1. Insurance has always been viewed as a slow-moving industry but
recent technology integrations and with customer needs evolving;
this has created a significant shift. New technologies and innovations
have permeated the insurance industry and upped-the ante in quality
and efficiency for products, claims and business practices. This paper
looks to examine how ‘Big Data’ will influence the insurance industry
and suggests recommendations, strategy and tactics to implement
change, highlighting the benefits, risks and challenges associated to
these changes.
The Insurance Sector
Introduction
Identification of a viable trend
This paper examined numerous trends which are impacting currently
on the insurance sector, from studies conducted the paper decided to
focus on ‘Big Data’ and the impact of this revelation on the industry.
‘Big Data’ refers to datasets whose size is
beyond the ability of typical database software
tools to capture, store, manage and analyse.
This definition is intentionally subjective and
incorporates a moving definition of how big a
dataset needs to be in order to be considered
‘big data’ McKinsey6
.
Leading consulting firms, insurers, reinsurers and key stakeholders in the insurance ecosystem have
already started to highlight the importance and value of Big Data for the domain.
Swiss Re, the world’s largest reinsurer states that - “For insurers, big data offers the opportunity to
assess their customers’ needs, target products and services to individuals and businesses, and support
underwriting decisions. At the same time, it entails risks relating to the permissible and appropriate use and
management of customers’ data as well as the challenge of designing business processes and products
that will provide a profitable return on investment.1
”
Another report prepared by PwC2
considers Big Data as one of the four drivers of growth, for the future of
insurance industry, as illustrated in the diagram below included in their global study of Future of Insurance
1
‘Two-Speed’
Global Growth
Information
Advantage
through ‘Big
Data’
Distribution
Disruption
& Customer
Revolution
Evolving
Business
Models ‘Big
and Fast’
Where?
What?
How?
Why?
Social
Technology
Economy
Environment
Political
The new market dynamics
Source: PwC Insurance 2020, January 2012
Future of insurance
Big Data
Big Data
**
**

2. Insurers have already developed strategic paths
and created internal organizations to respond to
the trend of using Big Data. Global insurance major-
AIG- was one of the first off the block to recognize
the potential of Big Data and created the role of
Chief Science Officer to ensure suitable integration
of Big Data. This initiative has been well received and
recognized by both the sector and the academia
(Harvard Business School’s appended article)3, 4
The importance of Big Data to the sector can be
gauged from IBM’s report where they share that 74
percent of insurance companies surveyed, use big
data and analytics, which is creating a competitive
advantage for their organizations. This represents
a staggering 111 % increase in just 2 years.5
when
compared to an earlier report written in IBM’s New
Intelligent Enterprise Global Executive Study and
Research Collaboration.
Innovation is a critical matter in the Insurance
world; very real competitive threats could recast
the whole insurance industry, and diminish insurers’
roles in offering protection and risk management
product and services. The industry faces broader
challenges, such as demographic shifts, the rise
in power of the emerging markets and changing
customer behaviours. As well as social, technology,
economic, environmental and political factors that
will impact this sector.
Innovation & current industry gaps Conventional applications7
of Big Data within
insurance
1. Customer
insights- social
listening, mobility,
customer experience
management
2.Claims/ Risk
management- Fraud
management,
Predictive analytics,
telematics
However, the gap is beginning to narrow as more and more insurance companies are realizing the
benefits of using advanced analytics for designing products, segmenting markets, developing distribution
strategies, and managing business’s, setting assumptions for financial reporting, and developing metrics
for risk management. In fact, a growing number of insurance companies have developed a new area of
expertise to serve the increasing need for data analysis, predictive analytics, and behavioural simulations.
Strategy and tactics to make a change
2
Given the value proposition and direct impact on - top line (customer management, growth) and bottom
line (risk & underwriting management having direct relation to price and profitability), the benefits from using
Big Data is beyond the initial doubters phase.
There are numerous successful case studies, reinforcing broader research suggesting that when companies
inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5
to 6 percent higher than those of the competition.
The promised land of new data-driven businesses, greater transparency into how operations actually work,
better predictions, and faster testing is alluring indeed.9 Partially, due to the black box nature of Big Data,
there would be competing and conflicting forces at work to take the promise to fruition.
Debates are expected between in house technology teams, vendors, business people and CFO. To resolve
these known speed breakers and achieve success speedily, a calibrated strategy would be required that
reflects adequate cost-benefit-advantage too.
In 2014, LIMRA and PwC completed an extensive research study on customer
behaviour. A key finding of this study was that the life insurance industry lags the
property and casualty insurance industry (and both lag other industries) in using
advanced analytical techniques to better understand customers.
Big Data

