the new age of insurance distribution
TRANSCRIPT
October 2016
1
The new age of insurance marketing
Rob Moffat
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
Balderton Capital: What we do
• Europe’s leading early stage tech investor
• Invest £1-20M into technology companies
• Look for potential for £1B outcomes
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
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
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
Insurance ‘roboadvisors’: Embroker, Policygenius
Insurance wallets: Knip, FinanceFox, Safe, Clark>600K
downloads of Knip alone
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
New mutuals: Lemonade (also Guevara, Friendsurance)
• …
Long tail, demand-driven: Bought By Many• … • Build social groups for niche
risks and push ‘group deals’ to them
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 claimsClimate Corp offered a similar model before its $1B acquisition by Monsanto
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
A VC’s view on the insurance marketMarketing
Sales and Service
Data(users, claims,
external)
Underwriting=>
Machine Learning
Capital and license
ClaimsAdministration
Opportunities for startups Marketing
Sales and Service
Data(users, claims,
external)
Underwriting=>
Machine Learning
Capital and license
ClaimsAdministration
OpportunityOpportunity
Disadvantage(unless new proprietary data)
Disadvantage
Table stakesTable stakes
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
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
What are the opportunities and threats?Marketing
Sales and Service
Data(users, claims,
external)
Underwriting=>
Machine Learning
Capital and license
ClaimsAdministration
Opportunity to open or revitalise segments if can be a good partner
Commoditised where data is plentifulReinsurers, hedge funds
Avoid drag factorBiggest advantage?
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)
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