A brief overview of current startup trends in insurance distribution. A talk given to the Insurance Institute of London at Lloyd's 18 October 2016.

1. October 2016
1
The new age of insurance marketing
Rob Moffat

2. Agenda
• Introduction
• Emerging models of insurance marketing & distribution
• An investor’s perspective on where the potential lies
• How this could help or challenge market incumbents

3. Balderton Capital: What we do
• Europe’s leading early stage tech investor
• Invest £1-20M into technology companies
• Look for potential for £1B outcomes

4. A selection of our portfolio
SOLD $1.0B IPO/EXIT $2.2B SOLD $0.9B
SOLD $0.9B IPO/EXIT $2.0B SOLD $0.6B

5. Agenda
• Introduction
• Emerging models of insurance marketing & distribution
• An investor’s perspective on where the potential lies
• How this could help or challenge market incumbents

6. Micro-insurance at point of sale: Zhong An
• Founded 2013
• 4B policies sold
• 200 insurance
products sold
• 400M customers
served
• Investors: Ping An,
Alibaba, Tencent
Shipping insurance for returns embedded into Taobao, Tmall
See also for example Simplesurance, Asurion, Cover Genius

7. Insurance ‘roboadvisors’: Embroker, Policygenius

8. Insurance wallets: Knip, FinanceFox, Safe, Clark
>600K
downloads of
Knip alone

9. Snapchat generation: Trov (see also Cuvva, usecover)
• $46M of funding to date
• Partner with AXA, Suncorp, Munich Re
• UK launch imminent
Aiming to change
insurance from a
grudge purchase to
an emotional one

10. New mutuals: Lemonade (also Guevara, Friendsurance)
• …

11. Long tail, demand-driven: Bought By Many
• … • Build social groups for niche risks
and push ‘group deals’ to them

12. Automation to serve smaller clients: Meteo Protect
Allows small businesses to purchase weather insurance, making it cost-
efficient through online sales, integrated systems and automated claims
Climate Corp offered a similar model before its $1B acquisition by Monsanto

13. Agenda
• Introduction
• Emerging models of insurance marketing & distribution
• An investor’s perspective on where the potential lies
• How this could help or challenge market incumbents

14. A VC’s view on the insurance market
Marketing
Sales and Service
Data
(users, claims,
external)
Underwriting
=> Machine
Learning
Capital and license
Claims
Administration

15. Opportunities for startups
Marketing
Sales and Service
Data
(users, claims,
external)
Underwriting
=> Machine
Learning
Capital and license
Claims
Administration
Opportunity
Opportunity
Disadvantage
(unless new proprietary data)
Disadvantage
Table stakes
Table stakes

16. Which are the biggest opportunities in distribution?
Micro-insurance at point of sale Proven model, maturing market
Insurance ‘roboadvisor’
Will take time to crack customer acquisition,
but many IFAs are replaceable with AI
Insurance wallets
Could have huge impact. Competition fierce,
can they continue to take trail commission?
Snapchat generation
Exciting but unproven. Pricing challenge?
Acquisition challenge?
New mutuals
Do people get concept? Will fraud/ claims
reduction be significant?
Long tail, demand-driven
Technology unlocks new market. Challenge is
lack of historical data, will insurers take risk?
Automation to serve small clients
Interesting model but limited to niches where
parametric approach can replace loss-based

17. Agenda
• Introduction
• Emerging models of insurance marketing & distribution
• An investor’s perspective on where the potential lies
• How this could help or challenge market incumbents

18. What are the opportunities and threats?
Marketing
Sales and Service
Data
(users, claims,
external)
Underwriting
=> Machine
Learning
Capital and license
Claims
Administration
Opportunity to open or
revitalise segments if
can be a good partner
Commoditised where
data is plentiful
Reinsurers, hedge funds
Avoid drag factor
Biggest advantage?

19. What are the opportunities and threats?
• The VC funding into fintech is now moving to ‘insuretech’
• Majority of the money will be for new distribution models and
MGA models (hard to see a $B exit in B2B sales to insurers,
license and balance sheet are off-putting to build from scratch)
• Tech entrepreneurs have 20 years of practice at innovating front
end, they will move fast and find new opportunities
• These startups will sometimes open up new segments, but
more often will take the place of existing brokers/channels
• Startups will look to partner, but if they cannot find a good
partner they will go it alone or backward integrate over time
• Reinsurers are making a real effort here (e.g Munich Re)

20. Startups’ wish list from insurance partners
• Allow flexibility on policy terms, wording and price
• Acceptance that this may result in small losses in early years
• Clear path from ‘innovation division’ into production
• Minimise ‘integration headwind’: do you have good APIs?
• Need help on claims and fraud, not always willing to admit it
• Open to equity participation, but not exclusivity
• Profit share models not introducer fees