In its 21st CEO Survey1 pertaining to
the insurance industry, PwC states
that to drive innovation, companies
have to become 'bionic'
organizations where humans
communicate with machines to
improve business outcomes.
In the survey, 53 percent CEOs of
insurance organizations said they
aim to modernize the work
environment with digital tools, and
collaborative physical environments
to help employees develop the skills
necessary to deliver in such a
model. Forty-nine percent CEOs are
looking at strategic alliances or joint ventures with InsurTech companies
to speed up their alignment with the
digital marketplace.
Insurers have accepted the impact
of Robotic Process Automation
(RPA) and Artificial Intelligence (AI)
as forces of disruption and
innovation, and are mapping the
transformation of their
organizations to accommodate
these changes.
Traditional automation is limited to
automating simple, repeatable
tasks in back-end processes.
RPA combined with AI can be used to identify and deal with exceptions,
analyze large volumes of data
generated by internal and
external sources, and translate
them into insights that can trigger specific actions.2
For insurers, RPA and AI are no
longer measures toward only cost
reduction and efficiency. These
technologies are now used to
drive personalization, deliver
speedy service and ensure higher
degrees of self-service. We take
a look at some leading examples
and use cases across critical
insurance processes.
Underwriting & Pricing: Customer-centric Strategies
Connected devices such as sensors
and wearables are leveraging the
power of RPA, analytics and AI to
give insurers real-time insights
into customer-specific risks.
This is opening up avenues for
personalized, flexible covers. It is
also helping companies move from being risk insurers to risk
mitigators, with bundled services
for alerts, repairs and rewards.
Underwriting is a key focus area for
RPA and AI initiatives. It is
estimated that the market for
technologies for underwriting processes is set to grow by 60 percent by 2020.3 RPA is now
applied in the assessment of loss
runs, which forms the basis for underwriting and pricing of products. RPA-infused loss runs,4 with automated reporting of
several years of claims history of customers, can reveal otherwise
undiscoverable insights related to
previous losses. For example, LexisNexis' Data Prefill5 solution
helps insurers reduce costs, and
increase speed and accuracy of
quoting and underwriting by
pre-populating insurance applications using only a few
customer data points.
Cyber insurance is a growing
market with breaches expected
to cost businesses over USD 8 Trillion in the next five years.6 Insurers are now offering RPA-backed pre-loss mitigation
services to help detect and
prevent suspicious activities.
Examples include automated
password defense solutions,
online cybersecurity training,
and social media and dark
web scanning.
Marketing & Sales: Improving Brand Protection
There is an 'advice gap' in the insurance market7 created by
disintermediation, mass online
selling and falling premiums due
to online websites that offer price
comparisons. AI is expected to fill
the gap created due to the falling
profitability of most personal lines.
For instance, U.K.-based startup
SPIXII is a virtual insurance
manager that chats with customers
to understand their insurance needs
and recommend the best coverage for them.8 The chatbot can be
accessed via most messaging
platforms as well as a native app,
and can converse in six languages.
Robo-advisors are also being
leveraged to mine non-traditional data sources such as social media to
offer need-based, personalized
recommendations that can lead to
higher cross-selling and upselling
rates. When informed of a
customer's travel plans, the
SPIXII chatbot can detect whether
the customer intends to participate
in activities (such as skiing or
paragliding) that are not covered,
and recommend an additional cover
for the same.
Insurers are also leveraging
robots to help their network of
agents find product information
and sales collateral to enhance
sales. For instance, a leading
commercial insurer in the U.S.
has launched a virtual assistant
specifically to assist its agents seeking information on business
insurance products.
Insurance companies today
have to manage their brands'
online presence while complying
with stringent regulations. RPA
helps maintain logs of posted
messages, in real time, and
complete with context to enable compliance reviews.9
An automated pre-approval
workflow ensures that employee
posts are first passed into the
compliance system where they are
automatically sent for approval.
Messages that are approved are
posted directly, while others are
formally reviewed and appropriate
actions taken.
Policy Administration & Servicing: Enhancing Customer Experience
RPA leverages AI and advanced
analytics to link back-end
processes to the front end in a
smart, contextual manner. This
augments the productivity and
effectiveness of customer-facing
agents by helping them access
the right information across
back-end systems faster, thus
improving resolution rates and
customer experience.
Agent-facing chatbots can identify
the customer before the agent even
answers the call, process queries in
natural language and make
proactive suggestions based on the customer's profile. Customerfacing
chatbots can answer
questions about policy status and
payments, enable automated policy
renewals, and even alert customers
in case of pre-loss warnings based
on sensor data, and facilitate
preventive repair and maintenance.
