Abstract

The pension industry, much like the rest of the financial industry, is increasingly
adopting data science and artificial intelligence-based solutions. Applications range
from leaner and faster operations (“doing the same thing better”) to completely new
value propositions. However, the available literature suggests that the pension industry appears to be relatively conservative and cautious when it comes to adopting new
and dynamically changing machine learning (ML) techniques. The black box nature of
most ML techniques also appears to contribute to the skepticism of the pension sector.
Hence, there seems to be a gap between the potential applications of data science
solutions proposed by researchers and their application in the pension industry. This
article provides (i) a review of what has been reported in the data science literature,
(ii) a taxonomy of ML techniques that can be applied for challenges in the pension
industry, and (iii) a categorization of the different aspects of the pension industry that
are covered in state-of-the-art applied data science.
We surveyed 25 papers and presentations on the application of data science in
the pension industry and highlight the major machine learning techniques that were
used and their applicability in the pension sector. These techniques are concisely
introduced to provide a basis for stakeholders to gain an understanding of their
potential applicability to tackle challenges in the pension industry. Based on the
existing research, three areas of the pension industry are identified as most relevant
for the application of machine learning techniques: customer focus, organizational
process optimization, and personnel optimization. Open issues and further opportunities regarding the application of data science in the pension sector are discussed.
We surveyed the existing body of literature to summarize how data science is
being currently leveraged to deal with issues related to pensions. Prominent developments appear along the fronts of prediction and chatbot development. Our analysis
suggests that there remains ample room in the pension industry to explore the use of
other machine learning and data mining methodologies, such as clustering, natural
language processing, and reinforcement learning. This includes gleaning insights from
unconventional sources such as social media activity, and developing new customer-focused and business development applications.
Original languageEnglish
Place of PublicationTilburg
PublisherNetspar
Number of pages35
Publication statusPublished - 2021

Publication series

SeriesNetspar Design Paper
Number183

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