How are machine learning and artificial intelligence revolutionizing insurance? This presentation explains it briefly, including current trends and effects on the business.

1. The Data Science Revolution in Insurance
Author: Stefano Perfetti, linkedin.ch/in/sperfetti, perfetti.stefano@gmail.com
2018 graduate of the Master of Advanced Studies in Management, Technology and Economics
at the Swiss Federal Institute of Technology / ETH Zurich and MSc in Software Engineering.
Source: Stefano Perfetti’s own ETH thesis, fully available here, including data.

2. CONTENTS
1. What is data science, in general?
2. How is data science changing insurance?
3. Current trends in insurance data science
 To jump on to a section,
please click on its title.

3. To “cook” data science, mix these 3 ingredients:
The data science Venn diagram.
Drew Conway (2010).

4. To “cook” data science, mix these 3 ingredients:
The data science Venn diagram.
Drew Conway (2010).
= “speak” hacker &
think in algorithms
to manipulate data. This ingredient
is needed in
big quantities!
It also takes knowledge
of the specific domain
that originates the data.

5. Conceptual map of data science
Conceptual map of data science. Mayo, M. (2016, March). Data science puzzle explained/2.
Retrieved from http://www.kdnuggets.com/2016/03/data-science-puzzle-explained.html/2 on 2017-09-24.
Because the field is so new,
concepts often have blurred
and overlapping definitions.

6. machine learning
big data
artificial intelligence
data science
data mining
Search interest over the 10 Y up to Jan-2018. From Google trends.
The popularity
of various terms
changes with time…
…and moreover, different people
express themselves in different ways.
“Machine learning”
is now trending,
but only few people
use the job title
“machine learners”.
Managers often talk
about “big data”,
which practitioners
often find vague
and inaccurate.

7. machine learning
big data
artificial intelligence
data science
data mining
Search interest over the 10 Y up to Jan-2018. From Google trends.
The popularity
of various terms
changes with time…
Most practitioners
prefer
“data science”
and the job title
“data scientist”.
As of February 2018,
LinkedIn lists more than
60’000 “data scientists”
in total, worldwide,
across all industries:
i.e. almost 9 times more
than just 4 years earlier.
…and moreover, different people
express themselves in different ways.

8. What can AI do for us today?
Prof. Andrew Ng
Top AI expert
Professor at Stanford University
Co-Chairman & Co-Founder of Coursera
Former VP & Chief Scientist of Baidu
Former Head of Google Brain
If a typical person can do a
mental task with less than one
second of thought, we can
probably automate it using AI
either now or in the near future.*
* November 2016 article
by Prof. Andrew Ng
in the Harvard Business Review
https://hbr.org/2016/11/what-
This is actually a lot,
if you consider
how many activities
are sequences of
1-second tasks!

9. What can AI do for us today?
Prof. Andrew Ng
Top AI expert
Professor at Stanford University
Co-Chairman & Co-Founder of Coursera
Former VP & Chief Scientist of Baidu
Former Head of Google Brain
If a typical person can do a
mental task with less than one
second of thought, we can
probably automate it using AI
either now or in the near future.*
* November 2016 article
by Prof. Andrew Ng
in the Harvard Business Review
https://hbr.org/2016/11/what-
AI was science fiction,
now it is our daily life.
AI is already part of:
- Siri and her “sisters”
- music and film
recommendation
services
- video games
- smart cars
- smart home devices
- customer support bots
- automated translators
and summarizers
- fraud detection
systems protecting
your bank account…
- …
The list is long and
keeps growing, fast.

10. 1996: Chess
AI has been mastering
increasingly complex
human abilities,
as seen with games.
The machines’ progress is fast. And accelerating.
Yes, now
machines
can outbluff
us, too!

11. The step from technology breakthroughs
to practical applications
is fast, too.
Around 2014-2015, machines caught up with us in the task of face recognition
thanks to deep learning, a new disruptive machine learning technology.

