global insurance market opportunities demystifying...

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


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


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


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/


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.

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