Credit Scoring, Response Modelling And Insurance Rating: A Practical Guide to Forecasting Consumer Behaviour by S. Finlay

Credit Scoring, Response Modelling And Insurance Rating: A Practical Guide to Forecasting Consumer…

byS. Finlay

Hardcover | October 27, 2010

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Within the financial services industry today, most decisions on how to deal with consumers are made automatically by computerized decision making systems. At the heart of these systems lie mathematically derived forecasting models. These use information about people and their past behavior, to predict how people are likely to behave in the future. For example, who is likely to repay a loan, who will respond to a mail shot and the likelihood that someone will claim on their household insurance policy. Decisions about how to treat people are then made on the basis of the predictions calculated by the system.

This book provides a step-by-step guide to how the forecasting models used by the worlds leading financial institutions are developed and deployed. It covers all stages involved in the construction of such a model, including project management, data collection, sampling, data pre-processing, model construction, validation, implementation and post-implementation monitoring of the model's performance.

About The Author

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.

Details & Specs

Title:Credit Scoring, Response Modelling And Insurance Rating: A Practical Guide to Forecasting Consumer…Format:HardcoverDimensions:296 pages, 9.02 × 5.98 × 0.04 inPublished:October 27, 2010Publisher:Palgrave Macmillan UKLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0230577040

ISBN - 13:9780230577046

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Extra Content

Table of Contents

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