Credit scoring, response modeling, and insurance rating: a practical guide to forecasting consumer behavior

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, including project management, data collection, sampling, data transformation and pre-processing, model construction, validation, implementation and post-implementation monitoring of the model's performance. INDICE: 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 .

• ISBN: 978-0-230-34776-2
• Editorial: Palgrave Macmillan
• Encuadernacion: Cartoné
• Páginas: 320
• Fecha Publicación: 29/06/2012
• Nº Volúmenes: 1
• Idioma: Inglés