Revolution of Emerging Technologies

Chapter 1: Introduction
1.1 Research Background
The insurance and financial services industry is flourishing due to advancement and revolution
of emerging technologies. It has directly provided new opportunities within financial markets to
create values for business organisations. It has directly helped insurance sector to improve their
process management, whereas it has helped financial companies to achieve competitive
advantage within competitive markets. These advanced technologies are continuously
developing which are helping to make the business processes more accurate and precise. These
technologies are making businesses more intelligent, whereas it is boosting their technical
abilities to innovate their internal processes (Riikkinen et al., 2018).
This study mainly focuses to identify the key drivers linked with demand of new technology
within the insurance sector. Artificial intelligence is an example of emerging technology which is
being increasingly used in financial sector enabling the companies to make their processes
smarter and robust. There are many advantages of AI for insurance industry such as creation of
business models, expansion of insurability, improvement of efficiency, cost reduction, better
engagement with customers, controlling the price risks, and measuring performance (hall, 2019).
In today’s world where most of the processes are digitalised, insurance customers are not very
satisfied with this experience. Insurers are lagging behind in using AI to use its full potential to
get benefits of better operations and creating value to their business (Deloitte, 2017) However,
the industry has started to invest in AI to yield the benefits of this technology in their business.
The main insurance business areas that can be benefited from AI are sales and marketing, risk,
and operations (Shroff, 2019).
According to Keefer (2019), speed of claim settlement has always been a priority of insurance
companies to retain customer satisfaction. It is very important for an insurance organisation,
whether is it small or large to be efficient in all aspects of claims management. However, many
insurance companies have considered technological advancement to improve their claim
management process. The artificial intelligence and machine learning remained the most

prominent technologies which have helped all types of industries. Yet, it is still required to
evaluate how artificial intelligence helps in improving the claim management process by
insurance companies.
1.2 Research problem
Due to technological advancement, business organisations are mainly relying on the advanced
technologies to improve their operations, performance, and day to day tasks. The key reason
behind relying on machines is linked with limitations involved with human handling the
operations. The operations managed by human involve high risks, whereas they are considered
as slower and costly. The advanced technologies can directly helps to improve the operations in
all types of industries (Chummun, 2018). However, there have been limited researches
conducted to identify which specific technological advancement is suitable for specific
companies. The selection of advanced technology mainly depends on different factors such as
nature of operations, geographical location, market orientation, and perceived usefulness of
technology (Dirican, 2015).
Similarly, artificial intelligence has completely revolutionised the operations of all types of
industries (Kumar et al., 2019). Majority of researchers have focused on importance and benefits
of AI for service sector industry such as travel and tourism, disruptive industry, health care, and
others (Wisskirchen et al., 2017). The concept of AI is still developing for specific types of
industries such as insurance, banking, and other industries. The adoption rate of AI remained
lower within these types of industries due to complexities involved with AI. The concept of AI
for claim management is particularly new for most of insurance companies (Mohapatra and
Tiwari, 2009). The key reason is that AI adoption requires high investment, whereas only big
insurance companies can afford the AI technologies. This is the reason that researchers have less
focused on evaluating the benefits of AI for claim management process. Moreover, there is
limitation of research which mainly focuses on how AI can expedite the whole claim
management process (Kumar et al., 2019).

1.3 Aim and Objectives
The aim of this research is to analyse value of artificial intelligence in achieving improved
claims management process in insurance sector. The scope will include but not restricted to
responses and engagements of UK insurance sector in general in claims management process in
specific. The objectives will include:
 To identify key factors of successful claims management process in insurance
 Critically explore the potential of Artificial Intelligence in insurance specifically in
claims management process.
 Barriers and challenges to implement Artificial intelligence in Claims management
process by comparing the theoretical potential to its actual potential
 To make recommendation to overcome those challenges and how it improves the Claims
Management Process
1.4 Methodology:
Interview and documentary methods is used to collect qualitative data. Direct conversation with
participants to gather data from individuals are interviews (Keith Francis Punch, 2014).
Documents can be all sorts of forms of physical or written or visual data that collect from
secondary data sources or primary sources (Moen and Middelthon, 2015). Primary data is
collected through interviews and secondary and primary data is collected through documentary
approach of data collection related to AI and its implication in claims management.
Semi structured interviews is conducted with open ended questions listed. Questions can be
asked in any order depending upon participant’s response. The interview data was collected from
five respondents from three key insurance companies. This helped the researcher to collect and
then critically analyse the opinions of insurance professionals about Artificial Intelligence and its
impact on claims management. The main participants were from claims department. Also, all
participants have sufficient knowledge about artificial intelligence and its implications within
claim management process.

1.5 Structure of dissertation
There are five chapters in this dissertation to achieve the above discussed research objectives.
 Introduction: This chapter explains the overview of research and problem statement.
The key emphasis is made to explain how interview methodology is adopted to achieve
the planned research aim and objectives.
 Literature review: This chapter explains the importance of claim management process
for insurance sector. The key emphasis is made to analyse the key opportunities and
challenges involved with artificial intelligence within insurance sector.
 Methodology: This chapter adopts Saunders research onion model to explain the key
components considered with selection of research methodology. It also explains how
interview methodology was adopted along with research philosophy, approach, and
choice.
 Analysis: This chapter presents and analyse the data collected from asymmetric email
interview. It also explains how thematic coded analysis can be adopted to analyse the
collected data from interviews. There were ten themes generated from the data collected
from respondents of interview.
 Conclusion: This chapter concludes the whole dissertation, whereas key
recommendations are provided to explain how AI can improve claim management
process of insurance companies.