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Tech and trends in the

Pascal Marmier, Which ones are opportunities … or challenges?

Source: Web site on AI influencers 2 Period of Rapid Change Accelerated by

New Modes of Consumer experience & Sharing, Gig Economy New Industry structures Consumption

• Cyber (commercial & personal) Emergence of New • Autonomous mobility & transportation (cars, trucks, planes) New pools • with new materials (3D )

Issues of trust and risk for Technology Companies Impact on value chain society, economy (automation)

• Artificial Intelligence Geopolitical Issues • Privacy • IP

3 Tech Transformation

4 Tech transformation: the drivers

• Mobile • AI / RPA • of • Chatbots things • Personalization • Research data

Explosion Human in data (Big Machine data) Data sharing

Data Data analytics • Numerous use • cases in industry • Emerging • Systems / Apps marketplaces

5 Tech Impact on the Transformation of value chain Thinking

Sensors IIoT

Data Big Data

Machine Information Learning

Decision AI

Action Automation

Tech as Leaders support 6 Technology is affecting the insurance value chain

PHYSICAL Pricing/ /claims Distribution VALUE CHAIN /development

• Robotics/ • Use of Big • insurance as • Customers prefer multi- • Use of Big Data to Telematics/ Data/analytics to identify more customer-centric touch, omni-channel reduce fraud and new claims drivers interaction improve claims Internet-of-things • Increase frequency of processes (IoT)/wearables offer • Predictive/ interaction • Smart devices usage-based Prescriptive • Self- apps to • Use Big Data /analytics • Less face-to-face insurance underwriting techniques improve customer post- for micro engagement experience opportunities • Artificial intelligence (AI) segmentation and • Scope for gains in to hone personalization • Blockchain applications • Emerging risks such efficiency in offline for smart and as cyber channels claims administration DIGITALISATION • Social-network • AI-driven Robo- advisors insurance groups

VIRTUAL INFORMATION CAPTURE AND ANALYSIS VALUE CHAIN

Source: Swiss Re Institute. 7 Swiss Re Tech transformation strategy – more than a vertical

Increase our Improve our Leverage clients value chain potential of data competitiveness

8 Machine Intelligence

9 Machine Intelligence @Swiss Re

Machine Intelligence refers to the interplay between Artificial Intelligence, and Cognitive Computing Support/predict underwriting decisions by combining banking and insurance data

. Additional reliable and Banking Data high volume data Predictive Model source for UW withdrawals . posses highly Clients standardized . Model predicts standard/ transactional data substandard card payments . Transactions could serve classification based on as proxy of lifestyle Machine banking data behavior of individuals . Underwriting data banking transfers Learning enriched with lifestyle predictors extracted from . Contain , income, banking data Underwriting Data smoker, age, etc . Shorten list of Q&A during . Contain historical UW UW process Anonymized data decisions on standard . Indicate whether bank vs. substandard L & H client should be pre- risk classification approved or a more Type of health benefit . Medical examination data detailed UW process is must be ignored from required Standard / sub-standard modelling

11 Machine Intelligence offers applications in various fields

Insurance Implications Voice • Cross Selling and Churn • Fraud detection in claims • Virtual Assistants • Claims prevention • Digital Health: voice as marker • Risk assessment and pricing

Vision Text () • Autonomous • Facial Analytics • Insights from contracts • Images to immediately provide • Patterns in news (trends) coverage – home or car

12 THE CHALLENGE Digital & Smart Analytics Case Study: Motor Insurance Portfolio Optimization

Where is your portfolio performing well or poorly within a state of ? How can you quickly and efficiently highlight loss performance by postal code and additionally forecast future performance in those areas?

Big Data Methods & Machine Learning

• Multi-year client claims and policy data • Hosted on Swiss Re infrastructure Automatic scoring of all incoming claims based on • Aggregates and forecasts accident identified cost drivers frequency, severity, loss ratio, and Benefits policy volume by postal code Predict future expected procedures costs given disease Semi-autonomous tool and comorbidity development paths

Predictive and visual analytics Manage highest risk-scored claims to effectively contain costs Portfolio reporting capability Client Success Story: Analysed >130 zip code areas for client portfolio assessment 13 Blockchain

14 Blockchain in a shell

Distributed Ledger Cryptography Smart Contracts

keeping score of who + unique combination of + decentralise and automate owes what to whom technology, consensus authorization and finding and authentication

Cedent A is reinsured B and C should not see Legal () logic is put with Reinsurers B and C certain things of each other into computational logic OLD WORLD each hold and repeat privacy, security and labour intensive and data in own ledger regulatory concerns manual processes

shared, immutable data public/private keys and “self executing” NEW WORLD on a distributed ledger consensus algorithms contracts

15 Industry verticals blockchain consortia potentially redefining future risk transfer landscape

ORIGINAL INSURED (RE)-INSURANCE MARKET MARKETS

Speciality risk consortium (e.g. Marine) JV for digitizing global supply chain

Port authorities Blockchain Insurance Industry Initiative Freight B3i Forwarder

Retro- Capital Insured Insurer Re-insurer cessionaire Markets

Blockchain-based: . Data autonomy . Trade-secret protection . Distributed clearance . Network-driven . Transparent end-to-end compliance

16 B3i Value Proposition – Unlocking improvement potential across insurance industry

Simplified Industry Value Chain

Retro- Capital Insured Insurer Reinsurer Markets

The Insurer - Reinsurer Interaction Policy / Claims underwriting Premium Handling Settlement Management

