Applications of Clustering with Mixed Type Data in Life Insurance

by   Shuang Yin, et al.

Death benefits are generally the largest cash flow item that affects financial statements of life insurers where some still do not have a systematic process to track and monitor death claims experience. In this article, we explore data clustering to examine and understand how actual death claims differ from expected, an early stage of developing a monitoring system crucial for risk management. We extend the k-prototypes clustering algorithm to draw inference from a life insurance dataset using only the insured's characteristics and policy information without regard to known mortality. This clustering has the feature to efficiently handle categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contains observations, during 2014, of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having a lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders' attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.


page 14

page 15

page 18


Information Disorders, Moral Values and the Dispute of Narratives

In this paper we propose a framework characterizing information disorder...

A new nonparametric interpoint distance-based measure for assessment of clustering

A new interpoint distance-based measure is proposed to identify the opti...

Mortality in a heterogeneous population - Lee-Carter's methodology

The EU Solvency II directive recommends insurance companies to pay more ...

Skewed link regression models for imbalanced binary response with applications to life insurance

For a portfolio of life insurance policies observed for a stated period ...

Measuring performance for end-of-life care

Although not without controversy, readmission is entrenched as a hospita...

Synthetic Dataset Generation of Driver Telematics

This article describes techniques employed in the production of a synthe...

Can we trust the standardized mortality ratio? A formal analysis and evaluation based on axiomatic requirements

Background: The standardized mortality ratio (SMR) is often used to asse...

Please sign up or login with your details

Forgot password? Click here to reset