A parallel genetic algorithm for propensity modeling in consumer finance

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.