In the insurance industry, timely and effective interaction with customers are at the core of everyday operations and processes that are key for a satisfactory customer experience. These interactions often result in sequences of data derived from events that occur over time. Such recurrent patterns can provide valuable information that can be used in a variety of ways to improve customer related work-flows. In this paper we demonstrate the application of a recently proposed algorithm to uncover such time patterns that takes into account the time between events to form such patterns. We use temporal customer data generated from two different use-cases (satisfaction and fraud) to show that this algorithm successfully detects patterns that occur in the insurance context.

Recommender systems have become extremely important to various types of industries where customer interaction and feedback is paramount to the success of the business. For companies that face changes that arise with ever-growing markets, providing product recommendations to new and existing customers is a challenge. Furthermore, it is important to have an algorithm which is descriptive, scalable, agnostic to missing features, and robust in providing these recommendations. Directed graphical models meet all these demands; however, if the dimensionality of the features is high, structure learning and inference can become computationally prohibitive. In this work, we propose an algorithm with some novel aspects to learn the structure of a graphical model (e.g. Bayesian network), which considerably speeds up both training (from days to minutes in some cases) and inference run-times with respect to standard Bayesian structure learning approaches, while achieving similar accuracy. We also show that this approach produces more accurate predictions than a state-of-the-art matrix factorization algorithm in the absence of complete evidence on several insurance-related datasets.

In this paper we describe a deployed recommender system to predict insurance products for new and existing customers. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they were adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our system. Experimental results show advantages of using probabilistic graphical models over the widely used low-rank matrix factorization model for the insurance domain.

Timely identification of potentially dissatisfied customers enables us to take meaningful interventions to improve customer experience. The goal of this work is to create models that can predict customer satisfaction for active insurance claims at any point in time during the claim process. In order to capture relevant temporal information, we introduce the concept of a "journey-map": a data-driven structured timeline where all the relevant events pertinent to the claim process are registered and positioned temporally with respect to each other. We also describe a machine-learning-based framework to extract and discover meaningful information relevant for the task at hand. The result of this work is a deployed system currently used during the claims process.

Coronary Heart Disease is the single leading cause of death world-wide, with lack of early diagnosis being a key contributory factor. This disease can be diagnosed by measuring and scoring regional motion of the heart wall in echocardiography images of the left ventricle (LV) of the heart. We describe a completely automated and robust technique that detects diseased hearts based on automatic detection and tracking of the endocardium and epicardium of the LV. We describe a novel feature selection technique based on mathematical programming that results in a robust hyperplane-based classifier. The classifier depends only on a small subset of numerical feature extracted from dualcontours tracked through time. We verify the robustness of our system on echocardiograms collected in routine clinical practice at one hospital, both with the standard crossvalidation analysis, and then on a held-out set of completely unseen echocardiography images.