Daniel Knüsli

Solutions to power your businessData Analytics: Was ist heute schon möglich? Potenziale. Grenzen. Relevanz

Road safetyThere are 1.2 million road casualties a year. Driver

and vehicle scoring, as well as technologies like driver

assistance (ADAS), can help us dramatically reduce road

casualties and develop a safer mobility future.

Cyber riskThe annual cost of cyber

crime to the global economy exceeds USD 400 billion.

Cyber solutions help us better manage and mitigate risks.

Natural catastrophesThe natural catastrophe

protection gap totalled USD 193 billion in 2017.

Parametric solutions can make insurance more accessible,

affordable and provide immediate pay out for

individuals and businesses to bounce back quickly.

Facing global risksTwo-thirds of global losses are uninsured

P&C Analytics • Tailored P&C Analytics consultancy

services to solve your every need • Portfolio Insights interactive risk

visualisation for steering and growth

Cyber • Cyber Product Suite for cyber

insurance product development• Cyber Analytics Platform for risk

scoring, portfolio, and accumulation management

Liability • Analyse and model liability portfolios

with Forward-Looking Modelling (FLM) to grow into new markets and segments

• Manage casualty accumulation risks

Property & Specialty• CatNet® natural hazard risk analyser• Sophisticated engineering

underwriting with PUMA• Agro Suite modular, end-to-end

agriculture solutions

Parametric• End-to-end pricing, risk monitoring,

policy administration, automated claims pay out and real-time reporting for flight delay, earthquake, tropical cyclone

SwiftRe® • Online risk placement, claims and

accounting platform • Cost-effective risk management• Full transparency into entire portfolio

Smart Homes • Develop next generation solutions

with advanced risk scoring• Preventive risk services • Fast time-to-market via Swiss Re’s

curated partners ecosystem

Automotive• Insurance-relevant driver scoring with

end-to-end telematics solution• Grow strategically and steer portfolios

with Motor Market Analyser

Ready to deploy

Increase efficiency, steer portfolios and grow into new markets and segments

P&C Solutions for your every need

Partnering with P&C Analytics…

Premium growthUnlocked premium growth potential of: >3%

UW profitability Improved portfolio loss ratio by: 6%

Pricing accuracy More accurate pricing leads to increased underwriting profit of: 5%

UW efficiency Reduced time to underwrite commercial risks by: 95%

Volatility reductionLong-term result volatility reduced by: 10 %

what we’ve already achieved with our clients

Powerful performance evaluation Outside-in smart visualisation of portfolio performance: INSTANT

We collaborate with our clients to

grow their business, increase their profitabilityand enhance their

efficiency

We provide tangible, data-

driven business insights that help our clients achieve

their ambitions

Broad set of analytic capabilities to generate powerful insights

Principles

Growth Profitability Efficiency

Go-to-Market

Portfolio and Loss driver analytics

Claims triaging and automation

Lapse, retention & propensity to bound analytics

Liability pricing analytics

Motor market analytics

Behavioural economics

Customer journey & platform consultancy

Portfolio Insights

Supply chain analytics

Property Risk and Cat analytics

Digital contract insights (Blockfinder)

Liability Cat modelling & analytics

Life Science Insights

Cross- and upselling analytics

Product Recall Insights

Value Chain

DistributionProduct

.DevelopmentUW &Pricing

OperationsClaims &

Portf. Mgmnt

Be

spo

keTo

ols

Our actionable business recommendations can help you reach your strategic objectives

A client story: Improved performance with insightsHow we use analytics to impact our clients’ underwriting strategy and improve profitability

6% decrease in portfolio loss ratio with further improvements expected

Developed new underwriting strategy and fine-tuned risk appetite based on analytics findings

6 months from kick-off to market roll-out

Enhancement of client portfolio data with external data sources

Detailed portfolio modelling and loss driver analysis by leveraging Swiss Re’s analytics tools and capabilities

Development of tactical risk map for future underwriting selection process and development of tailor made underwriting tool.

Our client had not achieved any significant performance improvement despite various internal portfolio “deep-dives” over past years.

On an industrial level, challenges included loss severity and frequency changes, impact of technology and cost pressures

Very large, global insurer.

