Credit scoring is the term used to describe formal statistical methods used for classifying applicants for credit into "good" and "bad" risk classes. Such methods have become increasingly important with the dramatic growth in consumer credit in recent years. A wide range of statistical methods has been applied, though the literature available to the public is limited for reasons of commercial confidentiality. Particular problems arising in the credit scoring context are examined and the statistical methods which have been applied are reviewed.

Statistical Classification Methods in Consumer Credit Scoring: A Review

SUMMARY Credit scoring is the term used to describe formal statistical methods used for classifying applicants for credit into 'good' and 'bad' risk classes. Such methods have become increasingly important with the dramatic growth in consumer credit in recent years. A wide range of statistical methods has been applied, though the literature available to the public is limited for reasons of commercial confidentiality. Particular problems arising in the credit scoring context are examined and the statistical methods which have been applied are reviewed.

Statistical Classification Methods in Consumer Credit Scoring: a Review

A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers

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.

Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring

Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Reject inference applied to logistic regression for credit scoring

SUMMARY This paper is concerned with methods of estimating Shannon's information measure, and with the sampling properties of those estimates. A Bayesian approach is used to suggest two possible classes of estimators, and their properties are compared with those of the usual Shannon measure of diversity. Particular attention is given to comparisons involving the MacArthur model of species abundances.

Bayesian estimation of Shannon's index of diversity

Sequential tests of composite hypotheses

This article reviews, in brief, the use of intensive probation schemes as an alternative to custodial sentencing in England and Wales and outlines the establishment of one such project in Leeds. Having briefly detailed the project's philosophy and working practices, the article reports the findings of an evaluation of the project and concludes with some suggestions as to whether the project is operating as a genuine replacement for custody.

INTENSIVE PROBATION FOR YOUNG ADULT OFFENDERS Evaluating the Impact of a Non-Custodial Sentence

Interval estimation of a well-known measure of heterogeneity or diversity is approached by fitting a distribution by the method of moments. Exact expressions for the moments of the distribution are derived and evaluated using a computer algebra package.

Derrick N. Joanes

Papers

Reject inference applied to logistic regression for credit scoring

Bayesian estimation of Shannon's index of diversity

Sequential tests of composite hypotheses

INTENSIVE PROBATION FOR YOUNG ADULT OFFENDERS Evaluating the Impact of a Non-Custodial Sentence

Interval estimation of a measure of diversity