This document discusses Versicherungskammer Bayern's (VKB) strategy around using smart data and analytics for customer-centricity. VKB is one of the largest insurers in Germany. It has started a journey of digital transformation and sees data and analytics as important to improving customer insight, innovating business models, and managing risk. The document outlines VKB's vision for an analytics platform and describes several use cases it is pursuing, such as using Watson for customer satisfaction analysis and predictive analytics for claims management. Next steps discussed include creating an integrated data platform and analytics environment and further deploying cognitive services like Watson across various use cases.

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Smart Data for Customer-Centricity at
Versicherungskammer Bayern
Dr. Shivaji Dasgupta
Versicherungskammer Bayern
May 2017

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We would like to talk about 3 things today
1
2
3
Introducing Versicherungskammer
Bayern (VKB)
Analytics and Use-Cases
for Smart Data
Next Steps

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Introduction to Versicherungskammer Bayern (VKB)

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Versicherungskammer Bayern – Who are we?
 Largest public-owned insurer and 8th largest in Germany (Revenue EUR ~
7 bn; ~ 7.000 employees)
 3 strong regional footprints in Bavaria; Berlin/ Brandenburg and Saarland
 15 iconic brands (with deep regional roots) including newer online brand
 Selected international activities in Luxembourg and the UK
 Owned by the Sparkassen Group (>50 million customers)

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Digitalization is a major trend for German insurances, …
Our customers increasingly adopt a digital behavior
Our competition is stepping up their digitalization efforts
(e.g., AXA and Allianz)
Our employees desire a digital and flexible work
environment

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..., and the journey has also started within VKB
STEP 2
Focus on “Lighthouse Projects”
and Analytics
Dedicated Digital-Team focusing on
customer-oriented digital projects
Focus on prioritized targets for
digital enablement – efficiency,
growth and customer-satisfaction
“Big-Data and Advanced Analytics”
identified as Lighthouse topic
1
Strategy aimed at “getting
the basics right”
Substantial analysis of market situation
and potential
Assessment of VKB’s capabilities,
skills, and overall “readiness”
Prioritization and decision on getting
the basics right incl. development of a
comprehensive roadmap

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Further challenges seen across the industry
Missing 360°
view of customer
information Unable to fully
exploit connections
between past,
present and
future customer
touch-points
Need for holistic
risk-assessment
and use of
analytics
in claims
management
Potential identified
in customer
retention via
consistent
customer
experience and
new offerings
 Empowerment
 Value,
not price
 Customer
insight
 Transparency
 New offerings
 New
competition
 Advice
changes
 More channels
 Collaboration
 Privacy
regulation
 Complex
legacy
 Skills
challenge
 Regulatory
impact
Forces of change and digitalization impact insurers on four overlapping and interdependent dimensions

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Digital reinvention has become an urgent necessity, and data
is the key
…are disrupting
industries
…necessitating
digital reinvention
Technology
forces…
Digital reinvention via Data enables and supports
deep, compelling experience
New
focus
New
ways
to
work
Data &
Cognitive
Analytics New
expertise
Faster, More Frequent, Iterations. More Discovery & Experimentation

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Data and Cognitive Analytics require the right foundation
Enables the access to
an endless universe of
Information and possibilities
CognitivePredictive
 Predict
 Decide
 Act on time
and in context
Descriptive
 Discover
 Report
 Analyze
Data you possess
 Customer records
 Transactional systems
 Institutional expertise
 Operational systems
 Content systems
Data that helps you
compete
 News
 Events
 Image data
 Social media
Data that’s
In motion
 Internet
of Things
 Sensory data
 Geo-spatial
 Weather
Make sense of industry business needs & take action
Gain unique insight into people, things and businesses:

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Analytics and Use-Cases for Smart-Data

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VKB has created a target vision for group-wide analytics
platform
Data sources
Data platform Anonymized customer data
Intention/customer behaviour/context
Data-Management Platform
Data
synchro-
nisation
Cognitive and
Analytics Layer
Modern DWH
BI-Tools (examples)Big-Data Analytics Tools (examples)
… …
External data sources
(examples)
Internal data sources
(examples)
Partner
data
Source: Discussions on industry best-practices

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Using Analytics tools and Smart-data, we are approaching our
customer-centric use-cases
Data sources
SMS
Multiple customer touchpoints
E-mail
Direct mail
Chat
Call center
Social
Mobile Apps WebLocation
Weather
Twitter
Customer demographic data
Customer interaction history
Transactional data
Mobile Apps
Web
Use-Cases
Call center
Customer
analytics
Outcomes &
methods
Source: Forrester, 2014

