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

2. We would like to talk about 3 things today1
Introducing Versicherungskammer Bayern (VKB) 2
Analytics and Use-Cases for Smart Data 3
Next Steps

3. Introduction to Versicherungskammer Bayern (VKB)

4. Versicherungskammer Bayern – Who are we?
Largest public-owned insurer and 8th largest in Germany (Revenue EUR ~ 7 bn; ~ 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)

5. 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

6. ..., and the journey has also started within VKB1
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 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
STEP

7. Further challenges seen across the industry
Forces of change and digitalization impact insurers on four overlapping and interdependent dimensions
Empowerment
Value, not price
Customer insight
Transparency
Missing 360° view of customer information
More channels
Collaboration
Privacy regulation
Unable to fully exploit connections between past, present and
future customer
touch-points
Potential identified in customer retention via consistent
customer experience and new offerings
New offerings
New competition
Advice changes
Complex legacy
Skills challenge
Regulatory impact
Need for holistic
risk-assessment
and use of analytics
in claims management
Customers:
Growing empowerment and interconnection
Growing demand for “best deal on value” (not only price)
Cross-industry competition for customer data and insight ========= key and core capability, digitization of processes is analytics-driven combined with cognitive capabilities to drive customer insight, address personalization of new products and harvest knowledge from unstructured data as a new capital!
Growing demand for transparency
Interactions
Increasing number of sales channels – parallel and intersecting
New types and culture of collaboration
Increasing regulatory influence on privacy
Services
New and broader service offerings beyond classical coverage
Sophisticated competition from other industries (e.g. automotive, retail, banks)
Increasing regulatory influence on quality and payments of advisory services (fee-based and subscription-based)
Structures
Complex and siloed legacy systems, processes and organizations
Limited availability of insurance experts
Increasing impact of local / regional regulation on costs and business model+
Taling poins for the challenges:
Unable to fully exploit connections between past, present and future customer touch-points
Lack of integration over multi-channel offerings and interactions; missing 360° view of customer information at time of interaction, hence inappropriate offers and communications
Potential identified in customer retention via consistent customer experience
Need for holistic risk-assessment and use of analytics in claims-management
Challenged in using analytics to add short-term value or enhance long-term strategy

8. Data & Cognitive Analytics
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
Data & Cognitive Analytics
New expertise

9. Data and Cognitive Analytics require the right foundation
Make sense of industry business needs & take action
Descriptive
Discover
Report
Analyze
Predictive
Predict
Decide
Act on time and in context
Cognitive
Gain unique insight into people, things and businesses:
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
Enables the access to an endless universe of Information and possibilities

10. Analytics and Use-Cases for Smart-Data

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

12. Using Analytics tools and Smart-data, we are approaching our customer-centric use-cases
Grow
Data sources
Multiple customer touchpoints
Call center
SMS
Cross-sell/ up-sell analysis
Web
Cross-sell/ up-sell analysis
Next-best action analysis
Customer propensity analysis
Customer lifetime value analysis
Mobile Apps
Customer analytics
Outcomes & methods
Customer look- alike targeting
Customer churn & attribution analysis
Retain
Direct mail
Transactional data
Attract & acquire
Behavioral customer segmentation
Social network analysis
Customer interaction history
Chat
Customer journey/path analysis
Customer demographic data
Sentiment analysis
Call center
Customer location analysis
Customer engagement analysis
Twitter
Customer satisfaction analysis
Customer device usage analysis
Social
Weather
Engagement & experience
Location
Mobile Apps Web
Source: Forrester, 2014

13. Analytics improves outcomes for key insurance business use cases framework
Customer Retention & Cross/Up-Sell Analytics
How can I better understand my policyholders to improve retention and determine relevant offers?
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?
Improve Customer Insight
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?
Innovate Business Models
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?
Manage Risk & Fraud
Selektiert was zur VKB passt

14. VKB is already working towards many of these use-cases along a „Smart-Data Strategy“
Concrete Examples
Customer Retention & Cross/Up-Sell Analytics
How can I better understand my policyholders to improve retention and determine relevant offers?
Use Watson to recognise displeasure in unstructured 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
Underwriting/ Pricing Optimization
How can I apply additional data sources to improve the underwriting & pricing process?
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
Catastrophe Insight & Response
How can I analyze data to get advanced insight to avoid losses and respond post event?
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
Claims Prediction, Individualized Web-Access and Text-Mining described in Application

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

16. Next Steps

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

18. 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

19. 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

20. … it was a pleasure Want to stay in touch? Dr. Shivaji Dasgupta

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