Classification has been commonly used in many data mining projects in the financial service industry. For instance, to predict collectability of accounts receivable, a binary class label is created based on whether a payment is received within a certain period. However, optimization of the classifier does not necessarily lead to maximization of return on investment (ROI), since maximization of the true positive rate is often different from maximization of the collectable amount which determines the ROI under a fixed budget constraint. The typical cost sensitive learning does not solve this problem either since it involves an unknown opportunity cost due to the budget constraint. Learning the ranks of collectable amount would ultimately solve the problem, but it tries to tackle an unnecessarily difficult problem and often results in poorer results for our specific target. We propose a new algorithm that uses gradient descent to directly optimize the related monetary measure under the budget constraint and thus maximizes the ROI. By comparison with several classification, regression, and ranking algorithms, we demonstrate the new algorithm's substantial improvement of the financial impact on our clients in the financial service industry.

Machine learning techniques have been used to predict customer behavior in telecommunications industry. Typically, several data sources, including historical usage, billing, payment, network, customer service, and demographic data, can be used in a predictive model. However, in some cases, e.g., in the prepaid customer segment, there is often little data available except for the call detail record (CDR) data. In this paper, we tackle this challenging problem, using significantly delayed CDR data as the primary data source to predict customer behavior. We extract calling links, i.e., who called whom, from the CDR data, and propose several distance measures based on calling links. We demonstrate that, by using information derived from the calling links alone as inputs to a neural network model, an acceptable accuracy for predicting churn (customer switching from one service provider to another) can be achieved. Calling links can also be used to identify calling communities, which may be used for targeted marketing campaigns and help predict acceptance of marketing offers.

A lift curve, with the true positive rate on the y-axis and the customer pull (or contact) rate on the x-axis, is often used to depict the model performance in many data mining applications, especially in the area of customer relationship management (CRM). Typically, these applications concern only the model accuracy at a relatively small pull or contact/intervention rate of the whole customer base, which is predetermined by a budget constraint for the project, e.g., how many customers can be contacted every month. In this paper, we address the important problem of enhancing the lift (true positive rate) at a specified pull rate. We propose two distinct algorithms, which are applicable to different scenarios. In particular, when the binary class label of the training set is extracted from a continuous variable, we can optimize a training objective which takes into account the specified pull rate rather than the class prior, based on the often ignored continuous variable. In those cases where only the binary class label is available during training, we propose a constrained optimization algorithm to maximize the true positive rate related to a specific decision threshold at which the specified pull rate is achieved. We applied both algorithms to our projects of predicting defection (decline in account value) of mutual fund accounts for two major U.S. mutual fund companies and achieved substantial enhancement of the lift at the specified pull rate.