Readings in Credit Scoring

Foundations, Developments, and Aims

Inbunden, Engelska, 2004

AvLyn C. Thomas,David B. Edelman,Jonathan Crook

3 339 kr

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Credit scoring is one of the most successful applications of statistical and management science techniques in finance in the last forty years. This unique collection of recent papers, with comments by experts in the field, provides excellent coverage of recent developments, advances and aims in credit scoring. Aimed at statisticians, economists, operational researchers and mathematicians working in both industry and academia, and to all working on credit scoring and data mining, it is an invaluable source of reference.

Produktinformation

Utgivningsdatum2004-07-08
Mått173 x 247 x 23 mm
Vikt730 g
FormatInbunden
SpråkEngelska
SerieOxford Finance Series
Antal sidor338
FörlagOUP OXFORD
ISBN9780198527978

Tillhör följande kategorier

A: HISTORICAL DEVELOPMENT OF CREDIT AND BEHAVIOURAL SCORING ; Legal, social and economic issues in implementing scoring in the US ; Problems in applying discriminant analysis in credit scoring models ; Behaviour scoring and adaptive control systems ; B: OBJECTIVES AND MEASURES IN CREDIT SCORING ; Measures for comparing scoring systems ; The use of affordability data - does it add real value? ; Improving lender offers using consumer preferences ; C: PRACTICAL IMPLEMENTATION OF SCORING SYSTEMS ; Updating scorecards: Removing the mystique ; Efficient frontier cut-off policies in credit portfolios ; D: FEATURES OF SCORING ; Can reject inference ever work? ; The flat-maximum effect and generic linear scoring models: a test ; The degradation of the scorecard over the business cycle ; Inferring the inferred ; E: OTHER APPLICATIONS OF SCORING IN CREDIT RISK ; Detecting credit card fraud using expert systems ; A single European scorecard ; Small sample scoring ; F: ALTERNATIVE APPROACHES TO SCORING SYSTEMS ; Survival analysis and the credit granting decision ; Graphical models in credit scoring ; Credit scoring using neural and evolutionary techniques ; Segmenting in Markov chain consumer credit behaviour models