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
Introducing Versicherungskammer
Bayern (VKB)
2
Analytics and Use-Cases
for Smart Data
3
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
1
Strategy aimed at “getting
the basics right”
Focus on “Lighthouse Projects”
and Analytics
Substantial analysis of market situation
and potential
Dedicated Digital-Team focusing on
customer-oriented digital projects
Assessment of VKB’s capabilities,
skills, and overall “readiness”
Focus on prioritized targets for
digital enablement – efficiency,
growth and customer-satisfaction
Prioritization and decision on getting
the basics right incl. development of a
comprehensive roadmap
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2
“Big-Data and Advanced Analytics”
identified as Lighthouse topic
Further challenges seen across the industry
Forces of change and digitalization impact insurers on four overlapping and interdependent dimensions
Missing 360°
view of customer
information
 Empowerment
 Value,
not price
 Customer
insight
 Transparency
Potential identified
in customer
retention via
consistent
customer
experience and
new offerings
 New offerings
 New
competition
 Advice
changes
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 More channels
 Collaboration
 Privacy
regulation
Unable to fully
exploit connections
between past,
present and
future customer
touch-points
 Complex
legacy
 Skills
challenge
 Regulatory
impact
Need for holistic
risk-assessment
and use of
analytics
in claims
management
Digital reinvention has become an urgent necessity, and data
is the key
Faster, More Frequent, Iterations. More Discovery & Experimentation
Technology
forces…
…are disrupting
industries
…necessitating
digital reinvention
Digital reinvention via Data enables and supports
deep, compelling experience
New
focus
New
ways
to
work
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Data &
Cognitive
Analytics New
expertise
Data and Cognitive Analytics require the right foundation
Make sense of industry business needs & take action
Descriptive
Predictive
 Discover
 Predict
 Report
 Decide
 Analyze
 Act on time
and in context
Cognitive
Gain unique insight into people, things and businesses:
Data you possess
 Customer records
 Transactional systems
Enables the access to
an endless universe of
Information and possibilities
 Institutional expertise
 Operational systems
 Content systems
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Data that helps you
compete
Data that’s
In motion
 News
 Internet
of Things
 Events
 Image data
 Social media
 Sensory data
 Geo-spatial
 Weather
Analytics and Use-Cases for Smart-Data
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VKB has created a target vision for group-wide analytics
platform
Big-Data Analytics Tools (examples)
BI-Tools (examples)
Cognitive and
Analytics Layer
Data-Management Platform
Anonymized customer data
Intention/customer behaviour/context
Data platform
…
…
Data sources
Partner
data
Source: Discussions on industry best-practices
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External data sources
(examples)
Internal data sources
(examples)
Data
synchronisation
Modern DWH
Using Analytics tools and Smart-data, we are approaching our
customer-centric use-cases
Use-Cases
Data sources
Multiple customer touchpoints
Call center
SMS
Web
E-mail
Mobile Apps
Transactional data
Customer interaction history
Customer demographic data
Customer
analytics
Direct mail
Outcomes &
methods
Chat
Call center
Twitter
Weather
Location
Source: Forrester, 2014
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Social
Mobile Apps Web
Analytics improves outcomes for key insurance business
use cases framework
Improve
Customer
Insight
Innovate
Business
Models
Manage
Risk &
Fraud
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Customer Retention &
Cross/Up-Sell Analytics
Digital
Engagement
Distribution
Optimization
How can I better understand
my policyholders to improve
retention and determine
relevant offers?
How can I reach my customers
with the same standards,
regardless
of channel?
How can I effectively manage
my producers and identify
the right actions?
Claims Optimization & Fraud
Prevention
Internet of Things Utilization
Underwriting/ Pricing
Optimization
How can I gain a deeper
understanding of my claims
process and better predict,
detect, and investigate fraud?
How can I capitalize on the
Internet of Things to offer
personalized value-added
services to my insureds?
How can I apply additional
data sources to improve the
underwriting & pricing
process?
Catastrophe Insight
& Response
Financial Performance
Management
Risk Management
& Compliance
How can I analyze data
to get advanced insight to
avoid losses and respond post
event?
How can I create a solid
foundation for better financial
decision making?
How can I ensure effective risk
management is used across
the enterprise?
VKB is already working towards many of these use-cases
along a „Smart-Data Strategy“
Concrete Examples
Customer Retention &
Cross/Up-Sell Analytics
Improve
Customer
Insight
How can I better understand
my policyholders to improve
retention and determine
relevant offers?
Underwriting/ Pricing
Optimization
Innovate
Business
Models
How can I apply additional
data sources to improve the
underwriting & pricing
process?
Catastrophe Insight
& Response
Manage
Risk &
Fraud
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How can I analyze data
to get advanced insight to
avoid losses and respond
post event?
 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“
 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
 Use weather and traffic data for claims management
 Built-in analytics from previous claims resolution
 Move towards predictive and effective resolution of claims
Claims Prediction, Individualized WebAccess and Text-Mining described in
Application
Data-Analytics Use-Cases
USE-CASE ROADMAP 2017
Use Case für Partners
Vertrieb
und Marketing
Sach


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
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Betrugserkennung
Watson – Medizinisches
Gutachten
-
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Gesundheitsfragenkorrelation

Vertriebspriorisierung über
Analytics (mit SSKM)

-
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Leben

Firmenkundenschaft

Kranken
Schadenmanagement
Modellierung der fortgeschrittenen Algorithmen für
Aktuariat
Telematik (OVAG)
Vertriebsstatistik über AloA
(2VM)
Personalisierung der Webseiten
Watson – Angebotswunsch

16
Underwriting
& Pricing
Versicherungskongress Potsdam
April 2017

Schadenregulierung durch
Predictive-Analytics (OVAG)
Schadenprognose auf Basis
Wetter (mit öVUs)
Management des
Bestandsgeschäfts

Watson – Unmutsäußerung
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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 Governance
Data Source
Open, internal and
external Data sources
Ingestion
Fast, scalable
Data ingest and
enrichment
Analytical Data
Repositories
Data lake with
Hadoop and optimized
analytical DWH
Data Security
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Discovery
& Insight
Advanced Analytics
and Visualization
Framework
Already, Watson as cognitive system in pilot to predict
customer satisfaction
Understand…
Understand unstructured data (pictures,
voice, text)
Analyse complexity
Learn,...
Learn from results (via feedback loops),
interactions and iterate
Becoming intelligent via learning
Answer,…
Building and reviewing hypothesis
Using intelligent Analysis-Tools
Delivering answers
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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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… it was a pleasure
Want to stay in touch?
Dr. Shivaji Dasgupta
[email protected]
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BACKUP
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