# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 1 Introduction  
The insurance sector by nature has been an intensively data-driven industry for many years, with insurance companies managing large quantities of customer data. The business of insurance is based on the analysis of data to understand and effectively evaluate risk. The insurer makes use of actuaries and actuarial science techniques to analyse insurance data to perform core roles. Therefore, insurance data can be claimed to be a dominant force in the sector [1]. Enhancing the quality of insurance data through appropriate pre-processing should improve the estimation process by increasing data quality. Examples of pre-processing tasks include handling of missing values [2], managing outliers [3], binning numerical data to create categories [4], and better handling categorical data correlation/association [5].  
The use of machine learning in the non-life insurance industry can be divided into three categories: actuarial, fraud detection, and customer behaviour. Actuaries carry out two tasks relevant to machine learning: calculating how much an insurance policy should cost, which is called pricing or _ratemaking_, and calculating how much money an insurershould set aside for the payment of future claims, which is called _reserving_. The nature of insurance data (especially aggregated data) and the detailed domain requirements means that bespoke domain specific models are required within the actuarial category. Fraud detection and customer behaviour (churn and propensity to buy) are however amenable to the application of more general machine learning techniques, such that advances in other domains can be applied to problems found in these categories. To give an example, techniques for detecting credit card fraud can be used to detect insurance fraud [6] and techniques developed for churning in the supermarket sector can also be applied to the insurance sector [7].  
In training datasets for supervised learning, redundant and irrelevant features have been demonstrated to affect the performance of learning models. Taking the commonly used Support Vector Machine (SVM) algorithm, the identification of important features significantly improves the robustness of SVM learning models [8], with such models being sensitive to noisy features. SVM models aim to optimise the kernel of the data, where these models suppose that the kernel matrix of training data is positive definite. However, the positive definite assumption cannot be ensured because of the influence of noisy features. Therefore, it is recommended to decrease the influence of noisy features to build robust SVM-based models [9].  
Insurance databases tend to contain multiple redundant and irrelevant attributes. These attributes negatively affect the accuracy of prediction of insurance reserve prediction techniques. Therefore, it is intuitively important to apply/embed feature selection prior to the creation of Machine Learning (ML) models in order to strip out low influence features. Furthermore, improving the model prediction power by feature selection and dimensionality reduction holds promise towards improving the processing and accuracy of many insurance problems such as insurance reserve prediction, customer retention, policy price, and insurance fraud detection. In our related works section we outline both the application of ML in the insurance sector and discuss various techniques used to implement feature selection. Above and beyond improved algorithmic performance, feature selection reveals the most and least influential features in a dataset for the model task in question. Domain experts can use these insights for practical purposes immediately or as a starting point for further investigations [10].