Digital society: Humans and machines living togetherDr. Jeffrey Bohn, Head, Swiss Re Institute

May, 2019

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Swiss Re Institute: Background and Partnerships

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University of Washington

Trends in a digitizing society

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Digital Society = People + Data + Networks + Machine IntelligenceWhat risks emerge?

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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

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Distributed ledger technology = decentralized transaction authentication

AI = cheap and abundant predictions

If data is the oil, Machine Intelligence is the refinery

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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)

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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

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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

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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

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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!

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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

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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.)

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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

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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

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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

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Debates just beginning with respect to the implications of a digitizing society

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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

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Legal notice

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