Mastering business risks through Advanced Analytics

Swiss Re Institute and Swiss Re Corporate Solutions welcome you!

15 October 201910:00 CET

• Webinar host: Marc Covarrubias, Head Strategic Client Management EMEA,

Swiss Re Corporate Solutions

• Advanced analytics in the insurance & InsurTech industry

Daniel Ryan, Head Insurance Risk Research, Swiss Re Institute

• Business impact through six core data science capabilities

Monica Epple, Head of Digital & Smart Analytics, Swiss Re

• Advanced Analytics client use cases

Thomas Keist, Director, Innovative Risk Solutions EMEA, Swiss Re Corporate Solutions

• Panel discussion and Q&A

Agenda

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sigma 4/2019Advanced analytics in the insurance & InsurTechindustry

Daniel RyanHead Insurance Risk ResearchSwiss Re Institute

• Internationally recognized expert on longevity and health data

• Speaking and writing on disease-based mortality models and the impact of innovation on insurance

• leads Insurance Risk Research group at the Swiss Re Institute

• Key research include:

– digital ecosystems and opportunities for insurance

– integration of experts insights and machine-learning into horizon scanning

– reduction of insurance protection gaps through behavioural economics

• Holds a Professional Doctorate in Health, University of Bath, an MBA from the Edinburgh Business School, and a Master Degree in Medical Sciences from the University of Cambridge

Daniel Ryan, Swiss Re Institute

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Data analytics first started to gain a foothold in the P&C industry in the mid-to-late 1990s

1990s

•Credit scoring – an early bell weather of the disruptive power of data in insurance

2000s

•Predictive modeling transformation of the P&C industry

•Analytics powered underwriting, claims triage and marketing

Today and the future

•Broad based analytics and big data

•Granular applications in operations and customer service

•A core strategic capability

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Evolution of data analytics in the P&C industry

Source: A.M. Best, Deloitte Predictive Analytics Symposium, Gartner, Swiss Re Institute

Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to

discover deeper insights, make predictions, or generate recommendations. (Source: Gartner)

Technology advances are fuelling an explosion in data, generated inexpensively and non-intrusively

Source: International Data Corporation (IDC), Swiss Re Institute

0

40

80

120

160

200

2010 2013 2016 2019 2022 2025

Forecast of data growth

Real time data (zettabytes) Non real time (zettabytes)

CAGR (2019-25)Real time data: 39%Non real time data: 24%

• By 2025, worldwide data will be 175 zetta-bytes, 1/3rd will be real-time.

• But ability to gain useful predictive insights is challenging.

• Insurers are under-invested in data curation.

• A clear role for data specialists and insurance experts to bridge the gaps.

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Insurers are planning to spend more on analytics, as they complete core system upgrades

0%

25%

50%

Core applicationand infrastructure

Digital Data and analytics Security

Estimates of technology spending at P&C insurers, 2019

Run Grow Transform

Source: Novarica, Swiss Re Institute

• Realignment within budgets, as insurers complete core systems upgrades and focus more on data and analytics.

• Analytics account for ~15% of P&C insurers’ IT spending.

• Personal lines more advanced but commercial lines are catching up.

• Achieved ROI is often lower than expected, because of poor operational integration.

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Note: Run expenses do not directly increase revenue or achieve new company goals, but they do maintain essential functions.Grow supports business growth (typically organic growth or improvements in business processes). Transform enables entering new markets, new customer segments, creating new value propositions and enacting new business models.

Analytics can support important business needs in four areas of the insurance value chain

8Source: Compiled from publications and interviews of insurers, brokers, and tech vendors. These are not Swiss Re case studies

Understand new risk pools

Design appropriate go-to-market strategies.

Enabling growthEngaging customers

Tailored recommendations

Behavioural science to understand consumers

Optimising portfolios

Investigate trends in underlying loss drivers

Untapped profit pockets

Anticipate market dynamics

Improving efficiency

Automate pricing, u/w and claims

Review policy wordings

Propensity to bind using submissions

Use cases

ROI horizon 3-5 years < 1 year < 1 year 3-5 years

2-5% lower loss ratio targeted under real trading conditions

• >50k man-hours saved• - 50% physical insp. cost• 60% savings in U/w exp.

• - 80% cost per click • - 40% acquisition costs • + 8% conversion rate

Longer term benefits

Examples

Use cases of analytics in the value chain of leading sectors

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Retail

Customer experience improvement

Targeted marketing

Supply chain analytics

Warehouse and inventory management

Healthcare

Operational efficiencies through automation.

Medical supply chain analytics

Pre-emptive identification of patient risk

Expanding medical insight through deep learning

Reducing cost of care

Telecommunications

Customer service and retention

Telephonic fraud prevention

Network optimisation and predictive maintenance

Operational efficiencies through automation.

Targeted marketing

Use cases

USD 10 million of savings through analytics driven hospital decision support system.

USD 7 million savings by using voice recognition to prevent telephonic frauds.

23% reduction in customer churn by analytics driven customer segmentation.

Examples

Source: Swiss Re Institute compilation of specimen pilots reported by industry players, consulting firms and technology vendors

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Sector Country Benefit

Telecom EMEA 35% increase in customer satisfaction score by applying social media analytics to solve customer issues.

Telecom EMEA 14% saving in energy consumption by machine learning driven network traffic optimisation and automation.

Telecom AMER USD 7mn savings by using voice recognition and analysis to separate genuine and fraudulent phone calls.

Healthcare APAC 75% of time savings by using RPA to automate high volume government healthcare claims processes.

Healthcare AMER USD 10mn of savings through AI and predictive analytics to improve hospital decision support system.

Healthcare AFR Preventing the counterfeit medicine risk by using GPS data analytics to identify unregulated pharmacies.