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Customer Retention &
Cross/Up-Sell Analytics
How can I better understand
my policyholders to improve
retention and determine
relevant offers?
Improve
Customer
Insight
Innovate
Business
Models
Manage
Risk &
Fraud
Digital
Engagement
How can I reach my customers
with the same standards,
regardless
of channel?
Distribution
Optimization
How can I effectively manage
my producers and identify
the right actions?
Claims Optimization & Fraud
Prevention
How can I gain a deeper
understanding of my claims
process and better predict,
detect, and investigate fraud?
Internet of Things Utilization
How can I capitalize on the
Internet of Things to offer
personalized value-added
services to my insureds?
Underwriting/ Pricing
Optimization
How can I apply additional
data sources to improve the
underwriting & pricing
process?
Catastrophe Insight
& Response
How can I analyze data
to get advanced insight to
avoid losses and respond post
event?
Financial Performance
Management
How can I create a solid
foundation for better financial
decision making?
Risk Management
& Compliance
How can I ensure effective risk
management is used across
the enterprise?
Analytics improves outcomes for key insurance business
use cases framework

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VKB is already working towards many of these use-cases
along a „Smart-Data Strategy“
Concrete Examples
 Use Watson to recognise displeasure in unstructured E-mail text
 Follow-up in marketing with customer segments
to avoid churn, as well as offer other possible products
 Work towards „next-best action“
Improve
Customer
Insight
Customer Retention &
Cross/Up-Sell Analytics
How can I better understand
my policyholders to improve
retention and determine
relevant offers?
 Use Netcrawler to track (near) real-time price segments
 Use other data sources for localized information
 Offer individualized pricing along with next-best action
for specific customer segments
Innovate
Business
Models
Underwriting/ Pricing
Optimization
How can I apply additional
data sources to improve the
underwriting & pricing
process?
 Use weather and traffic data for claims management
 Built-in analytics from previous claims resolution
 Move towards predictive and effective resolution of claims
Manage
Risk &
Fraud
Catastrophe Insight
& Response
How can I analyze data
to get advanced insight to
avoid losses and respond
post event?
Claims Prediction, Individualized Web-
Access and Text-Mining described in
Application

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Use Case für Partners
Schaden-
management
 Schadenregulierung durch
Predictive-Analytics (OVAG)
 Schadenprognose auf Basis
Wetter (mit öVUs)
 Betrugserkennung
 Watson – Medizinisches
Gutachten
 -
 -
Underwriting
& Pricing
 Gesundheitsfragen-
korrelation
 -
 Modellierung der fortge-
schrittenen Algorithmen für
Aktuariat
 -
Vertrieb
und Marketing
 Telematik (OVAG)
 Vertriebsstatistik über AloA
(2VM)
 Personalisierung der Web-
seiten
 Watson – Angebotswunsch
 -
 Vertriebspriorisierung über
Analytics (mit SSKM)
Sach
Kranken
Management des
Bestandsgeschäfts
 -
 Watson – Unmutsäußerung
 -
Leben
Firmen-
kundenschaft
16 April 2017Versicherungskongress Potsdam
Data-Analytics Use-Cases
USE-CASE ROADMAP 2017

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Next Steps

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Analytics platform driven by a coherent data strategy and
insights-driven organisation
Concrete Platform requirements and facilitation in progress
Data Security
Data Governance
Analytical Data
Repositories
Data lake with
Hadoop and optimized
analytical DWH
Discovery
& Insight
Advanced Analytics
and Visualization
Framework
Ingestion
Fast, scalable
Data ingest and
enrichment
Data Source
Open, internal and
external Data sources

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Already, Watson as cognitive system in pilot to predict
customer satisfaction
Understand…
Understand unstructured data (pictures,
voice, text)
Analyse complexity
Learn from results (via feedback loops),
interactions and iterate
Becoming intelligent via learning
Learn,...
Building and reviewing hypothesis
Using intelligent Analysis-Tools
Delivering answers
Answer,…

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Next steps …
 Create data platform as a strategic lake environment
with center of competence and integrated analytics
within platform
 Attach Watson platform and services with data
management platform
– Use cognitive functionality of data management
platform for training cognitive services
– Establish a centralized governed data exchange
platform for joint cognitive/analytics use cases
 Implement self-service analytics platform with
integrated security

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Want to stay in touch?
Dr. Shivaji Dasgupta
shivaji.dasgupta@vkb.de
… it was a pleasure

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