  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.