In terms of reporting and
compliance requirements, RPA
overcomes barriers of disparate
systems and multiple formats,
and creates records of all actions
and transactions to quicken
reviews, reconciliations and
compliance checks.
ClearPay,10 a Canadian payments
and reconciliation services
provider for insurance companies
(carriers and brokers), is using
RPA and AI to fully integrate the
settlements process and provide
real-time information on payments
in standardized reports.
The data is transferred
in real time from broker
management systems and
assembled in standardized
reporting formats to help brokers
fulfill contractual reporting
requirements and enable faster
reconciliation by carriers.
Claims & Fraud Detection: Driving Speed, Transparency & Accuracy
With customers' claims experience
emerging as one of the most
important metrics for insurers, it is
perhaps not surprising that the
estimated market for technologies
for improvements in the claims
process is expected to surpass USD 72 Billion by 2020.11
RPA can cut down the cost of a
claims journey by as much as 30 percent.12 RPA and AI can enable
self-service, and bring in
transparency and speed in the
claims process. Digital interfaces,
backed by chatbots, can offer
personalized guidance on the
submission of important information
and provide clear feedback to
customers on the next steps.
While the use of drones in property
and casualty insurance to assess
damages is a well-established use
case by now, InsurTech is now
exploring the use of 'machine vision'
in claims settlement.
A leading U.S. insurer has been
experimenting with an app-based
feature that uses smartphone
cameras to help customers
involved in accidents assess the
damage to their car in real time.
The app leverages AI that has been trained on thousands of images,
and can also provide cost estimates
for repairs.
Other AI products help drive down
insurer costs by assessing the most
accurate claims settlements.
U.K.-based software company
Tractable has an AI review program
that checks thousands of repair
estimates every minute, flagging
unnecessary repairs and arresting auto claims leakage.13
Fraud detection and prevention is
perhaps the process area with the
widest adoption of RPA and AI
technology across the industry.
Solutions are being leveraged
across the spectrum of claims
assessment and fraud detection to
enable early detection of trends and
suspicious claims.
InsurTech company Guidewire14 delivers predictive analytics
backed with RPA and machine-learning
to score claims in real time, routing and assigning
claims in the workflow, and
identifying claims that may
require litigation or subrogation.
In effect, it accurately fast tracks
correct claims, delivering higher
process returns.
Shift Technology, another
InsurTech company, has a
software that delivers actionable
insights on which indicators make a claim suspect.15 The model
takes the context of the claim in
consideration to intelligently
increase or decrease the weight
of suspicious indicators.
Even as insurers get their AI and
RPA strategies in place, there
is growing focus on concurrent
transformation in employee
skills. This is necessary to
ensure a long-term, continuously
evolving framework of innovation
and productivity.
Higher business involvement,
establishing a center of
excellence, and a more practical
and calibrated approach toward
adoption — from proof of concept
to business case and pilots — will
be some of the important features of successful adoptions.16
References:
1. https://www.pwc.com/gx/en/ceo-agenda/ceosurvey/2018/gx/industries/insurance.html
2. https://www.horsesforsources.com/enterprise-robotics-forecast_2016-2021
3. https://www.statista.com/topics/4116/insurtech/
4. http://blogs.lexisnexis.com/insurance-insights/2016/09/underwriting-decisions/
5. https://risk.lexisnexis.com/products/data-prefill
6. https://www.juniperresearch.com/press/press-releases/cybercrime-to-cost-global-business-over-$8-trn
7. https://www.businessinsider.com.au/fca-robo-advisors-could-fill-financial-advice-gap-2016-3
8. http://www.digitalinsuranceagenda.com/102/spixii-making-insurance-simple-accessible-and-personal-for-everyone/
9. https://www.insurancebusinessmag.com/uk/opinion/easing-the-burden-of-social-media-compliance-in-insurance-91488.aspx
10. https://www.canadianunderwriter.ca/inspress/one-thousand-users-benefiting-clearpay-transactions/
11. https://www.statista.com/statistics/667702/forecasted-global-market-size-insurtech-industry-by-improvement-type/
12. https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services
13. https://tractable.ai/products/insurance/
14. https://www.guidewire.com/sites/default/files/media/pdfs/Predictive_Analytics_for_Claims_data_sheet.pdf
15. https://www.intelligentinsurer.com/news/revolutionising-claims-handling-and-fraud-detection-with-ai-13318
16. https://fsinsights.ey.com/big-issues/Digital-and-connectivity/get-ready-for-robotic-process-automation