Transcription of Predictive Modeling Using Transactional Data
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Predictive Modeling Using Transactional Data
Financial Servicesthe way we see itPredictive Modeling Using Transactional Data 2 Contents1 Introduction 32 Using Transactional Data 43 Data Quality Data Profiling Exploratory Data Analysis 64 Cohort and Trend Analysis 75 Model Variable Definition 96 Model Selection 107 Conclusion 11 Predictive Modeling Using Transactional Data 3the way we see itIn a world where traditional bases of competitive advantages have dissipated, analytics driven processes may be one of the few remaining points of differentiation for firms in any industry1. This is particularly true in financial services, which has progressed rather fast along the analytical path in the last couple of decades. Analytics can be used to slice and dice historical data to analyze past performance and to produce reports. Here analytics helps firms react to past events. The real benefit of analytics is in Using past data to forecast or predict future events, providing firms with a strategic capability to be real benefit of analytics is in Using past data to forecast or predict future events, providing firms with a strategic capability to be IntroductionFigure 1: Reactive vs.
Predictive modeling involves creating a model that outputs the probability of an outcome given current state values of input parameters. In banking and insurance industries, it is typically used in the context of predicting customer behavior. Historical data related to past customer activity is used to create a predictive model that captures
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