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

By: Material type: Computer fileComputer filePublisher number: 9780230347762Publication details: Basingstoke : Palgrave Macmillan, 2012.Edition: 2nd edISBN:
  • 9781137031693
Subject(s): Genre/Form: Additional physical formats: Print version:: Credit Scoring, Response Modeling, and Insurance Rating : A Practical Guide to Forecasting Consumer BehaviorDDC classification:
  • 658.8342
LOC classification:
  • HG3701 .F55 2012
Online resources:
Contents:
Cover; Contents; 1 Introduction; 1.1 Scope and content; 1.2 Model applications; 1.3 The nature and form of consumer behavior models; 1.3.1 Linear models; 1.3.2 Classification and regression trees (CART); 1.3.3 Artificial neural networks; 1.4 Model construction; 1.5 Measures of performance; 1.6 The stages of a model development project; 1.7 Chapter summary; 2 Project Planning; 2.1 Roles and responsibilities; 2.2 Business objectives and project scope; 2.2.1 Project scope; 2.2.2 Cheap, quick or optimal?; 2.3 Modeling objectives; 2.3.1 Modeling objectives for classification models
2.3.2 Roll rate analysis2.3.3 Profit based good/bad definitions; 2.3.4 Continuous modeling objectives; 2.3.5 Product level or customer level forecasting?; 2.4 Forecast horizon (outcome period); 2.4.1 Bad rate (emergence) curves; 2.4.2 Revenue/loss/value curves; 2.5 Legal and ethical issues; 2.6 Data sources and predictor variables; 2.7 Resource planning; 2.7.1 Costs; 2.7.2 Project plan; 2.8 Risks and issues; 2.9 Documentation and reporting; 2.9.1 Project requirements document; 2.9.2 Interim documentation; 2.9.3 Final project documentation (documentation manual); 2.10 Chapter summary
3 Sample Selection3.1 Sample window (sample period); 3.2 Sample size; 3.2.1 Stratified random sampling; 3.2.2 Adaptive sampling; 3.3 Development and holdout samples; 3.4 Out-of-time and recent samples; 3.5 Multi-segment (sub-population) sampling; 3.6 Balancing; 3.7 Non-performance; 3.8 Exclusions; 3.9 Population flow (waterfall) diagram; 3.10 Chapter summary; 4 Gathering and Preparing Data; 4.1 Gathering data; 4.1.1 Mismatches; 4.1.2 Sample first or gather first?; 4.1.3 Basic data checks; 4.2 Cleaning and preparing data; 4.2.1 Dealing with missing, corrupt and invalid data
4.2.2 Creating derived variables4.2.3 Outliers; 4.2.4 Inconsistent coding schema; 4.2.5 Coding of the dependent variable (modeling objective); 4.2.6 The final data set; 4.3 Familiarization with the data; 4.4 Chapter summary; 5 Understanding Relationships in Data; 5.1 Fine classed univariate (characteristic) analysis; 5.2 Measures of association; 5.2.1 Information value; 5.2.2 Chi-squared statistic; 5.2.3 Efficiency (GINI coefficient); 5.2.4 Correlation; 5.3 Alternative methods for classing interval variables; 5.3.1 Automated segmentation procedures
5.3.2 The application of expert opinion to interval definitions5.4 Correlation between predictor variables; 5.5 Interaction variables; 5.6 Preliminary variable selection; 5.7 Chapter summary; 6 Data Transformation (Pre-processing); 6.1 Dummy variable transformed variables; 6.2 Weights of evidence transformed variables; 6.3 Coarse classing; 6.3.1 Coarse classing categorical variables; 6.3.2 Coarse classing ordinal and interval variables; 6.3.3 How many coarse classed intervals should there be?; 6.3.4 Balancing issues; 6.3.5 Applying transformations to holdout, out-of-time and recent samples
6.4 Which is best - weight of evidence or dummy variables?
Summary: 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.
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Total holds: 0

Enhanced descriptions from Syndetics:

Description based upon print version of record.

Cover; Contents; 1 Introduction; 1.1 Scope and content; 1.2 Model applications; 1.3 The nature and form of consumer behavior models; 1.3.1 Linear models; 1.3.2 Classification and regression trees (CART); 1.3.3 Artificial neural networks; 1.4 Model construction; 1.5 Measures of performance; 1.6 The stages of a model development project; 1.7 Chapter summary; 2 Project Planning; 2.1 Roles and responsibilities; 2.2 Business objectives and project scope; 2.2.1 Project scope; 2.2.2 Cheap, quick or optimal?; 2.3 Modeling objectives; 2.3.1 Modeling objectives for classification models

2.3.2 Roll rate analysis2.3.3 Profit based good/bad definitions; 2.3.4 Continuous modeling objectives; 2.3.5 Product level or customer level forecasting?; 2.4 Forecast horizon (outcome period); 2.4.1 Bad rate (emergence) curves; 2.4.2 Revenue/loss/value curves; 2.5 Legal and ethical issues; 2.6 Data sources and predictor variables; 2.7 Resource planning; 2.7.1 Costs; 2.7.2 Project plan; 2.8 Risks and issues; 2.9 Documentation and reporting; 2.9.1 Project requirements document; 2.9.2 Interim documentation; 2.9.3 Final project documentation (documentation manual); 2.10 Chapter summary

3 Sample Selection3.1 Sample window (sample period); 3.2 Sample size; 3.2.1 Stratified random sampling; 3.2.2 Adaptive sampling; 3.3 Development and holdout samples; 3.4 Out-of-time and recent samples; 3.5 Multi-segment (sub-population) sampling; 3.6 Balancing; 3.7 Non-performance; 3.8 Exclusions; 3.9 Population flow (waterfall) diagram; 3.10 Chapter summary; 4 Gathering and Preparing Data; 4.1 Gathering data; 4.1.1 Mismatches; 4.1.2 Sample first or gather first?; 4.1.3 Basic data checks; 4.2 Cleaning and preparing data; 4.2.1 Dealing with missing, corrupt and invalid data

4.2.2 Creating derived variables4.2.3 Outliers; 4.2.4 Inconsistent coding schema; 4.2.5 Coding of the dependent variable (modeling objective); 4.2.6 The final data set; 4.3 Familiarization with the data; 4.4 Chapter summary; 5 Understanding Relationships in Data; 5.1 Fine classed univariate (characteristic) analysis; 5.2 Measures of association; 5.2.1 Information value; 5.2.2 Chi-squared statistic; 5.2.3 Efficiency (GINI coefficient); 5.2.4 Correlation; 5.3 Alternative methods for classing interval variables; 5.3.1 Automated segmentation procedures

5.3.2 The application of expert opinion to interval definitions5.4 Correlation between predictor variables; 5.5 Interaction variables; 5.6 Preliminary variable selection; 5.7 Chapter summary; 6 Data Transformation (Pre-processing); 6.1 Dummy variable transformed variables; 6.2 Weights of evidence transformed variables; 6.3 Coarse classing; 6.3.1 Coarse classing categorical variables; 6.3.2 Coarse classing ordinal and interval variables; 6.3.3 How many coarse classed intervals should there be?; 6.3.4 Balancing issues; 6.3.5 Applying transformations to holdout, out-of-time and recent samples

6.4 Which is best - weight of evidence or dummy variables?

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.