Struggling with the rapidly changing industry, volatile and large losses and looking for solutions to better understand the changing risk landscape

6

The ResultsOur supportThe challengeThe client

Public recall data is not clean. Analytics and a combination of data sources are required to derive tangible business insights

17

130

7

15

1211

12

4

16

20

0

10

20

30

40

50

60

70

80

90

100

110

120

130

# r

eca

lls

1

name from DB

4

1 44

31

21

1 3

name from pdfs

TAKATA SOGYO

K.K. parent

17

113 130

TAKATA CORPORATION

TK Holdings, Inc.

Takata

T K Holdings

T.K. Holdings, Inc.

T.K. Holdings

TK Holdings (Takata)

Takata (Shanghai) Automotive Comp. Co.

TK Holding

Takata / T K Holding INC.

Takata/TK Holdings INC.

TK Holdings Inc.

TK Holdings

Takata AG

TK HOLDINGS INC

Takata Petri AG

Takata / T K Holding INC..

TK Holdings Inc. (Takata)

Takata/TK Holdings

TK Holdings Incorporated (Takata)

Example: NHTSA data on Takata recalls …

The NHTSA database lists the OEM that issued the recall, but not typically the supplier name. We can use text-mining techniques to extract the supplier name from “defect notices” in pdf form accompanying each NHTSA recall.

… combined with supply-chain data

Fiat Chrysler Automobiles N.V.

Takata costumers1, by market value

Honda Motor Co., Ltd.

Toyota Motor Corp.

Bayerische Motoren Werke AG

Volkswagen AG Pref

General Motors Company

Nissan Motor Co., Ltd.

Ford Motor Company

Renault SA

Ferrari NV

Tata Motors Limited

Leveraging third party supply-chain data, we gain insights into Takata’s customers, allowing to better understand supply chain risk and anticipate (accumulation) losses

no suit

lawsuit filed against Takata

1 Third party industry sources.

Data-driven insights into recall severity and frequency by component for better risk selection

… allow to identify car parts with increasing exposure to recalls

We use the compound annual growth rate (CAGR) of the last 5 years to identify problematic car parts with increasing exposure to recalls

Car-component level insights …

150’00050’000 450’000100’000

0

200’000

100

200

300

400

500

600

1’200

0

Body

# r

eca

lls

Wheel / Tires

Exterior Lighting

Powertrain

Interior System

Engine/Exhaust

Electrical System

Steering

Latches/Locks/Linkages

Engine Cooling

Fuel System

Heating / Ventilation

Brakes

Seat BeltsSeats

avg # units

Speed control (non-auto)

ChassisAir Bag

Other

Looking at the last 10 years, air-bags are leading in terms of units affected. Interior System recalls are frequent, but have a lower severity.

bubble scales by total number of units affected

7%

Engine/Exhaust

CAGR (5y)

Air Bag

Fuel System

4%

Seat Belts

-5%

Steering

PowertrainOther

SeatsChassis

Electrical SystemLatches/Locks/Linkages

Body

Engine Cooling

Wheel / TiresExterior Lighting 0%

Interior SystemSpeed control (non-auto)

BrakesHeating / Ventilation

6%

35%32%

2%

31%21%

18%18%

14%14%

-1%-2%

-5%

-15%

12%

A client story: Identify new markets and improve pricing accuracyHow we use analytics to identify growth opportunities and develop accurate pricing models

Improved pricing accuracy in various markets by 5%

Developed deep dive analyses and formulated holistic strategies for over 25 markets

2 months from kick-off to strategic insights of market opportunities

Combine our deep industry experience with expanded data pools and advanced analytics to develop an in-depth analysis to identify growth opportunities

Leverage Swiss Re’s forward-looking model and pricing expertise to enhance the pricing robustness and accuracy

Strategically and profitably grow into new risk pools and markets

Lack of information to identify and quantify the growth potential within specific markets and segments

Missing in-house resources to develop a holistic growth strategy leveraging market insights

Very large, global insurer.

