Over eight modules, the book covers consumer and business lending in both the developed and developing worlds, providing the frameworks for both theory and practice.
Over eight modules, the book covers consumer and business lending in both the developed and developing worlds, providing the frameworks for both theory and practice.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Raymond Anderson is a Canadian resident in South Africa, with a BComm from the University of Calgary (1980) and an MBA from the University of the Witwatersrand (1991). His wanderlust brought him to South Africa in 1981, where he was employed by The Standard Bank Group; 13 years as the computer/numbers guy in project finance, followed by 19 years in credit. His introduction to credit came when its computerisation was in its infancy, and he was involved in a multitude of credit scoring aspects. His first book, The Credit Scoring Toolkit, was published by Oxford University Press in 2007. Since 2015, he has been consulting independently, mostly through the World Bank's International Finance Corporation in developing countries, and is presently involved developing trip for their Asia office.
Inhaltsangabe
Credit Intelligence Predictive Modelling Overview Retail Credit Business Credit Side Histories Credit-A Microhistory The Birth of Modern Credit Intelligence The Dawn of Credit Scoring Front-Door Back-Door Stats and Maths and Unicorns Borrowed Measures Practical Application Predictive Modelling Techniques Project Management Data Acquisition-Observation Data Acquisition-Performance Target Definition File Assembly Sample Selection Data Transformation Segmentation Reject Inference Model Training Scaling and Banding Finalisation
Credit Intelligence Predictive Modelling Overview Retail Credit Business Credit Side Histories Credit-A Microhistory The Birth of Modern Credit Intelligence The Dawn of Credit Scoring Front-Door Back-Door Stats and Maths and Unicorns Borrowed Measures Practical Application Predictive Modelling Techniques Project Management Data Acquisition-Observation Data Acquisition-Performance Target Definition File Assembly Sample Selection Data Transformation Segmentation Reject Inference Model Training Scaling and Banding Finalisation
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