Thirteenth edition, July 2018
Artificial intelligence is the new electricity. We hear it will fundamentally shift the balance of power between labor and
capital, mostly by rendering labor obsolete. It will enable and empower transformative technologies that will rearrange the sociopolitical landscape and may lead to humanity’s transcendence (or extinction) within our lifetimes. As it changes the world it will necessarily rewrite the rules of insurance. That’s the myth, and the nature of the headlines.
Global Insurance Market Opportunities
Demystifying Artificial IntelligenceBy Paul Eaton, Aon
About the GIMOSince its launch in September 2015, the Global Insurance Market Opportunities report has quickly become a leading thought leadership study and reference document for the re/insurance industry.
In 2018, we are taking a new approach to its distribution by publishing articles throughout the year under the banner of Global Insurance Market Opportunities, rather than launching the single, compre-hensive report. In so doing, we aim to increase its utilization, bring our ideas to market as fast as possible to support further develop-ment with our re/insurance client partners, and make it easier for GIMO readers to digest the wealth of content generated annually.
Interestingly, insurance is heavy on intellectual property (think of proprietary underwriting models), technology, and data. And AI is hungry; hungry for data, of course. But also hungry for systems that can be automated and for proprietary classification problems that can be improved. That places insurance right in the appetite of artificial intelligence and its promise of transformation. If we want to act on artificial intelligence’s transformational potential, we need to understand what it actually is, separate the technologies from the hype, and develop a practical understanding of what is required to implement AI powered solutions in the insurance sector. This article will highlight these three steps and offers a realistic approach for carriers to take advantage of the opportunities.
AI is hungry; hungry for data, of course. But also hungry for systems that can be automated and for proprietary classification problems that can be improved. That places insurance right in the appetite of artificial intelligence and its promise of transformation.
Defining Artificial IntelligenceUnfortunately, our first step is also our hardest,
as a working definition of artificial intelligence
is difficult to assess. The scope of the term AI
is broad and it requires careful consideration
to avoid becoming hopelessly confounded
with its own hype. It is also challenging to come to a clear definition of natural intelligence, which leaves us struggling for a definition of artificial intelligence because the latter is so often compared to the former.
AI tends to be discussed in two flavors. The first is general artificial intelligence (also, artificial general intelligence and strong AI). GAI is machinery capable of human level cognition, including a general problem-solving capability that is potentially self-directed and broadly applicable to many kinds of problems. GAI references are accessible through fictional works, such as C-3PO in Star Wars or Disney’s eponymous WALL-E. The most important feature of GAI is that it does not currently exist and there is deep debate about its potential to ever exist.1
The second is usually referred to as narrow
AI. Narrow AI is task specific and non-
generalizable. Examples include facial
recognition on Apple’s iPhone X and speech-
to-text transcription by Amazon’s Alexa.
Narrow AI looks and feels a lot like software or,
perhaps, predictive models. Narrow AI can be
described as a class of modeling techniques
that fall under the category of machine learning.
What is machine learning? Imagine a set of
input data; this data has one or more potential
features of interest. Machine learning is a
technique for mapping the features of input
data to a useful output. It is characterized by
statistical inference, as advanced techniques
often underlie machine learning predictive
models. Through statistical modeling,
software can infer a likely output given a set of
input features. The predictive accuracy of
machine learning methods increase as their
training data sets increase in size. As the
machine ingests more data, it is said to learn
from that data. Hence, machine learning.
Perhaps most important of all, machine
learning (as an implementation of narrow AI)
is real and here today; for the remainder of
our discussion when we say, AI, we mean
narrow AI or machine learning.
Beyond the HypeThe hype around AI and its potential is
extensive. Silicon Valley billionaires opine on
the potential implications of the technology,
including comparing its power to nuclear
weapons.2 Articles endlessly debate if and
how quickly AI will structurally unemploy
vast swaths of white collar workers. MIT’s
Technology Review provides a nice summary
of the literature in which they state that up to
half of all jobs worldwide could be eliminated
in the next few decades.3
AI may well have this kind of impact. And the
social, political, and economic implications of
that impact, especially around questions of
potential large-scale unemployment, deserve
careful long-term consideration. However,
executives and business owners need to
evaluate technology investments today to
improve their current competitive position.
