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Abstract. Underwriting is an important stage in the life insurance process and is concerned with accepting individuals into an insurance fund and on what terms. It is a tedious and labour-intensive process for both the applicant and the underwriting team. An applicant must fill out a large survey containing thousands of questions about their life. The underwriting team must then process this application and assess the risks posed by the applicant and offer them insurance products as a result. Our work implements and evaluates classical data mining techniques to help automate some aspects of the process to ease the burden on the underwriting team as well as optimise the survey to improve the applicant experience. Logistic Regression, XGBoost and Recursive Feature Elimination are proposed as techniques for the prediction of underwriting outcomes. We conduct experiments on a dataset provided by a leading Australian life insurer and show that our early-stage results are promising and serve as a foundation for further work in this space.

Analysis of claims and risk management is the key task to avoid frauds and to provide risk management in the life insurance industry. Though visualization plays a fundamental role in supporting analysis tasks of the business domain, exploring user behaviour remains a challenging task. The prevalence of natural language interactions aided with data visualization has become quite the norm. With the increasing demand of visualization tools and varying level of user expertise, it comes as no surprise the use of natural languages interface. However, the design of visual analytics tools aided with natural language interfaces (NLIs) for risk management and claim analysis requires thorough task analysis and domain expertise. In this work, we investigate an alternative approach through a natural language interaction based interactive visualization such as chart, pie, and histogram, which can be applied for analyzing insurance claims and risk management. We design a new visual analytics solution (VAS) named (InsCRMVis 1 ) aided with NLIs. We present an expert evaluation