Lack of clear strategy to identify and prioritize growth opportunities among various markets and segments

9

The ResultsOur supportThe challengeThe client

Marktgrösse: Buttom-up-Marktschätzung durch Kombination interner und externer Daten in erprobter Swiss Re-Schätzungsmethodik

Swiss Re interne und externe Datenquellen …

30 Umsatz

10

50

70

# F

irm

en

90

15

0

11

0

13

0

16

0

Externe Daten• Bureau van Dijk (Orbis)• SME Finance Forum• World Banketc

Swiss Re interne Daten• SwissRe Institut (Makroökonomisches

Expertenteam)• LRD• CatNet®• Underwriter und Marktspezialisten

(qualitativer Input)

… verbunden mit einer robusten Schätzungsmethode

13090

Umsatz

10 7030 50 110 150

Schätzung

Werte

Segmentierung der Firmen in (Sub-) Industrien

Industrie 1

Industrie 2

Parametrische Schätzmethode zur Approximation fehlender Umsatzkennzahlen und Subindustrien mit wenigen Datenpunkten

def mittelstand

Änderung des Mittelstandes im verarbeitenden Gewerbefür Baden-Württemberg

positiverUmsatzzuwachs

Legende

200y100y 50y

Swiss ReFlut-Zonen

Wiederkehrperiode

Die Karte zeigt den Umsatzzuwachs von mittelständischen Betrieben im verarbeitenden Gewerbe pro Postleitzahl, zusammen mit den Flutzonen des Swiss Re Flut-Risikomodells

negativer Umsatzzuwachs

Mittelstand: Betriebe mit einem Umsatz zwischen 10 und 250m Euro

Freiburg

Stuttgart

Konstanz

Tübingen

Marktgrösse: Das Segment mit >250m EUR Umsatz hat ~10 mal weniger Firmen, aber erzeugt vier mal mehr Umsatz als der Mittelstand

Anzahl Firmen und Umsatz pro Branche

• Der Mittelstand (10 bis 250 mEUR) generiert einen Umsatz von 1300 Milliarden Euro,

• das Segment mit Umsatz > 250m, generiert 5800 Milliarden Euro

• Das Segment mit >250 mEUR Umsatz hat ca. 10 mal weniger Firmen als der Mittelstand

• Im verarbeitenden Gewerbe sind es mit 763 vs. 5998 Firmen 8 mal mehr im Mittelstand

• Die Mehrheit der Firmen mit Umsatz über 250 mEUR sind in West Deutschland gelegen, spezifisch Baden-Württemberg, Bayern und Nordrhein-Westfallen

Beobachtungen

Umsatz

Geographische Verteilung

Anzahl Firmen

37,2

272,7

355,6

3,8

3,2

74,6

256,0

133,6

220,7

4,6

50,8

28,3

4,1

388,7

356,9

911,8

524,4

2′000 bn Euro

Retail Trade

Agriculture, Forestry, Fishing

Public Administration

Construction

Manufacturing

Finance, Insurance, Real Estate

Mining

Services

Transportation and Public Utilities

Wholesale Trade

2.7

1′727.8

1′377.1

10 to 250 mEUR

> 250 mEUR

79

75

55

37

763

20

170

398

340

403

2′0004′0006′000

3′985

1′0084′078

1′029

6

# Companies

5′998

4

1′548

4′966

2′572

Marktstrukturen und -trends: Swiss Re’s makroökonomisches Vorhersagemodell prognostiziert ein solides jährliches Wachstum von ca. 3.3%

Rezession

GDP Growth

Finance, Insurance,Real Estate

Agriculture, Forestry,Fishing

Manufacturing

Services

Wholesale Trade

Mining

Construction

Public Administration

Retail Trade

5.2%

Inflation (CPI)

Transportation andPublic Utilities

2.0%

3.4%

2.3%

4.3%

6.0%

3.1%

0.6%

6.8%

2.2%

2.6%

1.3%

-16%

-14%

-12%

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

2008 2009 2010 2011 2012 2013 2014 2015 2016

jäh

rlic

he

Wa

chst

um

sra

te

Wholesale Trade

Finance, Insurance,Real Estate

Manufacturing

Services

Modell für die Prognose der jährlichen Wachstumsrate: • Basierend auf Swiss Re internen, makroökonomischen Vorhersagemodellen • Training 2008-2016• Vorhersageperiode 2018-2023

Historisches Industrie-Wachstum der Umsätze in Euro Durchschnittliche jährliche Wachstumsrate 2018 - 2023