3. 3
This paper recommends implementation of this 4 part strategy to
achieve success in Big Data
1. Leadership commitment-
Kick-starting a Big Data initiative
is easy but making it go through
the final stages is the challenge
every management must be
cognizant of and committed
to. Big Data involves not just a
new approach to working but
also calls for financial budgets,
culture shift, new skilling and
an overall plan to relook at
business. Leadership must be
fully committed to this initiative
and integrate Big Data in their
strategic plans
2. Data- a robust work plan to
assemble and integrate data
which is usually buried in silos or
perhaps is in a different format, is
a critical step. Critical data may
reside in legacy IT systems such
as customer service, pricing,
and supply chains while critical
information often resides outside
companies, in unstructured
forms such as social-network
conversations.
3. Analytics model- Advanced
analytic models are needed
toenabledata-drivenoptimization
or predictions.
4. Tools- intuitive tools that
integrate data into day-to-
day processes and translate
modelling outputs into tangible
business actions are the
cornerstone of any successful
Big Data strategy. Many
companies fail to complete
this step in their thinking and
planning—only to find that
managers and operational
employees do not use the new
models, whose effectiveness
predictably falls
Tactical steps are outlined below to accelerate progress of the
implementation strategy
Develop a road-map
to support sharing
and managing
content through tools
for data storage and
access methods
Identify and adopt the
technology to be able
to collect data aligned
to the business
strategy and goals
Seamless governance
compliance. Adoption
and usage of data
should follow the
standard and local
geography policies.
Continuously
develop a culture to
promote big data
leaders to facilitate
decision making,
insight discovery and
process optimization.
Other considerations that should be made when implementing a strategy to incorporate Big Data and
achieve success are detailed below
1. Gather business requirements before gathering data
2. Implementing big data is a business decision not IT
3. Use agile and iterative approach to implementation
4. Evaluate data requirements.
5. Ease skills shortage with standards and governance.
6. Optimize knowledge transfer with a centre of
excellence.
7. Embrace and plan your sandbox for prototype
and performance.
8. Align with the cloud operating model
9. Associate big data with enterprise data:
10. Embed analytics and decision-making using
intelligence into operational workflow/routine
Big Data
**

4. Benefits, Risks and Challenges of implementing recommendations
1. Speed to success- our recommended strategy
is a market tested, feedback looped and proven
to bring results. Integrating and channelizing
energies through this calibrated approach will
bring in speedier success to the business
2. All bases covered our recommended strategy
captures all critical elements reducing exposure to
any unknowns
1. Any Big Data implementation strategy calls for
leadership commitment, financial costs, culture
shift, suitable vendor on boarding and teaming of
the right skills. With an initiative as complex and
complicated as this, cost of risk of failure is high.
2. That Big data is used more to inform, strategic
direction and not so incorporated into operations,
impacting negatively on customer relations. It is
important that big data is used to inform all delivery
points of the organisation.
Benefits Risks
Identifying what you are trying to find out from Big Data and using this to implement a strategy. Big Data
is still perceived in some corners as a grey area. A successful Big Data implementation must support
business success which can only be generated if our vision and map for Big Data is clear from day one.
If recommendations are not implemented then organisations will not retain a critical competitive advantage,
the customer experience with the organisation will not be consistent across all consumer touchpoints and
a general loss of shareholder value will be experienced over a long period of time.
Challenges
The potential of Big Data and its value proposition
to Insurance sector, has been explored within
this paper and proven that implementation of a
big data strategy is key to retain a competitive
status within the industry. Insurers across Life and
Non-Life segment are already reaping rewards of
their efforts over the last few years and the most
successful evidence of Big Data driven insurance
companies is from the success of Oscar Health
Insurance in US, which is powered exclusively
around Big Data and Analytics. There are similar
insurers being set up in Asia and Europe, which
further testify to the promise of Big Data.
Conclusion
Bibliography
1. Swiss Re- Sigma No 2/ 2014
2. PwC’s ‘Life Insurance 2020- Competing for the Future’
3. LinkedIn, Harvard Business Review
4. Forbes
5. IBM- “Analytics: Real-world use of big data in insurance“
6. McKinsey- McKinsey Global Institute-Big Data: The next frontier for innovation, competition and productivity
7. Investopedia- http://www.investopedia.com/articles/investing/042915/how-big-data-has-changed-insurance.asp
8. Bain- ‘Global Digital Insurance Benchmarking Report 2015’
9. McKinsey- ‘Big Data- what’s your plan’
10. Information Management. 5 essential component of data strategy.
11. IBM- Big Data and Analytics Hub
4
Big Data
**
** all figures are representational