Smart Data for CustomerCentricity at Versicherungskammer Bayern Dr

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

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 Seite 2 © Konzern VKB

Introduction to Versicherungskammer Bayern (VKB) Seite 3 © Konzern VKB

Versicherungskammer Bayern – Who are we? § Largest public-owned insurer and 8 th 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) Seite 4 © Konzern VKB

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 Seite 5 © Konzern VKB

. . . , 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” Prioritization and decision on getting the basics right incl. development of a comprehensive roadmap Seite 6 © Konzern VKB 2 Focus on prioritized targets for digital enablement – efficiency, growth and customer-satisfaction “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 competition § Advice changes Seite 7 © Konzern VKB § More channels § Collaboration § Privacy regulation § Complex legacy § Skills challenge § Regulatory impact 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

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 Seite 8 © Konzern VKB 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 Enables the access to an endless universe of Information and possibilities § Transactional systems § Institutional expertise § Operational systems § Content systems Seite 9 © Konzern VKB 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 Seite 10 © Konzern VKB

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 Seite 11 © Konzern VKB 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 Grow acquire Attract & Customer interaction history ioral Behav er custom on ntati segme Customer demographic data Twitter Weather Location Source: Forrester, 2014 Seite 12 © Konzern VKB t-be ana st acti on lysi s Outcomes & methods C en ust ga om an gem er aly e sis nt Cust o satis mer factio n analy sis er tom /path s Cu ney sis r y jou anal Customer analytics SMS er e om alu t s v Cu ime ysis t al e lif an hurn mer c Custo ribution & att sis analy E-mail Retain Transactional data Cus to alik mer lo e ta rget oking Nex l/ sel is ss- lys Cro ll ana -se up Mobile Apps er m ty to si us n is C ope lys pr ana Web Cross-sell/ up-sell analysis Call center Multiple customer touchpoints Direct mail Social network analysis Se n an time aly nt sis er Custom e ag s u device analysis er tom on s i Cu cat sis lo aly n a Data sources Chat Call center Social Mobile Apps Web

Analytics improves outcomes for key insurance business use cases framework Improve Customer Insight Innovate Business Models Manage Risk & Fraud Seite 13 © Konzern VKB 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 Seite 14 © Konzern VKB 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 Web. Access and Text-Mining described in Application

Data-Analytics Use-Cases USE-CASE ROADMAP 2017 Use Case für Partners Vertrieb und Marketing Sach Leben Firmenkundenschaft 16 Seite 15 © Konzern VKB Schadenmanagement Modellierung der fortgeschrittenen Algorithmen für Aktuariat § § - § § Betrugserkennung Watson – Medizinisches Gutachten - § Gesundheitsfragenkorrelation § Vertriebspriorisierung über Analytics (mit SSKM) § - § § § Telematik (OVAG) Vertriebsstatistik über Alo. A (2 VM) Personalisierung der Webseiten Watson – Angebotswunsch § § § Kranken Underwriting & Pricing Versicherungskongress Potsdam April 2017 § Schadenregulierung durch Predictive-Analytics (OVAG) Schadenprognose auf Basis Wetter (mit öVUs) Management des Bestandsgeschäfts § Watson – Unmutsäußerung - § -

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 Seite 17 © Konzern VKB 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 Seite 18 © Konzern VKB

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 Seite 19 © Konzern VKB