Risikobewertung: Granulare Einblicke in die Exponierung gegenüber Naturgefahren

8,7%

100,0%

3,1%

50,3%

15,5%

43,4%

27,3%

12,4%

50,4%

11,5%

6,4%

2,3%

62,6%

Erdbeben

Waldbrand

6.2%

Flut

Hagel

Hagel-Karte

65,7%

100,0%

3,1%

28,3%

13,7%

86,3%

7,8%

11,5%

67,3%8.1%

Waldbrand

2.2%

6.0%Erdbeben

Flut

Hagel

Ba

de

n-W

ürt

tem

be

rgN

ord

rhe

in-W

est

fale

nH

ess

en

Erdbeben-Karte

46,5%

100,0%

53,5%

20,1% 11,1%

100,0%

3,8%

58,5%Flut

0.0%Erdbeben

0.0%

Waldbrand

0.0%

6.4%

Hagel

Flut-Karte

• Wir analysieren die Exponierung des Mittelstandes pro Bundesland bezüglich verschiedener Naturgefahren und aggregieren die Information auf Bundesland-Ebene

Statistische Analyse

• Die Exponierung ist zum Teil stark Bundesland-abhängig. So ist Erdbeben vor allem im Westen ein Thema, Hagel im Süden und Waldbrand nur im Nordosten (in geringem Ausmass).

• Flut ist ein lokales Thema und von Relevanz für ganz Deutschland

Einblicke

Legende

Erdbeben

Waldbrand

Hagel

Flut

Risikobewertung der Sachversicherung: Risikoexponierung nach Branche

Risiko-Exponierung für Sachschäden nach Branche Zoom-in für das verarbeitende Gewerbe

Swiss Re‘s Tarifierungsmodell für Sachschäden ermöglicht eine Einschätzung der Risiko-Exponierung nach Brache, für Betriebsunterbrechung (BI) und Materialschaden (MD)

niedrige Exponierung

hoheExponierung

• Der Finanzdienstleistung-Sektor und der Dienstleistungssektor haben ein geringeres Schadenpotential.

• Das verarbeitende Gewerbe und die Landwirtschaft sind wesentlich stärker exponiert

Bottom-up risk scoring for risk selection and prospecting

0,0 0,5 1,0 1,5 2,0

1,75

Albertsons

Disney

CVS

0,68Eldorado

Extended Stay 0,75

Hyatt Hotels

Kyo-Ya

Sears Holding

Sears Hometown

Wal-Mart

Wyndham

0,83

0,86

1,06

0,88

0,72

0,84

0,73

0,81

26

468

504

337

425

10,000 05,000

6

2,785

2,858

6,931

4

4,878

Total Risk(1) Normalized Risk(2)

Disney displays the highest relative risk due to its buildings being in locations that pose severe seismic and tropical cyclone risk.

Sears Hometown

Disney

Sears displays one of the lowest relative risks due to its buildings being well spread out across the USA with a proportion being outside of high Nat cat risk zones.

(1) The Total Risk is a proxy for the expected Nat cat loss for the corporate across all locations.(2) The Normalized Risk = Total Risk/#locations; therefore this measure serves as a relative risk across corporates.

xxx agency channel – demographics strongly skewed with respect to age and gender

Locations Sales force demographics

xxx

Competitors

Relative sales forcestrength1

23%47%

71%

254

67%

53%33%

29%

77%

15%

85%8%48

92%

87 187 362 267

95% 687%

< 25

8%

93%3%

97%

25 -30

926%

92%

5%

30 - 40

94%

40 - 50 50-60

1%99%

60 <

30 195 179 199

Clie

nt

ad

viso

ryA

ll a

ge

ncy

em

plo

yee

s

xxx’s sales force has a higher age – also compared to the market – and has an even stronger gender bias than the market – especially at lower ages

Market

Median age 42

% female 7.6

xxx

44

5.4

Market

49

5.6

xxx

56

4.6

All agents FKB

Female Male

High

Low

A client story: Improved transparency with insightsDeploying a highly tailored made visualization suite to enhance management focus and improve data quality

Instant drill-in capability to identify both positively and negatively performing areas

Deployment enabled swift identification of systematic data weaknesses and transition to a culture of excellence

Swiss Re provided a client-customised tool with relevant KPIs demonstrating data quality, performance and volume

This provided management with powerful levers to quickly identify areas requiring attention and growth opportunities

Client invested in cutting edge IT systems but lacked the ability to monitor and assess business performance across the company due to lacking data quality & complex organizational structure

Large Global player

Client required easy to understand analytics to improve focus & steer business towards improved performance across portfolio

The resultsOur supportThe challengeThe client

2 months

Snap-shots from the Portfolio Insights toolHarnessing the full potential of your data

Snap-shots from the Portfolio Insights toolHarnessing the full potential of your data