) andComprehensive Capital Analysis and Review (CCAR),
• Differences between CCAR and DFAST, Impact of the Regulatory guidelineson Systematically Important Financial Institutions (SIFIs),
• Stress test scenario design (historical v/s hypothetical)
• Pillar1 v/s Pillar 2 stress testing
• Macroeconomic stress testing.
Fees structure*INR 37000 / USD 720*plus Service Tax as applicable
* Easy Loan Available
OPTIONAL PRE-REQUISITES MODULE: 30 HOURS (for participants who don’t come with prior SAS skills)
SAS programming (Base and Advanced)For INR 13000 plus taxes / USD 195
