A Parallel Genetic Algorithm for Propensity Modeling in Consumer Finance

  • Ramasubramanian Sundararajan
  • Tarun Bhaskar
  • Padmini Rajagopalan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)

Abstract

We consider the problem of propensity modeling in consumer finance. These modeling problems are characterized by the two aspects: the model needs to optimize a business objective which may be nonstandard, and the rate of occurence of the event to be modeled may be very low. Traditional methods such as logistic regression are ill-equipped to deal with nonstandard objectives and low event rates. Methods which deal with the low event rate problem by learning on biased samples face the problem of overlearning. We propose a parallel genetic algorithm method that addresses these challenges. Each parallel process evolves propensity models based on a different biased sample, while a mechanism for validation and cross-pollination between the islands helps address the overlearning issue. We demonstrate the utility of the method on a real-life dataset.

Keywords

Genetic Algorithm Logistic Regression Credit Card Validation Sample Business Objective 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ramasubramanian Sundararajan
    • 1
  • Tarun Bhaskar
    • 1
  • Padmini Rajagopalan
    • 2
  1. 1.Software Sciences & AnalyticsGE Global ResearchBangaloreIndia
  2. 2.Department of Computer ScienceUniversity of Texas at AutinAustinUSA

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