Transformational Insurance Analytics for Sustained Competitive Advantage How a regional carrier leapfrogged into the future of analytics and data

Transcription

1 Transformational Insurance Analytics for Sustained Competitive Advantage How a regional carrier leapfrogged into the future of analytics and data 0

2 Today s Speakers Sanjeev Kumar, Saama As Saama s Head of Insurance, Sanjeev Kumar, is responsible for delivering innovative data analytics solutions for the insurance industry at Saama. Sanjeev is very passionate about solving business problems and eternally believes in process improvement. He strongly believes that today s next generation business intelligence in the form of advanced analytics will revolutionize the insurance industry. Sanjeev is a winner of the Application Innovator award and a regular speaker at various conferences, including Business Intelligence. Cindy Maike, Hortonworks As General Manager for Insurance, Cindy Maike is responsible for the overall business and technology vision, go-to market strategy, and executive engagement worldwide for Hortonworks market leading Hadoop offerings for the insurance industry. This includes the many opportunities of working with customers and partners leveraging analytics for current day business growth and exploring usage of new data driving innovation in the evolving world of insurance. 1

3 Agenda Insurance and IT Trends in Insurance Transformation Opportunities to Achieve Data-Driven Business Objectives through a Modern Data Architecture Regional Insurer Case Study and Demo Summary and Key Resources 2

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5 The Landscape of Insurance Data has Changed Dramatically 400B Connected Home products by B Workman s Comp Fraud annually 50M US drivers would have bought UBI Insurance by 2020 The Era of Big Data Disruption has Arrived! 500GB data per annual vehicle in UBI programs 70% of insurers will offer mobile capabilities by % Efficiency by drones in the workflow by

6 Changing Rules of Engagement New Competitive Landscape Breakdown of Value Chains Customer Centricity Technology & Data as Strategic Weapon Regulation and Risk 5 5

7 Opportunities to Achieve Data-Driven Business Objectives through a Modern Data Architecture 6

8 View of Insurance Industry Data Landscape Data velocity Batch Real-time Prior loss runs 3rd party risk models Policy verification Claim system FNOL intake Claims triage Investigation TPA invoices Applications / Submissions Weather-event Vendor invoices Forms and letters Social media Medical bills Drone image feeds Geo-location Deposition recording Sensor (IoT) Notes and diary Transcriptions Photos Dimensional/Structured Semi-structured Data variety Behavioral/Unstructured 7

9 The Future of Data Actionable Intelligence GROUP 1 GROUP 2 DATA AT REST INTERNET OF ANYTHING DATA IN MOTION STORAGE STORAGE GROUP 3 GROUP 4 Hortonworks unique approach to Data-in-Motion and Data-at-Rest powers Actionable Intelligence 8

10 Actionable Intelligence from Connected Data Platforms Modern Data Applications Capturing perishable insights from data in motion Ensuring rich, historical insights on data at rest Necessary for modern data applications DATA IN MOTION ACTIONABLE INTELLIGENCE DATA AT REST Hortonworks DataFlow Hortonworks Data Platform 9

11 The Key is to Find the Value in Data Providing Business Insights and Outcomes ANY DATA Existing and New Datasets EXISTING SOURCES BUSINESS VALUE Understand Data Richness ANALYTICAL VALUE Method of Analysis Policy Claim CRM Loss Control Agency Billing Rating Document Ease of Access NEW SOURCES Social Clickstream Geo- Location Sensor Mobile Server Log Real-time Impact (Footprint) Transformative All of the Above Preventive Discovery/ Learning Predictive Reporting Monitoring 10

12 How do we use all this data? To capitalize on the value of big data and leapfrog the competition, leading insurers are moving towards consolidated data management. The introduction of an enterprise data hub built on open-source Apache Hadoop provides a cost-effective way for insurers to aggregate and store ALL their data, in any format, in a highly secure environment. Users can access rich data sources, blend and analyze data from any source, in any amount, detect patterns, model risk and gain valuable realtime insights that deliver results. 11

13 How are the trends driving the decision making in P&C? 45% of carriers say that big data /analytics has significant impact in the way they do business 26% of carriers say that big data /analytics has completely reshaped the way they do business 12

