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.
Within all large consumer facing organizations, most decisions about how to deal with people are made automatically by computerized decision making systems. Information about people, their lifestyle and past behavior are used to predict how they are expected to behave in the future. It can be determined if someone applying for a bank loan will make their repayments, who will respond to a marketing communication and the likelihood that someone will claim on their insurance policy. This book provides a step-by-step guide to how Predictive Analytics is used by some of the world's most influential organizations. This includes international banks, leading insurance providers, credit reference agencies and national governments. It covers all stages of the Predictive Analytics process. This includes project management, data collection, sampling, data transformation and pre-processing, model construction, validation, implementation and post-implementation monitoring of the model's performance.
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