Retail EMEA50% rise in quarterly sales by analytics driven identification of top-selling products and planning the store layout accordingly.

Retail AMER 23% reduction in customer churn rate by analytics driven customer segmentation and targeted marketing.

Retail EMEA 11% increase in sales by refining targeted marketing through online customer behaviour analytics.

Source: Swiss Re Institute compilation of specimen pilots reported by industry players, consulting firms and technology vendo rs

Examples of analytics application in leading sectors

Imp

act

on

th

e c

om

pa

ny

Estimated time for deploymentShort term Long termLo

wH

igh

1. Rapid deployment (up to 1 year)

2. Wider business integration (1 to 3 years)

3. Long term gains (3 to 5 years)

Deploy at small scale as pilots

Roll out successful projects across business

Entire business transformation and scalable model

While some projects can generate near term ROI, executives suggest that larger scale projects can take as much as three to five years to show ROI

11Source: Ideou, Stanford d.school, Swiss Re Institute

Feasibility(technical capability)

Viability(economic

case)

Desirability(The business

wants it)

Successful projects

Require change management

Many analytics use cases

Projects that can’t be fully implemented

Time horizon for benefits to showAnalytics project assessment framework

Key messages

• Past successes that focused on improving expense ratios have catalysed new investment with pilots showing meaningful improvement along the value chain, as corporates gain better visibility into underlying drivers.

• Several weeks is the minimum time for rapid deployments. Wider business integration can take longer. Most insurers aim for a success rate of around one-third in operationalising pilots.

• The outlook is promising as analysts expect spending on data and analytics across all industries to rise. However, patience is crucial due to the inherent complexity across the value chain.

• Challenges exist, such as working with legacy systems, traditional mindsets and a lack of specialist talent at the cross-over of data science, risk knowledge and digital technology.

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Business impact through six core data science capabilities

Monica EppleHead of Digital & Smart AnalyticsSwiss Re

• Responsible for the team of data scientists focused on modelling and prototyping

• Substantial track record in International Management Consulting Industry with focus on Digitization, IoT/Telematics and Big Data Strategies for Primary Insurers and Reinsurers

• Expert in portfolio and claims analytics

• More than 10 years experience as line Manager in Claims Department and Corporate Underwriting Business at a German primary insurer

• Holds Master’s degree in Insurance from the University of Leipzig and each a diploma in finance (FH) and business information systems (VWA)

Monice Epple, Swiss Re

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Business impact through six core data science capabilities

Monica Epple, Head of Digital & Smart Analytics

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Zurich, October 2019

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Our six core data science capabilities enable us to deliver tangible business impact

Big Data Methods

We can process and clean large amounts of heterogeneous data to build a strong foundation for the building of additional models

We leverage text mining and natural language processing algorithms to extract insights from unstructured data sources

We possess machine learning capabilities to facilitate human-machine interaction and to support business decision making

We use advanced statistics and machine learning to predict future outcomes and to effectively support business decision making

Text analytics

Machine Learning

Predictive Modelling

Visual AnalyticsWe employ advanced visualization methods to enable users to comprehend and explore structured and unstructured data

Rapid Prototyping

We ensure lean and agile project management by rapidly developing working proof of concepts or failing quickly and cheaply

Co-Development with our clients leveraging data analytics and risk expertise will create sustainable value for both sides

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Swiss Re

Analytics CapabilitiesData from SR and client

Data-Driven Insights &

Recommendation

Predictive Modeling & Machine Learning

Development of Risk Models leveraging

Machine Learning,

Swiss Re data and risk models/expertise

1 2 3

+ =

Industrial related data

Enrichment of additional data sources to gain

more granular insights by the client

Achievements

✓ Prevention

✓ Risk Assessment

✓ Client Engagements

✓ Innovation

Joint approach in deriving conclusions

Risk Drivers

PredictValidate

Historical Risk & Claims Data

External Data

Improve accuracy of model

Keep humans at the design center

Anomalies

Client Behavior

Visual analytics tool for

automotive recalls

Visual analytics of the Life

Science market

Claims

Documents/Text

Advanced Analytics client use cases

Thomas Keist DirectorInnovative Risk Solutions EMEASwiss Re Corporate Solutions

• Product development lead for Innovative Risk Solutions units since 2016. Leveraging experience for large structured transactions

• Joined Swiss Re 1994, held several positions in the areas of structured (re)insurance and alternative risk transfer solutions

• Joined Zurich Financial Services in 2000 for their reinsurance unit’s Initial Public Offering under the name of Converium (now Scor)

• Became Head Swiss Re Corporate Solution's Asia Pacific unit in 2006, based in Singapore

• Returned to Swiss Re in 2011, assuming GSA regional management

• Master of Science in Economics and Business Administration from the University of Zurich

Thomas Keist, Swiss Re Corporate Solutions

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Advanced data analytics client example 1Insurance coverage for loss of profit due to “loss of attraction”

Client’s deviation of monthly revenues from expected, against historic large events

Client’s risk retention

Swiss Re Corporate

Solutions Cover

Po

siti

ve D

evi

ati

on

sN

eg

ati

ve D

evi

ati

on

s

«positive events»

«negative events»

Deviation outlyers (95% confidence)

Advanced data analytics client example 2Insurance coverage for loss of profit due to Low Water Levels in rivers

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Panel discussion and Q&A

Daniel Ryan

Head Risk ResearchSwiss Re Institute

Thomas Keist

DirectorSwiss Re Corporate Solutions

Monica Epple

Head Data and Smart AnalyticsSwiss Re

Marc Covarrubias

Head Strategic Client Management EMEA

Swiss Re Corporate Solutions

MODERATOR

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Legal notice

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©2019 Swiss Re. All rights reserved. You are not permitted to create any modifications or derivative works of this presentation or webinar 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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