Credit Scoring Its Applications Monographs on Mathematical Modeling and Computation 1st Edition by LynThomas, David Edelman, Jonathan Crook ISBN 978-0898714838 0898714834
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Credit Scoring Its Applications Monographs on Mathematical Modeling and Computation 1st Edition by Lyn C. Thomas, David B. Edelman, Jonathan N. Crook – Ebook PDF Instant Download/Delivery: 978-0898714838, 0898714834
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ISBN 10: 0898714834
ISBN 13: 978-0898714838
Author: Lyn C. Thomas, David B. Edelman, Jonathan N. Crook
Table of contents:
1 The History and Philosophy of Credit Scoring
1.1 Introduction: What is credit scoring?
1.2 History of credit
1.3 History of credit scoring.
1.4 Philosophical approach to credit scoring
1.5 Credit scoring and data mining
2 The Practice of Credit Scoring
2.1 Introduction
2.2 Credit assessment before scoring
2.3 How credit scoring fits into a lender’s credit assessment
2.4 What data are needed?
2.5 The role of credit-scoring consultancies.
2.6 Validating the scorecard
2.7 Relation with information system
2.8 Application form.
2.9 Role of the credit bureau.
2.10 Overrides and manual intervention
2.11 Monitoring and tracking
2.12 Relationship with a portfolio of lender’s products
3 Economic Cycles and Lending and Debt Patterns
3.1 Introduction
3.2 Changes in credit over time
3.3 Microeconomic issues
3.3.1 Present value
3.3.2 Economic analysis of the demand for credit.
Credit constraints
3.3.4 Empirical evidence.
3.4 Macroeconomic issues.
3.4.1 A simplified Keynesian-type model of the economy
3.4.2 The money channel
3.4.3 The credit channel
3.4.4 Empirical evidence
3.5 Default behavior
4. Statistical Methods for Building Credit Scorecards
4.1 Introduction
4.2 Discriminant analysis: Decision theory approach
4.2.1 Univariate normal case
4.2.2 Multivariate normal case with common covariance.
4.2.3 Multivariate normal case with different covariance matrices
4.3 Discriminant analysis: Separating the two groups
4.4 Discriminant analysis: A form of linear regression
4.5 Logistic regression
4.6 Other nonlinear regression approaches
4.7 Classification trees (recursive partitioning approach)
4.7.1 Kolmogorov-Smirnov statistic
4.7.2 Basic impurity index i(v)
4.7.3 Gini index
4.7.4 Entropy index
4.7.5 Maximize half-sum of squares.
4.8 Nearest-neighbor approach
4.9 Multiple-type discrimination
5 Nonstatistical Methods for Scorecard Development
5.1 Introduction
5.2 Linear programming
5.3 Integer programming
5.4 Neural networks
5.4.1 Single-layer neural networks
5.4.2 Multilayer perceptrons.
5.4.3 Back-propagation algorithm
5.4.4 Network architecture.
5.4.5 Classification and error functions
5.5 Genetic algorithms
5.5.1 Basic principles
5.5.2 Schemata.
5.6 Expert systems.
5.7 Comparison of approaches
6 Behavioral Scoring Models of Repayment and Usage Behavior
6.1 Introduction
6.2 Behavioral scoring: Classification approaches
6.3 Variants and uses of classification approach-based behavioral scoring systems.
6.4 Behavioral scoring: Orthodox Markov chain approach.
6.5 Markov decision process approach to behavioral scoring
6.6 Validation and variations of Markov chain models
6.6.1 Estimating parameters of a stationary Markov chain model
6.6.2
6.6.3 Testing if p(i, j) have specific values pº(i, j)
6.6.4 Testing if p. (i, j) are stationary
6.6.5 Testing that the chain is Markov
Estimating parameters of nonstationary Markov chains.
Mover-stayer Markov chain models
6.7 Behavioral scoring: Bayesian Markov chain approach
7 Measuring Scorecard Performance
7.1 Introduction
7.2 Error rates using holdout samples and 2 x 2 tables
7.3 Cross-validation for small samples
7.4 Bootstrapping and jackknifing
7.5 Separation measures: Mahalanobis distance and Kolmogorov-Smirnov statistics
7.6 ROC curves and Gini coefficients
7.7 Comparing actual and predicted performance of scorecards: The delta approach
8 Practical Issues of Scorecard Development
8.1 Introduction
8.2 Selecting the sample
8.3 Definitions of good and bad
8.4 Characteristics available.
8.5 Credit bureau characteristics.
8.5.1 Publicly available information
8.5.2 Previous searches
8.5.3 Shared contributed information
8.5.4 Aggregated information
8.5.5 Fraud warnings.
8.5.6 Bureau-added value
8.6 Determining subpopulations.
8.7 Coarse classifying the characteristics
8.7.1 x²-statistic
8.7.2 Information statistic
8.7.3 Somer’s D concordance statistic
8.7.4 Maximum likelihood monotone coarse classifier
8.8 Choosing characteristics
8.9 Reject inference
8.9.1 Define as bad
8.9.2 Extrapolation
8.9.3 Augmentation
8.9.4 Mixture of distributions
8.9.5 Three-group approach
8.10 Overrides and their effect in the scorecards
8.11 Setting the cutoff.
8.12 Aligning and recalibrating scorecards
9.Implementation and Areas of Application
9.1 Introduction
9.2 Implementing a scorecard
9.3 Monitoring a scorecard
9.4 Tracking a scorecard
9.5 When are scorecards too old?
9.6 Champion versus challenger
10 Applications of Scoring in Other Areas of Lending
10.1 Introduction
10.2 Prescreening
10.3 Preapproval
10.4 Fraud prevention
10.5 Mortgage scoring
10.6 Small business scoring
10.7 Risk-based pricing
10.8 Credit extension and transaction authorization
10.9 Debt recovery: Collections scoring and litigation scoring
10.10 Provisioning for bad debt
10.11 Credit reference export guarantees
11 Applications of Scoring in Other Areas
11.1 Introduction
11.2 Direct marketing
11.3 Profit scoring.
11.4 Tax inspection
11.5 Payment of fines and maintenance payments
11.6 Parole
11.7 Miscellany
12 New Ways to Build Scorecards
12.1 Introduction
12.2 Generic scorecards and small sample modeling.
12.3 Combining two scorecards: Sufficiency and screening
12.4 Combining classifiers
12.5 Indirect credit scoring
12.6 Graphical models and Bayesian networks applied to credit scoring
12.7 Survival analysis applied to credit scoring
13 International Differences
13.1 Introduction
13.2 Use of credit
13.2.1 Consumer credit
13.2.2 Credit cards
13.3 International differences in credit bureau reports
13.3.1 The U.S.
13.3.2 Other countries
13.4 Choice of payment vehicle
13.5 Differences in scorecards
13.6 Bankruptcy.
14 Profit Scoring, Risk-Based Pricing, and Securitization
14.1 Introduction
14.2 Profit-maximizing decisions and default-based scores
14.3 Holistic profit measure.
14.4 Profit-scoring systems
14.5 Risk-based pricing
14.6 Securitization
14.7 Mortgage-backed securities
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Tags: Lyn Thomas, David Edelman, Jonathan Crook, Credit Scoring, Its Applications, Mathematical Modeling