BIG MUNICH RE Digisurance Big Data Use Cases

Transcription

1 Image: Bayerische Zugspitzbahn Bergbahn AG / Lechner Digisurance Big Data Use Cases 5 February 2016 Wolfgang Hauner, RID1.4

2 Trend Radar Correlation Map Computing Everywhere Industrialization 4.0 Predictive Analytics Open Data Collaborative Consumption Mobile Health Services Virtual Assistant Systems Integrated Systems Autonomous Systems and Devices Automated Decision Taking 3D Printing Big Data Context-aware Computing Haptic Technologies Augmented and virtual worlds Web 4.0 Digitalization Web-Scale IT New Payment Models Loc-based services Telematics Smart Home Internet of Things Crowdsourcing Cybersecurity Wearable Devices Digital Identity Risk-based Security Software-defined Anything Cloud/Client Architecture Robotics/Drones Citizen Development User Centered Design On-Demand-Everything Trend A trend depicts all kind of future development with an impact on one specific business. Trends tend to influence and drive each other. Example of a trend: Big Data Sub-trend A sub-trend specifies one potential form of appearance of a trend. Example of a sub-trend: Predictive Analytics 3 February

3 Data Analytics: Leverage data knowledge Center of Competence Knowledge / Technology Link to Innovation Topics Build Analytics Community Generate business value through data analytics 3 February

4 Data Analytics Topics 3 February

5 How Data Analytics supports new business opportunities Data sources Internal data: Underwriting data, Claims data,... External data: customer data, sensor data, Combination of internal and external data Leading edge technology Platforms: SAP HANA, SAS HPA, Open source tools: Hadoop, Hive, Spark, Infrastructure management: Ambari, git, Advanced analytics skills Explorative analysis Textmining Multivariate effect analysis Predictive modeling Client management remains central entry point for all services and solutions 3 February

6 Which topics drive our clients? Wearables Predictive UW Textmining Churn Analysis Up-/Cross- Selling Data Sources Fraud Detection Social Media Analysis Big Data Technology Telematics Geospatial Sensor Data 3 February

7 Big Data Pilot: Customer 360 Results Benefits xx Generate a holistic, customizable and comprehensive report of a client in just one click Identify business opportunities based on selected target group Extract the results of the 360 to transfer it into a PowerPoint presentation Outlook Integration in our new CRM tool, especially the analytics part shall be further developed (e.g. LoB split, inclusion of health, integration of other countries, integration of other news sources) 3 February

8 Big Data Pilot: ELD Early Loss Detection (incl. Loss Scout App) Results Benefits Easier, faster and cheaper information about insurance relevant loss events Matching with risk data portfolio possible at the same time Transparency on all losses independent of participation and risk assessment Database on loss events allows in-depth trend detection Faster & more effective claims management Current Scope: Property loss events (Fire) in USA & UK Outlook Testing phase with Munich Re clients until end of march Further extension: Casualty (especially D&O) and additional new Markets for property losses (Spain and Latin America) in new pilots 3 February

9 Analytical Use Case: Munich Re Data Miner Results Multi-dimensional searches Parallel analytical processing Various visualizations Exportable results Benefits Multi-dimensional searches based on standardized search technology to accelerate web research (example Tianjin) Extended analytics to gather further data insights based e.g. on topic analysis and organizational grouping Parallel processing and delta mechanism for multiprocessed search requests Results shown in different visualizations (word cloud, table, topic analysis, etc.) and exportable to excel (share results to the benefit of all) Outlook Additional analytics modules for better insights and broader application Collaboration functionalities for more efficient case analysis 3 February

10 Analytical Use Case: Insurance product hot spot analysis Results Benefits Provides an accurate overview of an insurance product s sales structure Blue regions represent a low probability to purchase the insurance product, while purple regions represent the hotspots Various factors like age, gender, occupation, place of residence, etc. can be taken into account for the model creation Supports resource allocation of sales forces Outlook Extend the model s application area to any lines of business Analysis of customer needs 3 February

11 Image: Bayerische Zugspitzbahn Bergbahn AG / Lechner Thank you very much for your attention Wolfgang Hauner, RID Münchener Rückversicherungs-Gesellschaft 2015 Munich Reinsurance Company