• Contract certainty • Manual processes • Data duplication • Settlement and • Counterparty data • Pairing and inefficiency reconciliation and risk • Latency • Fraud risk latency

Typical Challenges

17 Swiss Re expects a range of benefits

Working Capital Operational Efficiency Quality and Integrity of Foreign Exchange Improvement and Risk Reduction Data Management

Faster and more Reduction of contract Normalised and high- Accelerated FX efficient premium and , quality data in a shared transactions and claims settlement and reconciliations and source with central consistent valuation optimised liquidity process inefficiencies control over integrity and management easier auditing

Positive Impacts: • Combined Ratio • Improved liquidity • Risk Reduction

18 All this is Powered by Big Data

Better People Opportunity Transactional Decision Leave Data to Efficiencies All the Time Personalize Making

Follow Along To See How Engagement is an Insurance Opportunity

19 Ecosystems and consumer engagement – generating value out of tech

20 Platforms and ecosystems offer new business models and integrated customer journeys

Content providers Content

Energy & Utilities Dashboard Connectivity Services

Partnerships

Devices 21 Platforms and ecosystems offer new business models and integrated customer journeys

Awareness

Considera- Advocacy tion

Retention Purchase

22 Ecosystems and insurance

• Customer is at the center of any ecosystem Consumer • as tool to serve most of the customer needs engagement

• Data is shared across service providers without needs for additional from the customer • Network effects: value of services or data serve several players or customers in the ecosystem Features

• are essential to create and derive value. • Role can evolve from stakeholder to orchestrator of an ecosystem. Role of • Use but also share analytics and risk assessment with other in the ecosystem. Think of offering “data as a insurer service”

23 Ecosystems are expected to emerge in place of traditional industries by 2025 IoT Prevention Subscription UW service Wearables Data sharing with providers

Holistic protection approach

https://www.mckinsey.com/industries/financial-services/our-insights/insurance-beyond-digital-the-rise-of-ecosystems- and-platforms Ecosystems arise when services can be digitalized and there is a large amount of data

25 For Example: Life & Health risk exposure of corporate and individual risk portfolios could be regularly reassessed based on data insights

Breakfast Park & Ride Park & Ride Start Work Active End Work Outdoor Outdoor Shopping Mobility Time with Sleep with family (mobility) (mobility) 08.15h Break 17:30h Activity Activity 19.00h Service Family 23:15h 07.00h 07.30h 08.00h 10:15h 17:45h 18:45h 19:15h 19:45h

Health risk related Interact. Digital Health Risk Related Interventions

Healthy Stage Great achie- Don’t eat fruits Today 7.5h Don’t forget vement today before at 21h sleep nece- High stress level your vitamins ssary – listen to Pre-Illnes Stage Reminder Take a walk (12k steps) – Relax – – here is why  (blood pressure)! D today – here some music in Take outside for 7k missing for take the train - prepare for – relax / watch is why  advance – here Medication 35min next badge! eat an orange! sleep at 23h! Chronic Stage developments! is why 

Impact on Activity Stress Generated Adherence Gratification Adherence future risk motivation – prevention and Customer Sleep control Insights and health risk for completed and self health exposure – health emotional and and health monitoring & activities (bon- risk control – Check for adherence control health literacy literacy control ding services) cust. education intervention! monitoring feedback Notification Prevention Suggestion Notification Casualty Notification Sit. Awarness26 Example of consumer engagement and link to health (simplified UW)

27 How to Start Innovation Journey

28 Different ways to organize your tech strategy

Insurers' technology strategies

Venture Invest in InsurTech start-ups with a proven , product, customers, first revenues

Start-up Insurers run pilots with start-ups, act as capacity partnerships providers, offer claims management expertise

Nurture in-house teams, and less mature start- Innovation labs ups. Turn ideas into business applications. and accelerators Insurers support with initial and networks

Consulting for business model innovation. Multi Contracts with million dollar projects to tap expertise in business large tech vendors processes, tech and integration

Source: Swiss Re Institute, Expertise paper, Technology and insurance: themes and challenges, June 2017 29 It’s not about the tech, it’s about interacting and working with people

• Talents – Lots of new talents because the industry is attractive – Data scientist team at Swiss Re – New skills needed: underwriters need to access new sources of data • Approach to innovation – Try, experiment -> need for speed because it’s impossible to anticipate far ahead in the future – Lean methodology / Hackathon / Prototyping • Look for partners – collaboration is key – Internal collaboration across functions is often the first step. Create dedicated teams – Accelerators are becoming trusted partners to create winning corporate – startup collaborations • Different way to approach projects – KPIs are different: speed of implementation, ROI

30 Resources

VC number https://techcrunch.com/2017/07/22/vcs-love-insurance-even-if-you-dont/ Startups changing insurance https://techcrunch.com/2017/07/29/startups-want-to-change-what-you-insure-and-how-you-insure-it/ McKinsey view on digital insurance http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/digital-blog/what-the-new- world-of-insurance-could-look-like https://www.mckinsey.com/industries/financial-services/our-insights/insurance-beyond-digital-the-rise-of- ecosystems-and-platforms

31 Legal notice

©2017 Swiss Re. All rights reserved. You are not permitted to create any modifications or works of this presentation or to use it for commercial or other public purposes without the prior written permission of Swiss Re.

The information and opinions contained in the presentation are provided as at the date of the presentation and are subject to change without notice. Although the information used was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the details given. All liability for the accuracy and completeness thereof or for any damage or loss resulting from the use of the information contained in this presentation is expressly excluded. Under no circumstances shall Swiss Re or its Group companies be liable for any financial or consequential loss relating to this presentation.

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