AI and Machine Learning

1. InView : AI in insurance
The last 10 years have been about building a world that is mobile-first. In the next 10
years, we will shift to a world that is AI-first. - Sundar Pichai, CEO of Google, Oct 2016

2. Artificial Intelligence (AI)
Commentary & insights
AI has the potential for modernizing, streamlining and simplifying the siloed
design of the financial industry.
Effectiveness of AI has been transformed recently due to the development of
new algorithms, greater availability of data, better hardware to train them and
cloud-based services to catalyze adoption among developers.
Key reason that use of AI is expected to grow within the insurance sector is the
scale of the data the industry handles – AI inherently needs data to train and
learn. More the data, better the outcome.
Background
In essence, AI is about embedding human intelligence into machines, enabling
systems to learn, adapt and develop solutions to problems on their own.
Different AI base technologies, such as natural language processing (NLP),
computer vision, robotics, machine learning and speech recognition, are
currently being leveraged to develop systems that can think, learn and adapt.
With Forrester predicting investment in artificial intelligence to grow by 300% in
2017, the key focus should be on the insurance industry due to AI’s potential to
enhance automation, reduce cost, lower risk, increase productivity, and facilitate
better and faster decision-making.

3. AI in insurance - Agenda
What is AI? Opportunities Insurance Use
cases &
Industry
Activity
Adoption
Challenges
AI Regulation

4. AI technology – The Basics
What is AI
AI is the science and engineering of making
intelligent machines, especially intelligent
computer programs.
AI is based on disciplines such as Computer
Science, Biology, Psychology, Linguistics,
Mathematics, and Engineering with the
ultimate aim to create expert systems that
can understand, think, learn, and behave like
humans.
AI is not a monolithic subject area and
encompasses various streams like Machine
Learning, Neural Networks, NLP, Social
Network Analysis, visualization, robotics etc.
AI is gaining in importance because it
tackles profoundly difficult real world
problems, and the solutions to those
problems can be applied across sectors.
With so much activity around AI
experimentation and implementation,
insurers can no longer afford to overlook AI
and its implications.
Components of AI
Source - http://www.besttechguru.com/future-technology-explained-what-is-artificial-intelligence/
Intelligent
Multi-
Disciplinary
All
Encompassing
Real-World
Modeling
Hard To
Ignore

5. Disrupting (& Improving) Insurance
What does AI mean for Insurance? How AI can benefit Insurance
Source - http://www.tcs.com/SiteCollectionDocuments/White%20Papers/Artificial-intelligence-reimagines-insurance-0816-2.pdf
Robo Advisors, Virtual Assistants, AI enabled
authentication
Need Based Offering, Personalisation and
Intelligent Pricing
Predict Customer Needs and Behavior, Quick
Decision Making, Optimize Sales Strategy
Automate & Optimize Underwriting Model,
Real-time Underwriting
FNOL Damage Assessment, Fraud Detection,
Assistance and Quick Processing
Operational
efficiency
Revenue
expansion
Customer
experience
Competitive
advantage

6. Xtra by AXA – AI driven personal wellness coaching App Amazon Alexa and Echo – Embodiment of AI
Insurance Use Cases
Fukoku Mutual – Replacing humans with AI system
Fukoku Mutual is replacing its 34-
strong claims assessment workforce
with an advanced form of IBM’s
Watson Explorer AI
Fukoku’s Watson system can analyze
and interpret claim data including
unstructured text, images, audio and
video to decide policy payouts
Alexa is Amazon’s pioneering virtual
assistant that powers Amazon’s Echo
and other voice recognition devices
Liberty Mutual is offering Alexa skill
to help users navigate the insurance
quoting process
Aviva has developed Alexa skill which
can answer questions about insurance
Axa’s Xtra is imbued with an
“intelligent” bot Alex that helps
monitor daily activity, manage your
weight, provide healthy dish recipes
Alex is not just a reactive chat-bot as
it can also provide suggestions
Alex demonstrates the evolving
nature of consumer engagement
 In just 3 seconds, immediately
after the customer touched Submit
on their phone, Lemonade AI bot Jim
reviewed the claim, cross-referenced
it against the policy, ran 18 anti-fraud
algorithms and issued wiring
instructions for the full amount less a
$250 deductible
Lemonade – New Claims settlement paradigm

7. AI Insurance Participants
AI Testers (Insur)Tech to watch
Incumbents AI Initiative
USAA trialed IBM Watson to advise its
clients on topics including insurance
Allianz has partnered with Baidu to leverage
AI to boost innovation in China
Microsoft is deploying machine learning in
GAFFEY’s claims automation
Transformed authentication mechanism
from password to voice recognition
Geico has integrated a bot Kate into its app
which offers advice about insurance policy
Zhong An has launches Blockchain and AI-
focused technology incubator
When customers visit Buyonic, they are
greeted not by a human but android Siber
Insur(Tech) AI Initiative
SPIXII is building customer-centric automated
experiences using an in-house hybrid bot
Brolly is UK’s first artificially intelligent insurance
advisory application
Sure has introduced the world's first on-demand
insurance app powered by artificial intelligence
Insurify enables customers to generate price
quotes by texting a photo of their license plate
Tractable develops proprietary AI algorithms that
can learn and perform expert visual tasks
RiskGenius leverages both ML and NLP to parse
policy terms in order to identify coverage gaps
MotionsCloud estimates claims damages by
analysing text, audio, image and video inputs
Baidu has integrated its Melody AI into the
popular iOS and Android Baidu Doctor app
IPSoft’s Amelia automates insurance processes
such as claims - is being tested by several insurers

8. AI adoption challenges
Key challenges to adoption AI Regulation – The New Cause of Worry
Never-ending “learning loop”
Huge upfront resource cost
The Reliability Issue – Remember Tay, the
Microsoft AI bot who turned racist
The man vs. machine conundrum
Privacy concerns
Regulatory hurdles
The new EU General Data Protection Regulation states
that individuals:
“shall have the right not to be subject to a decision based
solely on automated processing, including profiling, which
produces legal effects concerning him or her or similarly
significantly affects him or her”.
Other geographies
may come up with
more stringent rules
Such surprises may
stifle AI innovation

9. Contact The Digital Insurer: hugh.terry@the-digital-insurer.com
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