# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 2 Related Works
### Machine Learning Applications in Insurance Sector  
As mentioned previously, one of the main applications of machine learning in insurance is in actuarial tasks. While both reserving and pricing actuaries have long made use of linear models, research into the applicability of newer, more advanced, and non-linear techniques has been slow, and it is only relatively recently that this area has attracted much attention [11]. In both ratemaking and reserving, two key motivations behind research into machine learning techniques are (1) the realisation that the factors determining the appropriate rate or reserve are too complex to be modelled in a linear fashion and (2) the availability of more data both in terms of quantity and diversity (including text, image, and telematics) [11; 12; 13]. In ratemaking, the traditional method is Generalized Linear Models (GLM) [14; 15]. The response variable of these models can have a Gamma or Poisson distribution [16]. This makes them particularly suitable for ratemaking as both claims severity and frequency need to be estimated and claim severity is often Gamma distributed, while the Poisson distribution is used to fit claim frequency [16]. Generalized Additive Models (GAM) go beyond GLMs in the ability to find non-linear relationships and have been explored firstly by [PERSON] et al. [17] and more recently by [PERSON] et al. [18]. Neural networks have been part of the research into ratemaking for more than 20 years and have also been studied more recently; they show particular promise in modelling non-linear relationships that are not captured by more basic models [11; 19; 20]. [PERSON] et al. [11] mention an issue we have encountered in our own study; machine learning models, particularly neural networks, require large datasets. However, the confidential nature of insurance data means that large publicly available datasets are few and far between [11]. The other actuarial function, reserving, has been dominated by the chain ladder method. [PERSON] [21] gives a good overview of the early debate on this topic. The key to the chain ladder method is reserve triangles. Reserve triangles are matrices that track accumulating claims data (number of claims and value) over time. A stochastic process is then used to generate the run-off matrices from the reserve triangles. These run-off matrices are used in estimating the final reserve value. Finally, stochastic regression models are used in predicting the insurance reserve values for these claims. The models aim to predict the future development (frequency and value) of claims based on previous patterns. While the models perform adequately for large claim portfolios, they cannot cope with changes in patterns of behaviour that give rise to claims, whether social, economic, or random. These models also work with aggregated data, which means data related to individual claims or small groups of claims is not taken into account. This in turn means that the output of these models has little use for reserving at an individual claim level [10; 11; 12]. In addition, non-structured data which contains information about factors affecting the value of claims and hence the appropriate reserve is lost to the model. Research has addressed these issues. Machine learning techniques that are not dependent on past behaviour continuing into the future have been applied to reserve prediction. Tree based techniques, SVM, neural networks and deep learning techniques are among the approaches [22; 23; 24; 25]. These techniques can also utilise a greater range of features combining numerical, categorical and textual data in the same model. Attention has also been paid to applying machine learning techniques at an individual claim level [26; 27]. Insurance fraud detection is another area within the insurance domain in which machine learning can play a part. As mentioned previously, fraud detection in the insurance domain can be viewed as an industry specific application of more general machine learning techniques rather than a unique problem.