Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior
A guide on how Predictive Analytics is applied and widely used by organizations such as banks, insurance providers, supermarkets and governments to drive the decisions they make about their customers, demonstrating who to target with a promotional offer, who to give a credit card to and the premium someone should pay for home insurance.
1110839439
Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior
A guide on how Predictive Analytics is applied and widely used by organizations such as banks, insurance providers, supermarkets and governments to drive the decisions they make about their customers, demonstrating who to target with a promotional offer, who to give a credit card to and the premium someone should pay for home insurance.
129.99 In Stock
Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior

Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior

by S. Finlay
Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior

Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior

by S. Finlay

Hardcover(2nd ed. 2012)

$129.99 
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Overview

A guide on how Predictive Analytics is applied and widely used by organizations such as banks, insurance providers, supermarkets and governments to drive the decisions they make about their customers, demonstrating who to target with a promotional offer, who to give a credit card to and the premium someone should pay for home insurance.

Product Details

ISBN-13: 9780230347762
Publisher: Palgrave Macmillan UK
Publication date: 06/26/2012
Edition description: 2nd ed. 2012
Pages: 297
Product dimensions: 8.60(w) x 5.60(h) x 1.00(d)

About the Author

STEVEN FINLAY has worked in the field of predictive analytics for over fifteen years. His research interests cover all areas of predictive analytics, forecasting and data mining. He is currentlya Predictive Analytics Specialist, working for the UK Government, and is a visiting research fellow within the Lancaster University Management School, UK.

Table of Contents

Introduction Project Planning Sample Selection Gathering and Preparing Data Understanding Relationships in Data Data Transformation (Pre-processing) Model Construction (Parameter Estimation) Validation, Model Performance and Cut-off Strategy Sample Bias and Reject Inference Implementation and Monitoring Multi-model (Fusion) Systems Further Topics Bibliography
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