Making Sense of Big Data in Insurance

Size: px

Start display at page:

Download "Making Sense of Big Data in Insurance"

Philippa Sutton
8 years ago
Views:

1 Making Sense of Big Data in Insurance Amir Halfon, CTO, Financial Services, MarkLogic Corporation

2 BIG DATA?.. SLIDE: 2

3 The Evolution of Data Management For your application data! Application- and hardware-specific Mainframe Era For your structured data! Normalized, tabular model Application-independent query User control Relational Era For all your data! Schema-agnostic Massive scale Query and search Analytics Application services Faster time-to-results Modern Era SLIDE: 3

Normalized, tabular model Application-independent query User control Relational Era For

4 CONSIDER SLIDE: 4

5 Fraud Detection and Prevention Beyond algorithmic transaction analysis From claim-centric to person-centric - beneficiary behavior across claims Data sources beyond the firewall networks of people rather than individuals Information across all parties involved in a claim Counter-parties as well as partners (e.g. auto repair shops) Notes; images; sound files Integrated event processing Alerting; case management SLIDE: 5

rather than individuals Information across all parties involved in a claim Counter-parties as well as

6 Customer Insight Beyond policy administration Regulatory drivers Increase customer retention and satisfaction Personalized insurance Single customer view Across multiple channels and LoBs On-boarding docs; customer care logs; clickstreams; telemetry; geospatial SLIDE: 6

Single customer view Across multiple channels and LoBs On-boarding

7 Claims Management Beyond structured data Supporting notations, images and video Hierarchical ACORD standard formats Mainframe modernization Integrated workflow and information Event driven architecture Faster claim processing Innovative interaction models SLIDE: 7

modernization Integrated workflow and information Event driven

8 Underwriting Commercial and Reinsurance Beyond re-entering data Supporting information at quotation (loss histories, property schedules, etc.) Diverse file formats, rarely standard Typically assessed manually by the underwriter..then re-examined if a claim is subsequently made Time consuming, inefficient Information remains locked in the documents, with little or no analytical use. Data needs to be searchable and discoverable SLIDE: 8

) Diverse file formats, rarely standard Typically assessed manually by the underwriter.

9 THE TECHNICAL ANGLE SLIDE: 9

10 Last Generation Reference Data Unstructured OLTP Warehouse Documents, Messages { } Social Metadata Video Audio Signals, Logs, Streams Archives Data Marts Search SLIDE: 10

11 Hadoop MapReduce Hive HBase Lucene Solr HDFS

12 Hadoop s Limitations Hadoop was designed for batch processing Does not support real-time applications on its own Limited support for SQL-based analytics Requires expertise to configure, deploy and manage Until recently has not supported mainstream security authentication technologies You still need a database SLIDE: 13

Requires expertise to configure, deploy and manage Until recently has not

13 30 YEARS OF SUCCESS ACID IS INDISPENSABLE ENTERPRISE HARDENED RDBMS: THE GRAND DADDY OF DATABASES TODAY S DATA DOESN T FIT DATA MODELING IS SLOW SCALING IS SLOW & EXPENSIVE SLIDE: 14

14 Hadoop + Relational Database SLIDE: 15

15 SCHEMALESS FAST & AGILE RUNS ON SCALE- OUT CLUSTERS NOSQL: THE YOUNG UPSTART DATA IS MORE THAN KEYS & VALUES BUSINESS REQUIRES ACID TRANSACTIONS IT REQUIRES HA/DR/SECURITY SLIDE: 16

16 12 YEARS OF SUCCESS ACID IS INDISPENSABLE ENTERPRISE HARDENED ENTERPRISE NOSQL: THE BEST OF BOTH SCHEMA AGNOSTIC FAST, AGILE & POWERFUL RUNS ON SCALE-OUT CLUSTERS SLIDE: 17

17 Enterprise NoSQL Schema-agnostic design Real-time indexing for search and query Reverse queries for event processing and alerting Scale-out shared-nothing cluster topology Analytics and Visualization Transactions SLIDE: 18 Hadoop Integration

and alerting Scale-out shared-nothing cluster topology

18 Database + Search Interactive access to unstructured text Best if integrated at the DB layer: Quicker time to information Greater than the sum of its parts No separate indexes to maintain One less friction point Search as a text processing engine Turn text into numbers, create new dimensions Infer new information from text search and enrich SLIDE: 19

indexes to maintain One less friction point Search as a text processing engine Turn

19 New Generation Data Management Load and index data as is from any sources Schema-agnostic, poly-structure data, Full text search Deliver data to each consumer in the right format Security Transactions Compliance Interactions Fraud Detection Connections Risk Management Hadoop File System integration etc. SLIDE: 20

consumer in the right format Security Transactions Compliance Interactions

20 Example: Fraud and Financial Crime Prevention Analytics Profile Configuration Tools Profile Data Extracted From multiple sources Decision Support Profiles include social graphs SLIDE: 21

Data Extracted From multiple sources Decision

21 Final Thoughts One way to do more with less is to do less.. ETL, schema design, database maintenance.. to spend less on complex infrastructure and manual processes and focus more on harnessing new generation technology to maximize agility SLIDE: 22

22 THANK YOU SLIDE: 23

You Have Your Data, Now What?

You Have Your Data, Now What? Kevin Shelly, GVP, Global Public Sector Data is a Resource SLIDE: 2 Time to Value SLIDE: 3 Big Data: Volume, VARIETY, and Velocity Simple Structured Complex Structured Textual/Unstructured