From that perspective, we find it more practical
to focus on examining which existing tasks
could be automated by AI today.
2
Demystifying Artificial Intelligence
Machine learning is mapping a set of inputs to a target output.
Inputs: Radar, Lidar, Images
Output: Positions of cars, roads
Narrow AI looks and feels a lot like software or, perhaps, predictive models
3
Demystifying Artificial Intelligence
Enter pigeons.In 2012, researchers trained pigeons to
recognize people based only on their faces as
part of a study on cognition.4 Suppose you
had millions of face-recognizing pigeons; this
force of labor could be deployed in a compre-
hensive facial recognition system. A system
remarkably similar in function to the facial
recognition AI of devices like modern smart
phones. It turns out pigeons have also been
trained to recognize voices,4 spot cancers
on X-rays,5 and count6 among a host of
other tasks related to headline-grabbing
AI achievements.
The metaphor is admittedly silly. Instead of
pigeons, imagine an army of virtual robots
capable of classifying information from the
real world to produce a machine-readable
data set. In machine learning language, these
robots take unstructured data and make it
structured. Said robots resemble the auto-
mation machinery of a factory; like spot
welders tirelessly joining steel members to
form automobile frames, our virtual robots
tirelessly recognize if a face is featured in a
photograph. In contemplating the question,
what could be automated with AI, a useful
starting place is the army of robots (or
pigeons!). For example:
What existing analyses could be improved or optimized? Could pricing or underwriting be improved
using better classifiers or non-linear modeling
approaches?
What data currently exist at the firm that could be made available for new types of analysis? Claims adjusters’ notes can be processed
by natural language algorithms and cross
referenced with photos of physical damage
or prior inspections.
What data would you analyze if it could be made available? What if you could listen to all the policyholder
calls received by your customer service
department and annotate which questions
stumped the customer service representatives?
Or which responses lead to irritation in the
policyholders’ voices?
Bringing AI to InsuranceWhat is an insurer to do? Start by not fretting.
We propose two considerations to facilitate
a sleep-at-night perspective. First, insurers
are already good at AI or its precursor
technologies. The applicability of AI in the
present and near future is entirely based on
narrow AI technologies. For example, natural
language processing and image recognition
are both machine learning implementations
with working business applications right now.
Both use predictive models to achieve results.
The software may be artificial neural networks
trained on vast data sets, but they are
nonetheless conceptually compatible with
things insurance carriers have used for years,
like actuarial pricing models. The point is that
the application of AI is an incremental step
forward in the types of models and data
already applied in the business.
Second, sorting through the hype requires a
staple of good business decision making: the
risk-cost-benefit analysis. Determining which
technologies are worth investment is within
scope for decision makers that otherwise
know how to make selective investments in
growing the capabilities of their firm. The
problems faced by a carrier are much bigger
than sorting out AI if management lacks the
basic skillset for making business investments.
Providing an inventory of every application
of AI is beyond the scope of this article.
DeepIndex provides a list of 405 at
deepindex.org, from playing the Atari 2600
to spotting forged artworks. Instead, suppose
that AI, like electricity, will be broadly
applicable across industries and functions,
including the components of the insurance
value chain from distribution to pricing and
underwriting to claims. The goal is to identify
and implement the AI empowered solutions
that will further a competitive advantage. Our
view is that carriers’ success with AI requires
three key ingredients: data, infrastructure,
and talent.
4
Demystifying Artificial Intelligence
Data: AI might be considered the key that
unlocks the door of big data. Many of the
modeling techniques that fall under the AI
umbrella are classification algorithms that are
data hungry. Unlocking the power of these
methods requires sufficient volume of training
data. Data takes several forms. First, there are
third party data sources that are considered
external to the insurance industry. Aerial imagery
(and the processing thereof) to determine
building characteristics or estimate post-
catastrophe claims potential are easy examples.
Same with the vast quantities of behavioral
data built on the interactions of users with
digital platforms like social media and web
search. Closer to home, insurance has long
been an industry of data and carriers are
presumed to have meaningful datasets in claims,
applications, and marketing, among others.