12. The step from technology breakthroughs
to practical applications
is fast, too.
Around 2014-2015, machines caught up with us in the task of face recognition
thanks to deep learning, a new disruptive machine learning technology.
Just a few
years later, this
technology already
has applications
everywhere. It is
used in your
facebook profile…
…in security
scanners in
public places…
…and since the
iPhone 8, it lets
you unlock
your phone by
looking at it.
This is
just one
of many
examples.

13. This presentation cannot fully explain
data science / AI / machine learning:
This was just the gist of it.
Now let’s consider how this all
affects the insurance industry.

14. Data science in the insurance industry
Traditional insurance
actuaries also operate
in this intersection.
Insurers already
embraced statistics
centuries ago.
The data science Venn diagram.
Drew Conway (2010).

15. Data science in the insurance industry
The development
of computer science
has created many
new useful tools.
The data science Venn diagram.
Drew Conway (2010).
Therefore, now
insurers also hire
data scientists.

16. How does insurance work,
since always?
premiums for
risk protection
Insurance is a contract
where clients give insurers
a small fixed premium
in exchange for protection
against unwanted risks.
Insurance is often
a grudge purchase,
made only out of need.
Usually, the risk protection
is given as a payout or
other compensation
after a bad event strikes.
The insurer
invests the
premiums,
while waiting
for possible
payouts.

17. premiums for
risk protection
Customer
acquisition
Risk
assessment
Retention
& upselling
Claims
processing
Offer
creation
The underwriting cycle is at the heart of insurance,
since always
If there are
insured
damages,
activate the
protection:

18. premiums for
risk protection
Customer
acquisition
Risk
assessment
Fraud
avoidance
Retention
& upselling
Claims
processing
Offer
creation
design
possible
offers
check
The underwriting cycle is at the heart of insurance,
since always
Blocking scams
is a win-win for
insurers and for
honest customers!
Product
development
2 other important
business functions

19. Data science in insurance…
premiums for
risk protection
Customer
acquisition
Risk
assessment
Fraud
avoidance
Retention
& upselling
Claims
processing
Product
development
Offer
creation
2. adds
granularity
3. adds new
capabilities
Active risk
management
= prevent damages
1. increases
effectiveness
and efficiency
in all functions
Win
win!

20. Data science in insurance
premiums for
risk protection
Customer
acquisition
Risk
assessment
Fraud
avoidance
Retention
& upselling
Claims
processing
Product
development
Offer
creation
Active risk
management
= prevent damages
+ PROFITABILITY
+ SOCIAL BENEFITS
For a deep and detailed examination of all use cases o data science in insurance, see:
Boobier, T. (2016). Analytics for insurance: The real business of Big Data. John Wiley & Sons.

21. What do insurance data scientists have to say?
 For my ETH thesis (here), I surveyed as many as I could in Aug / Sep 2017.
 300 insurance data scientists from 40 countries took part in this survey.
 What follows is a summary of trends emerging from the survey.

22. Data
science
in
insurers’
business
functions
The survey
confirms that
data science
is fast
becoming
pervasive in
insurance,
across all
business
functions.

23. Newer, more complex
data science methods
are used more rarely, but
laggards are catching up
more or less uniformly
across the board.
Data science methods
are publicly available in
academic research, so
they alone cannot lead to
a sustainable competitive
advantage.

24. Data types
used
by
insurers
On the
contrary,
it appears
that laggards
cannot
catch up
so fast with the
best-in-class
in the ability
to use data
of more
sophisticated
types.

25. Diffusion of data formats in insurance data science / 1
The diffusion of
technologies to process
various data formats
appears to match
the typical
innovation diffusion
pattern.

26. current market share
[% of insurers using it]
current growth rate
[% of insurers
about to adopt it]
numeric
&
structured
datatext
images
speechvideo
Image & speech processing
appear to be about to boom
in the insurance sector!
Diffusion of data formats in insurance data science / 2

27. Where do insurers go for “help” on data science?
Consultants can help
especially with setting up
or (re)organizing
data science staff
and processes.

28. Where do insurers go for “help” on data science?
Data science APIs such as:
- IBM Watson
- Google Cloud Machine Learning Engine
- Amazon Machine Learning API
- Microsoft Azure Machine Learning
- BigML
- …
provide sophisticated capabilities.

