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Every year, financial services organizations make billions of dollars worth of decisions using automated systems. For example, who to give a credit card to and the premium someone should pay for their home insurance. This book explains how the forecasting models, that lie at the heart of these systems, are developed and deployed.
Every year, financial services organizations make billions of dollars worth of decisions using automated systems. For example, who to give a credit card to and the premium someone should pay for their home insurance. This book explains how the forecasting models, that lie at the heart of these systems, are developed and deployed.
STEVEN FINLAY has worked in the field of predictive analytics for over fifteen years. His research interests cover all areas of consumer credit, forecasting and data mining. He is currently Head of Analytics at HML in the UK and is a Visiting Research Fellow at the Lancaster University Management School, UK.
Inhaltsangabe
Introduction Project Planning Sample Selection Gathering and Preparing Data Understanding Relationships In Data Data Pre-Processing Model Construction (Parameter Estimation) Validation, Model Performance and Cut-off Strategy Sample Bias and Reject Inference Implementation and Monitoring Further Topics Bibliography
Introduction Project Planning Sample Selection Gathering and Preparing Data Understanding Relationships In Data Data Pre-Processing Model Construction (Parameter Estimation) Validation, Model Performance and Cut-off Strategy Sample Bias and Reject Inference Implementation and Monitoring Further Topics Bibliography
Introduction Project Planning Sample Selection Gathering and Preparing Data Understanding Relationships In Data Data Pre-Processing Model Construction (Parameter Estimation) Validation, Model Performance and Cut-off Strategy Sample Bias and Reject Inference Implementation and Monitoring Further Topics Bibliography
Introduction Project Planning Sample Selection Gathering and Preparing Data Understanding Relationships In Data Data Pre-Processing Model Construction (Parameter Estimation) Validation, Model Performance and Cut-off Strategy Sample Bias and Reject Inference Implementation and Monitoring Further Topics Bibliography
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