Infrastructure: Accessing the data to feed
the AI requires a working infrastructure. How
successfully can you ingest external data sources? How disparate and unstructured can
those sources be? Cloud computing is not
necessarily a prerequisite to successful AI,
but access to vast, scalable infrastructure is
enabling. Are your information systems
equipped, including security vetting, to do
modeling in the cloud? Can you extract your
internal data into forms that are ready to
be processed using advanced modeling
techniques? Or are you running siloed legacy
systems that prevent using your proprietary
data in novel ways?
Talent: Add data science to the list of AI
related buzzwords. We claimed earlier that
many of the advancements attributed to
narrow AI are predictive models conceptually
like modeling techniques already used in the
insurance industry. However, the fact that
your pricing actuary conceptually appreciates
an artificial neural net built for fraud
detection using behavioral data does not
mean you have the in-house expertise to
build such a model. Investments in recruiting,
training, and retaining the right talent will
provide two clear benefits. The first benefit is
being better equipped to do the risk-cost-
benefit analysis of which data and methods to
explore. The second is having the ability to
test and, ultimately, implement.
In Aon’s 2017 Global Insurance Market
Outlook we explored the idea of the third
wave of innovation as propounded by Steve
Case, founder of AOL, in his book, “The Third
Wave: An Entrepreneur’s Vision of the Future”.
The upshot of the third wave for insurers was
that partnership with technology innovators,
rather than disruption by them, would be the
norm. This approach applies now more than
ever as technological innovators continue to
unlock the potential of AI. If you don’t have
the data, or the infrastructure, or the talent to
bring the newest technologies to bear, you
can partner with someone that does. Artificial
intelligence is real. While the definitions are
somewhat vague - is it software, predictive
models, neural nets, or machine learning -
and the hype can be difficult to look past, the
impacts are already being felt in the form of
chatbots, image processing, and behavioral
prediction algorithms, among many others.
The carriers that can best take advantage of
the opportunities will be those that have a
pragmatic ability to evaluate tangible AI
solutions that are incremental to existing
parts of their value chain.
“If you don’t have an AI strategy, you are going to die in the world that’s coming.”7
Devin Wenig CEO, eBay
Maybe true, but that does not make it
daunting. The core of insurance is this: hire
the right people, give them the infrastructure
they need to evaluate risk better than the
competition, and curate the necessary data
to feed the classification models they build.
AI hasn’t, and won’t, change that.
The carriers that can best take advantage of the opportunities will be those that have a pragmatic ability to evaluate tangible AI solutions that are incremental to existing parts of their value chain.
What does it take to embed machine learning in my business?
Data Infrastructure Talent
5
Demystifying Artificial Intelligence
About the Author:Paul Eaton is a Managing Director at Aon in the analytics division of Reinsurance Solutions in Chicago. His current role focuses on capital management and strategic growth initiatives. Paul has a B.A. in Mathematics from Northwestern University and is a Fellow with the Casualty Actuarial Society.
1 https://www.technologyreview.com/s/609048/the-seven-deadly-sins-of-ai-predictions/
2 https://www.cnbc.com/2018/03/13/elon-musk-at-sxsw-a-i-is-more-dangerous-than-nuclear-weapons.html
3 https://www.technologyreview.com/s/610005/every-study-we-could-find-on-what-automation-will-do-to-jobs-in-one-chart/
4 https://www.sciencedaily.com/releases/2012/06/120622163056.htm
5 http://www.sciencemag.org/news/2015/11/pigeons-spot-cancer-well-human-experts
6 https://www.nytimes.com/2011/12/23/science/pigeons-can-learn-higher-math-as-well-as-monkeys-study-suggests.html?_r=0
7 https://www.linkedin.com/pulse/you-dont-have-ai-strategy-going-die-world-thats-coming-alex-cheal/
Demystifying Artificial Intelligence
About Aon Aon plc (NYSE:AON) is a leading global professional services firm providing a broad range of risk, retirement and health solutions. Our 50,000 colleagues in 120 countries empower results for clients by using proprietary data and analytics to deliver insights that reduce volatility and improve performance.
© Aon plc 2018. All rights reserved.The information contained herein and the statements expressed are of a general nature and are not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information and use sources we consider reliable, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.
www.aon.com
GDM06967