14 Regional Insurer Case Study 13

15 The Situation In early 2014, a large regional carrier with under $1b in Net Written Premiums and operation in 20+ states was in a similar dilemma. It was facing: Competitive and pricing pressure on the outside and it was struggling to integrate Structured (Survey) and Unstructured (Notes) data leading to inconsistent customer satisfaction and customer retention Non-availability of centralized data (challenge from data to KPIs) There was a real need of real-time analysis, that required processing of massive amounts of data faster Need of scalable, integrated, secure data in a cost effective way Optimized and consolidated data from many sources and maintain organization s - Single Source of Truth It wanted to embark on a transformation program to consolidate and modernize its existing IT systems, which support core Insurance processes Policy Admin, Claims, and Billing but was faced with some questions/decisions 14

16 Which road to take? Status Quo Change Traditional EDW Should we wait for core system replacement first? Fluid Analytics for Insurance/Hadoop Start the advanced analytics journey along with core system replacement? 15

17 Insurance Core System Replacements The Data Conversion Dilemma Core System Modernization/Replacement Key Steps 1. Requirements Defined 2. Software Evaluated & Selected 3. Configuration Begins 4. READY, SET, GO.. then Meeting 1: Scope: We need to convert 10 years of data Meeting 2 Costs Estimated It s HOW MUCH!? Scope: We will convert 1 year of history What about the Data? Meeting 3 Status: Estimated budget over-run Scope: Only open transactions will be converted Meeting 4 Status: Over budget Scope: We will go live with new transactions 16

18 A Radical Shift in Data Strategy A Data Warehouse is great for: Structured data Predictable query patterns Combining similarly structured data But not so great for: Otherly structured data Ad-hoc data blending for analysis Complex, multi-stage data analysis Very high volumes of data Low-latency / real-time data The Carrier made the unique decision to harness Hadoop to aggregate terabytes of internal and external data into a single platform and power their enterprise data hub. 17

19 How it all comes together 18

20 Saama s Fluid Analytics for Insurance A solution comprising Saama's Fluid Analytics for Insurance,a unique offering that established a strong, robust data foundation utilizing Hortonwork s Hadoop and provided the capability for real time streaming and predictive analytics, including self-service reporting in visualization software using Tableau Fluid Analytics is a highly flexible, high reuse, rapid iteration process designed to provide more frequent, more relevant and highly measurable business outcomes. 19

21 Saama Fluid Analytics for Insurance Conceptual Architecture The data is presented in a visual user interface that is intuitive, insightful and actionable. Charts and dashboard are enriched with industry-standard KPIs, implemented in multiple server platforms and mobile devices. imap culls data from multiple sources, including: internal structured data (policy, claims, customer, billing, telematics and call center); internal unstructured data (claims notes, telematics, log data); real time geospatial data; syndicated data from third party sources; enterprise search, and social media. Data Quality & Standardization Structured Data Hadoop Ecosystem Unstructured Data External Data The Hadoop Ecosystem is leveraged for processing, managing and generating large data sets. For the first time, analysts, underwriters and actuaries will have direct access to the data in native format in a self-service mode. 20

22 Hortonworks Delivers Connected Data Platforms Modern Data Applications INTERNET OF ANYTHING ACTIONABLE INTELLIGENCE Saama s Fluid Analytics for Insurance Real-time Integrated Secure Adaptive PERISHABLE INSIGHTS DATA IN MOTION Hortonworks DataFlow Powered by Apache NiFi HISTORICAL INSIGHTS DATA AT REST Saama s Fluid Analytics for Insurance Hortonworks Data Platform Powered by Apache Hadoop Open Central Interoperable Ready 21

23 Central Management of Data at Rest YARN DATA OPERATING SYSTEM Centralized Platform for operations, governance and security Batch Interactive STORAGE GOVERNANCE OPERATIONS SECURITY STORAGE Machine Learning Streaming Diverse Applications run simultaneously on a single cluster Maximum Data Ingest including existing and new sources, regardless of raw format Search Shared Big Data Assets across business groups, functions and users 22

24 See it in Action! 23

25 Driving Better Business Outcomes Driving top / bottom line improvements by better understanding the existing book of business Driving improved agency selection and operations decisions by analyzing performance of distribution partners Improving customer loyalty through prediction of churn leveraging date across Policy, Claims, Billing Improving product marketing, pricing and features by analyzing performance across agent, market, customer Improved risk mitigation by early detection of sales problems and loss trends to determine the best course of action 24

26 Summary and Key Resources 25

27 Key Takeaways The insurance engagement model is/has changed Managing new and existing data and analytical needs is achievable through: Industry accelerators of Fluid Analytics for Insurance Visualization for business users using capabilities like Tableau Manage traditional and new data sources regardless of volume, veracity or variety through open source capabilities Insurance is about the #Futureofdata 26

28 Q&A Or questions to 27