We describe graphical model based methods for analyzing prescription and medical claims data in order to identify fraud and waste. Our approach draws on ideas from speech recognition and language modeling to identify patients, doctors and pharmacies whose prescription encounters show significant departure from normative behavior. We have analyzed claims data from a large healthcare provider, consisting of over 53 million individual prescription claims in the calendar year 2011.

We discuss real-world case studies involving the implementation of a Web services middleware tier for the IBM Blue Gene/L supercomputer to support financial business applications. These programs that are representative of a class of modern financial analytics that take part in distributed business workflows and are heavily database-centric with input and output data stored in external SQL data warehouses. We describe the design issues related to the development of our middleware tier that provides a number of core features, including an automated SQL data extraction and staging gateway, a standardized high-level job specification schema, a well-defined Web services (SOAP) API for interoperability with other applications, and a secure HTML/JSP Web-based interface suitable for general users. Further, we provide observations on performance optimizations to support the relevant data movement requirements.

We develop a new concept for aggregating items of evidence for class probability estimation. In Naive Bayes, each feature contributes an independent multiplicative factor to the estimated class probability. We modify this model to include an exponent in each factor in order to introduce feature importance. These exponents are chosen to maximize the accuracy of estimated class probabilities on the training data. For Naive Bayes, this modification accomplishes more than what feature selection can. More generally, since the individual features can be the outputs of separate probability models, this yields a new ensemble modeling approach, which we call APM (Adjusted Probability Model), along with a regularized version called APMR.

We describe two methodologies for obtaining segmented regression estimators from massive training data sets. The first methodology, called Linear Regression Tree (LRT), is used for continuous response variables, and the second and complementary methodology, called Naive Bayes Tree (NBT), is used for categorical response variables. These are implemented in the IBM ProbE™ (Probabilistic Estimation) data mining engine, which is an object-oriented framework for building classes of segmented predictive models from massive training data sets. Based on this methodology, an application called ATM-SE™ for direct-mail targeted marketing has been developed jointly with Fingerhut Business Intelligence [1]).

Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior. The models constructed are typically segmentation-based models in which the target audience is split into subpopulations (i.e., customer segments) and individually tailored statistical models are then developed for each segment. Such models are commonly employed in the direct-mail industry; however, segmentation is often performed on an ad-hoc basis without directly considering how segmentation affects the accuracy of the resulting segment models. Fingerhut BI approached IBM Research with the problem of how to build segmentation-based models more effectively so as to maximize predictive accuracy. The IBM Advanced Targeted Marketing-Single EventsTM (IBM ATM-SETM) solution is the result of IBM Research and Fingerhut BI directing their efforts jointly towards solving this problem. This paper presents an evaluation of ATM-SE's modeling capabilities using data from Fingerhut's catalog mailings.

1 Introduction Predictive modeling techniques are now being used in application domains where the training data sets are potentially enormous. For example, certain marketing databases that we have encountered contain millions of customer records with thousands of attributes per record. The development of statistical modeling algorithms for such massive data sets is important for emerging data mining application areas such as database marketing, targeted advertising, fraud detection, credit/risk modeling, and information retrieval.

The Boundary Element Method is a widely-used discretization technique for solving boundary-value problems in engineering analysis. The solution of large problems by this method is limited by the storage and computational requirements for the generation and solution of large matrix systems resulting from the discretization. We discuss the implementation of these computations on the IBM SP-2 distributed-memory parallel computer, for applications involving the 3DD Laplace and Helmholtz equations.