29. LEGEND:
underlined:
p-value < 0.05
Color scale:
full green = +1,
full white = 0,
full red = -1.
What can we learn from answer correlations?

30. What can we learn from answer correlations?
Unsurprisingly,
different measures of
strength in data science are
strongly linked together.

31. What can we learn from answer correlations?
But interestingly,
they are also correlated
to an insurer’s propensity to
external linkages, i.e.:
partnering with consultants,
universities or other insurers,
plus using data science APIs.
External linkages are
a plausible cause of superior
data science capabilities.

32. When are insurance data science projects successful?
The top 2 success factors
are human factors that are
common to all projects.

33. When are insurance data science projects successful?
But the data scientists are also key:
they should have both technical
and insurance business know-how.

34. Therefore,
experienced insurance data scientists
are key employees for insurers.
So, what should insurers know about their careers?

35. Education of insurance data scientists
Typically, they are highly educated,
but not specifically in data science (yet).
Data science programs have
just started producing graduates,
so keep an eye on this share!

36. What keeps insurance data scientists busy?
Communication and
coordination do take up
time, as in most jobs…

37. What keeps insurance data scientists busy?
...but core data science
tasks are at the top.
Therefore, insurance
is a good industry for
practicing data science.

38. Finally, how do insurance data scientists’
career trajectories look like?
They are often recruited
from other roles and,
even more often,
from other industries.
This suggests there is
a dearth of already
experienced insurance
data scientists.

39. Finally, how do insurance data scientists’
career trajectories look like?
Once in their role,
they want to remain in
data science…
…but troublingly for
insurers, not necessarily
in the insurance sector.

40. AXA
9%
Liberty Mutual Insurance
5%
Aetna 5%
The Hartford 4%
Zurich 4%
AIG 3%
Allianz 3%
Generali 2%
Humana 2%
BlueCross and BlueShield 2%
MetLife
2%
Aviva
2%
Nationwide Insurance
1%
State of the data science arms race
among insurers
High concentration:
Just 14 big insurers
employ 51% of them.
Will data science
trigger consolidation?
Estimates based
on LinkedIn profiles
as of August 2017
49% of insurance data scientists
are spread out among 206 insurers,
with 136 of them employing only one.

41.  Data science is changing insurance deeply.
 As a result, the industry is in a state of flux,
where big changes are possible.
 External linkages, such as collaboration with consultants,
academia or other insurers, as well as using data science APIs,
appear to help insurers increase their data science capabilities.
 Data scientists in insurance are not committed to the industry, despite
being key employees and knowing the importance of domain knowledge.
Therefore, insurance company managers should:
 make sure their companies adapt well to ongoing changes;
 use external linkages as a tool to enhance data science capabilities;
 strive to retain their experienced data scientists, as key employees.

42. For questions, or for job offers,
please feel free to contact me.
I am passionate about data science, innovation, insurance and financial services.
As of March 2018, I am actively looking for a job in Zurich (CH).
Stefano Perfetti / linkedin.ch/in/sperfetti / perfetti.stefano@gmail.com

43. For my thesis, which is the source of this presentation, my heartfelt thanks go to:
My thesis supervisors Cristina Kadar and José R. Iria,
for giving me the chance to work with them, exposing me to brilliant ideas and patiently giving me feedback;
Tony Boobier, international book author, Raphael Troitzsch of Swiss Re, and Alexander Cherry of Insurance Nexus,
for helping me – among other things – in the crucial task of survey distribution;
Gundula Heinatz, Matyas Filep, Teresa Kubacka, Markus Odermatt, and Georg Russ of Die Mobiliar,
for collectively supporting me with a lot of their time, explanations and advice;
Daniel Berger and Dennis Meier, of BearingPoint,
for also supporting me with their time, explanations and advice;
Other people who gave me help, advice or encouragement, in alphabetical order:
Edward Di Cristofaro, Kornelia Papp, Helen Raff, Yash Shrestha, Julie Simou, David Taylor, Zhiwei Jerry Wang;
To those that I am surely forgetting right now: apologies, and thanks!
To all those who took the time and effort to respond in my survey: I do appreciate.
All the best,
Stefano
Acknowledgements