Ramasubramanian Sundararajan

A fuzzy mathematical programming approach for cross-sell optimization in retail banking

We consider the problem of selecting the optimal list of customers to target for a cross-sell campaign in a retail bank. Target selection involves taking estimates of several parameters (response propensity, expected volume, expected profit from a customer, etc) and deciding on the list of customers to whom the offer should be sent such that a certain set of business objectives are met/optimized. We discuss some of the issues related to the target selection process, namely those of unreliable estimates and computational complexity of the problem. We propose a fuzzy mathematical programming technique to address these issues. The imprecise parameters and constraints are represented as triangular fuzzy numbers, while the problem of computational complexity is addressed through a group-level formulation. We use an example of a real-life cross-sell problem for a bank to demonstrate the method. We also provide some sensitivity analyses on critical resources.

Marketing Optimization in Retail Banking

In this paper, we address the problem of making optimal product offers to customers of a retail bank by using techniques including Markov chains, genetic algorithms, mathematical programming, and design of experiments. Our challenges were large problem size, uncertainty about estimates of customer responses to product offers, and practical issues in training and implementation. The solution had an estimated financial impact of around

Metrics for model selection in consumer finance problems

20 million; it also provided other intangible benefits, including structured decision making, the capability of performing what-if analysis, and portability to other markets and portfolios.

Learning with an Embedded Reject Option

We consider the issue of model selection for some prediction problems in consumer finance. In particular, we look at performance metrics in the context of classification problems. Example areas considered include response modeling, profitability modeling and default prediction in the framework of a customer relationship management (CRM) system. We propose some guidelines for choosing the appropriate performance measure for the predictive model based on the decision framework it is part of.

A parallel genetic algorithm for propensity modeling in consumer finance

The option to reject an example in order to avoid the risk of a costly potential misclassification is well-explored in the pattern recognition literature. In this paper, we look at this issue from the perspective of statistical learning theory. Specifically, we look at ways of modeling the problem of learning with an embedded reject option, in terms of minimizing an appropriately defined risk functional, and discuss the applicability thereof of some fundamental principles of learning, such as minimizing empirical risk and structural risk. Finally, we present some directions for further theoretical work on this problem.

System and method for dynamic allocation of resources in a computing grid

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.