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MODERNISING UK GENERAL INSURANCE PRICINGGI Pricing Solutions to Win in a Digital Age
Customers of digital age demand competitive price and tailored products
General insurance (GI) purchase has historically been perceived as a ‘one-o� ‘ annual decision by most UK consumers. Over last two decades digital technologies – like aggregators and new age insurers – have transformed the customer journeys
COMPETITIVE PRICING IS ESSENTIAL TO RETAIN CUSTOMERS
of UK consumers actively shop
around at renewal
of UK consumers willing to share personal data
(location, lifestyle) if it reduces
premium
Customers usePrice Comparison
Websitesto shop around
Customers are open to share personal data
73%
40%
COMPETITIVE PRICING
NEW AGE INSURERS BRINGING NOVELTY IN PRODUCT DESIGN
.........................................................................................................................
Personalised experience
Emergence of digital-native
millennialsAdvent of new
age insurers brought in
novel products like pay per use insurance
Digital savvy millennials (1/3rd
adult base) typically rent rather than
own car/home. This poses a unique
pricing challenges due to their
non-standard insurance needs
NEW AGEINSURERS
INTERNAL AND ENVIRONMENTAL CHALLENGES RESHAPING GI PRICINGINTERNAL CHALLENGES
ENVIRONMENTAL CHALLENGES
Based on a super complaint about loyalty pricing (Sep 2018) , FCA
has set out a package of remedies to enhance governance , control
and oversight on pricing. The focus has been on bringing reforms to make insurance
renewal process more transparent. Their upcoming
reforms (Jun 2020) might ban or restrict practices like price
optimisation and auto renewals, resulting in a negative impact on
loss ratios
Premiums have not increased in tandem
with rising repair costs due to steep
competition
Possible increase in claim costs in
absence of EU’s Freedom of Services Act
Uncertain impact of Brexit on UK economy. A weaker
economy would lower demand for insurance and
put pressure on pricingand margins
Data integrationand usability is a key
challenge due to organisational silos
Pricing model refresh is slow due to
significant manual steps
Insurers need to ensure existing employees don’t feel alienated
while new technologies are adopted and some of traditional processes
get transformed
Insurers competing with other digital industries
to hire data scientist/engineers
Implementing ML/AI based solutions or
heavy data processing is not feasible for
many insurers due to lack of requisite data
and legacy technology stack
Data accessibility
and readiness is still a key challenge
Hiring talent for in-house
capability development
very competitive
Technology-stack incapable
to supportbig data and ML
solutions
FCA remediation of
pricing practices
Competitive pressure on
pricing
Post Brexit uncertainty and
operational ine�iciencies
IMPERATIVES FOR GI
PRICING
Single customer viewInsurance being low contact business, Pricing needs integrated customer intelligence across functions - marketing, risk, claim, call center
IT modernisation – a priorityTransition from mainframe to modern
cloud-first technology framework to enable faster processes and
Analytics/AI Leverage 3rd party advantage
Superior usage of 3rd party data and
technology can improve risk
assessment, reduce fraud and claim cost
Focus on data readinessTransition to structured data warehouse/ processes/ routines for data treatment for improving productivity of pricing teams
Partner with InsurtechsBuild novel products and value
added services in partnership with Insurtechs to expand revenue base
e.g. pushing commission-richadd-ons through bundled products
Reactive to proactive pricingKeeping abreast of
market trends to dynamically move
underwriting priorities and implement measures
for pricing leadership
FOUR PILLARS OF GI PRICING PRACTICE TO WIN IN DIGITAL AGE
Data quality and readiness at the core of
pricing practice
Case: EXL helped a leading UK insurer create and execute a strategic roadmap for data architecture
Models use data across all touchpoints through integration across customer journey for more contextual pricing
Enrich prediction with novel external data sources
Enable the use of PCW Data for Competition Pricing Insights
Creating single customer view
Implementing BI and analytics layers on integrated DataMart
Identifying use cases where transformed data architecture would unlock significant business value
Contact us and learn moreWriters
CONTRIBUTERS
Shubham JainSenior Engagement Manager, Insurance Analytics, [email protected]
Tamal ChandraProject Manager, Insurance Analytics, [email protected]
Kshitij JainPartner and Head of Data Analytics - UK & [email protected]
Siddharth BhatiaVP Insurance Analytics [email protected]
Explore beyond linear models for superior
personalisation of pricing
Machine learning for finer detection of decision boundaries
Peril level technical pricing and brand level retail pricing models
Optimal frequency of pricing model refresh
E�ort reduction and accuracy improvementCase: EXL helped an insurer automate entire lifecycle of the Model Factory
Reduced coding
Faster speed to market
Enhanced validation
Robust compliance
checks in place across testing
and model development
Adequate testing coverage and speed to market for price change deployments while adhering to regulations
Explainable ML Model
Case: EXL proposed an automated testing framework for an insurer to create testing data, execute the test and showcase results in a seamless way
Case: Use of explainable ML Models in Insurance and Banking domain
Develop analytics solutions to target
high value customers and improved loss
ratio
Case: EXL developed a reusable rating optimization platform for a motor insurer creating significant increase in revenue
Case: :EXL helped an insurer transition from rule based customer value to RADAR based model (faster, easy to visualize)
Develop data driven tools and platforms that leverage cutting edge analytics to optimize business decisions/processes.
There are several key changes in customer behavior that insurance pricing needs to address
