digital society: humans and machines living together8c691c35-ec84-4892...digital society: humans and...
TRANSCRIPT
Digital society: Humans and machines living togetherDr. Jeffrey Bohn, Head, Swiss Re Institute
May, 2019
2
Swiss Re InstituteWhy the Swiss Re Institute?
Support internal decision making processes
Commercial leverage through supporting clients and stakeholders
institute.swissre.com
Superior research driving better decisions
3
Global presence
Conferences
Client executive programmes
Economic and risk research reports
Economic research presentations
US election
US infrastructure
Country report
Group Advisors
SRI Symposia
sigma MonteCarlo launch
sigmas World insuranceEmerging markets
CEP Africa
SSAinsurance
Insurancemarket report
Willingnessto pay
India health
China Belt Road
China Diabetes
Yinchuan CEP
China wind
CEP CPICCEP NCL
Haze
SRI Symposia
4
Swiss Re InstituteCommercialising our output with stakeholders and clients
Centre for Global Dialogue
Global conference programme
Proprietary database creation
Sharing of data outputs
Executive education
Functional training
Start-up programmes
Ideation and prototyping
sigma
Risk Dialogue series
Conferences & speakers Data collection & curation Client training Client servicesPublications
5
Swiss Re Institute: Background and Partnerships
Strategic
Tactical
Opportunistic
University of Washington
Trends in a digitizing society
6
7
Digital Society = People + Data + Networks + Machine IntelligenceWhat risks emerge?
8
The Digital Society is the result of many technology trends
Semiconductors = cheap and abundant computation
The internet = cheap and abundant connectivity
Big data = cheap and abundant information
IoT & 5G = cheap, abundant, and connected sensors
------------------------------------------------------------------------------
Distributed ledger technology = decentralized transaction authentication
AI = cheap and abundant predictions
If data is the oil, Machine Intelligence is the refinery
9
Data is the oil for machine intelligence: Crude multiplies in value after refinement
Generate & collect Organise & curate Analyse & transform Deliver & multiply value
IoT 5G Connectivity Big Data Cloud computing Human + Artificial Intelligence Robotics Autonomous systems
Platform (e.g., Intelligent City)
10
Capacity opportunities and constraints
Reaching physical
limit
Increase may be more like15% per year
Bandwidth doubles every
21 months
Collecting, curating, and coordinating data constrains evidence-based R & D efforts
Access to data & data-driven insight still constrained by access to bandwidth & storage
Open-source algorithms are relatively less valuable than data access & insight
Moore’s law Kryder’s law Nielsen’s law
11
The future: Human Intelligence augmented by some kind of Machine Intelligence
Caution: How we define “artificial” and “intelligence” will influence research, development,and deployment of machine intelligence
YOU ARE HERE
Changing risk landscapes
12
13
Black swans, gray rhinos, and perfect storms…
General purpose technologies change the risk landscape
Extreme-downside, scenario categories are not created equal
Mitigation
Black swans: Unknowable given current information & virtually
impossible to predict
Gray rhinos: Highly probable & straightforwardly predictable given current information, but neglected
Perfect storms: Low probability & not straightforwardly predictable given
the outcome results from interaction of infrequent events, but can be identified via scenario analysis
14
Machine intelligence’s risk [See Possible minds: 25 ways of looking at AI, 2019 (Russell and Pearl)]
Value alignment and counterfactual imagination….
Machine’s objective specified exogenously
Machines may set removed variables to extremes
Most important problems tend to be multi-factorial
The risk…
Machines may pursue objectives damaging to humans
Machines may disclose data contrary to regulations or policy
Counterfactual imagination may be the key to mitigating machine intelligence’s risks
The King Midas problemHumans have imagination!
15
Algorithmic risk for critical enterprise-wide software systems
Algorithmic risk on the rise due to trends for software systems:
• Complexity
• Connectivity
• Ubiquity
• Interoperability (or lack thereof)
Trend exacerbated due to…
• Lack of software engineering standards & benchmarks
• Shortage of system architects
• Increased incidence of “algorithmic malpractice”
• Dependence on compiled components
Primary threats from algorithmic risk
• System operational fragility i.e., risk of failure
• System vulnerability i.e., risk of cyber attack
16
Cyber risk
Accidental breaches of security
Unauthorized & deliberate breach of computer security to access systems – both internal & external
Human error leading to vulnerabilities (e.g., IT operational issues, poor capacity planning, integration issues, system integrity, etc.)
17
Agile data analytics
Software talent (particularly in machine intelligence) is in short supply requiring higher throughput
Augmented intelligence & intelligent automation require more agile development processes
Less than 5% of data analyst work generates ROI
Data driven organizations are outcompeting rest
18
Changes arising from distributed ledger technology (DLT)
Internal improvements
Enhancement ofinsurance value-chain
End-customer driven initiatives
New value-chains
Business model disruption
Incremental efficiency gains
Organizing members of existing insurance value-chain into a
DLT cooperative for driving data standardization, aggregated
data access and shared processes
End-customers in non-insurance verticals organizing
DLT cooperatives for aggregation of shared
services including insurance
New players creating a DLT-based alternative value-chain or market for distribution of
re/insurance services
Enhancing internal business process efficiencies by
hosting shared services on internal DLT platforms
End-customer
Reinsurer
InsurerCapital
Broker
End-customer
End-customer
End-customer
Service
Insurance
Service
End-customer
Newplayer
Capital
Newplayer
19
Digitizing trends changing insurance
Forward-looking modeling of risk pools
Incorporating unstructured data into business and capital steering
Tracking natural catastrophe damage in real time
Assessing damage
Automated underwriting
Improving customer targeting
Parametric insurance contract implementation
Intelligent automation & robotic process automation (RPA) for underwriting and claims processing
Chatbots for customer support
Natural language processing applied to contract review
Final remarks
20
21
Debates just beginning with respect to the implications of a digitizing society
22
Quantum cities – A Swiss Re Institute initiativeRisk Intelligence to understand risks at scale and create innovative solutions
GovernmentsMunicipalities
EnterprisesCitizens
Knowledge Assets
Risk Pools & Products
Qu
an
tum
C
itie
s
Data Assets
5G DLT Internet of Things Robotics Machine Intelligence
Ship
Airplane Port
Airport City
Building
Truck Car
Moto Plants
Weather
NatCat events
SatellitePeople
Goods Food
Commodities
Payments Orders
SocialFx/Rates
Accumulation models
Behavioural models
Knowledge Graph
Climate ModelsRisk ModelsLossesLiabilitiesRegulations
AgroFoodEnergyCommercialInfra-
structureSupply chain
Life & Health
Property Climate NatCat
ResilientPeople
ResilientEconomy
ResilientEnvironment
What-if?Simulation
How?Prevention/Solutions
Where?Accumulation
When?Forecasting
What?Risk exposure
23
Legal notice
24
©2019 Swiss Re. All rights reserved. You are not permitted to create any modifications or derivative 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.