Consumer finance data generator - a new approach to Credit Scoring technique comparison
This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due to the limited availability of data. The same conclusion cannot be guaranteed when using other data from another source. The following research challenge can therefore be formulated: how should the comparison be managed in order to get general results that are not biased by particular data? The solution may be in the use of various random data generators. The data generator uses two approaches: transition matrix and scorings. Here are presented both: results of comparison methods and the methodology of these comparison techniques creating. Before building a new model the modeler can undertake a comparison exercise that aims at identifying the best method in the case of the particular data. Here are presented various measures of predictive model like: Gini, Delta Gini, VIF and Max p-value, emphasizing the multi-criteria problem of a "Good model". The idea that is being suggested is of particular use in the model building process where there are defined complex criteria trying to cover the important problems of model stability over a period of time, in order to avoid a crisis. Some arguments for choosing Logit or WOE approach as the best scorecard technique are presented.
Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring
Acknowledgments. Chapter 1: Introduction. Scorecards: General Overview. Chapter 2: Scorecard Development: The People and the Process. Scorecard Development Roles. Intelligent Scorecard Development. Scorecard Development and Implementation Process: Overview. Chapter 3: Scorecard Development Process, Stage 1: Preliminaries and Planning. Create Business Plan. Create Project Plan. Why "Scorecard" Format? Chapter 4: Scorecard Development Process, Stage 2: Data Review and Project Parameters. Data Availability and Quality. Data Gathering for Definition of Project Parameters. Definition of Project Parameters. Segmentation. Methodology. Review of Implementation Plan. Chapter 5: Scorecard Development Process, Stage 3: Development Database Creation. Development Sample Specification. Development Data Collection and Construction. Adjusting for Prior Probabilities. Chapter 6: Scorecard Development Process, Stage 4: Scorecard Development. Explore Data. Missing Values and Outliers. Correlation. Initial Characteristic Analysis. Preliminary Scorecard. Reject Inference. Final Scorecard Production. Choosing a Scorecard. Validation. Chapter 7: Scorecard Development Process, Stage 5: Scorecard Management Reports. Gains Table. Characteristic Reports. Chapter 8: Scorecard Development Process, Stage 6: Scorecard Implementation. Preimplementation Validation. Strategy Development. Chapter 9: Scorecard Development Process, Stage 7: Postimplementation. Scorecard and Portfolio Monitoring Reports. Review. Bibliography. Index.
Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis
Abstract The construction of a suitable rule of allocation in the two-population discrimination problem is considered in the case where there are initially available from the populations II1, II2, n 1, n 2 observations and M unclassified observations. An iterative reclassification procedure based on the n 1 + n 3 + M observations is proposed and found asymptotically optimal when M → ∞ and n 1 and n 2 are moderately large. The case of finite M is evaluated by a Monte Carlo experiment which suggests that the proposed procedure, after only one iteration, gives a rule with smaller average risk than the usual rule based on just the n 1 + n 2 classified observations.