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Leveraging Geo-Spatial (Big) Data for Financial Services Solutions

For effective decision making, Big Data needs to be delivered at the right level of granularity at the right time. Capgemini’s FS BIM Innovation Practice, working through our Mastermind and Greenhouse processes to ensure a focus on real-world client issues, has developed a Reference Architecture (RA) based upon HP HAVEn to achieve these goals.

While Geo-Spatial Data has traditionally been applied to non-FS domains, effective application of this data has the potential to improve decision-making in FS, including in the areas of underwriting and pricing, claims, and bank and credit card fraud.

Presented at HP Discover Barcelona 2014 by:
Guillaume Runser - WW Solutions Marketing, HP
Ernest Martinez - Global Head - FS BIM Banking, Capgemini
Stephen Williams - BIM Innovation Practice Head, Capgemini

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Leveraging Geo-Spatial (Big) Data for Financial Services Solutions

  1. 1. Leveraging Geo-Spatial (Big) Data for Financial Services Solutions Ernest Martinez (Capgemini), Guillaume Runser (HP), Stephen Williams (Capgemini)/ 4.12.2014 #HPDiscover © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  2. 2. Please give us your feedback Session DT6127 Speakers: Ernest Martinez, Guillaume Runser, Stephen Williams Use the mobile app to complete a session survey 1. Access “My schedule” 2. Click on this session 3. Go to “Rate & review” If the session is not on your schedule, just find it via the session scheduler, click on this session and then go to “Rate & review”. Thank you for providing your feedback, which helps us enhance content for future events. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 2 without notice.
  3. 3. How Big Data is impacting the Insurance industry
  4. 4. Is the insurance industry keeping up with the changing risk environment ? “Insurers and brokers are trying to get their arms around the challenges better. I think part of the answer is investing in research and development; making better use of the vast amount of data available and perhaps looking at solutions with a greater degree of innovation - without discarding the fundamentals of insurers managing their books of business in a way that has served them well in times of financial turmoil for other sectors.” • President of FERMA © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  5. 5. Big Data is recognized throughout the Financial Services Industry as a key competitive lever “No other industry has more to gain from leveraging Big Data than the financial services sector..” Market Watch, Big Data in Financial Services Industry “Financial services companies should be looking to emerging big data tools as the answer to finding hidden consumer sentiment on a real-time basis.” Putting Big Data to Work for Financial Services Companies © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. “82% of those surveyed in the Chartered Institute of Loss Adjusters believe those insurers that do not capture the potential of big data will become uncompetitive” The Big Data Rush ”Part of the answer is investing in research and development is making better use of the vast amount of data available and perhaps looking at solutions with a greater degree of innovation” President of Federation of European Risk Management Associations “The visionary bank needs to deliver business insights in context, on demand, and at the point of interaction by analyzing every bit of data available” Financial Services Data Management: Big Data Technology in Financial Services
  6. 6. Most insurers agree on Big Data’s potential for competitive advantage Believe those insurers that do not capture the potential of Big Data will become uncompetitive Agree that analyzing multiple-source data together, rather than separately, is crucial to making accurate predictions Agree that linking information by location is key to usefully combining disparate sources of Big Data Say that the digitally-enabled world will see the emergence of new risk rating factors 82% 86% 88% 96% © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Source: the big data rush: how data analytics can yield underwriting gold. Survey conducted by Ordnance Survey and the Chartered insurance Institute, 25 April 2013
  7. 7. A wealth of data exists inside and outside the organization that could improve risk assessment • Geographic and Geo-Spatial Is the facility located in a site prone to natural disasters? © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. • Political Is the facility located in a region of political stability/instability? • Economic Is the facility located in a high, middle, or low economic area? • Crime Is the facility located in a high crime area? • Risk Density What are the nearby risk factors? • Customer Personal details, claims history, other policies ? • Claims How many claims have been made in this area?
  8. 8. The challenge is to integrate large volumes of varied data and make it accessible How do separate the data I need from the vast data that exists? How and where can I access the data I need? How do I identify new data sources to mine for relevant information? How do I analyze data in multiple formats from disparate sources? © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Business impact Delays and inefficiencies in collation of data required for informed decision-making Inability to treat risks individually and assess accurately Inability to use data proactively and lack of predictive capabilities =
  9. 9. Enhancing Financial Services Solutions with Geo-Spatial Data
  10. 10. Leveraging Geo-Spatial Big Data for Financial Services Solutions • To be useful to decision makers, Big Data needs to be delivered at the right level of granularity at the right time • Capgemini’s FS Business Information Management (BIM) Innovation Practice, working through our Mastermind and Greenhouse processes that ensure a focus on real-world client issues, have developed a Reference Architecture for Big Data based upon HP HAVEn to achieve these requirements. • Geo-Spatial Data has traditionally been applied to problems in oil and gas as well as utilities. However, effective application of this data has the potential to improve decision making in FS, including in the areas of: • Underwriting and Pricing – Individualized Risk Assessment • Claims – Adjuster Placement and Fast Claim Payouts • Bank and CC Fraud – Point of Sale Cross Referencing • Capgemini BIM Innovation is currently working with HP to incorporate geo-spatial data and reasoning into our Big Data Reference Architecture using our Commercial Insurance Risk Analytics (CIRA) platform as a use case • Through the inclusion of geo-spatial data and reasoning, and incorporating the power of Autonomy/IDOL to integrate these data, the depth of solutions we provide to our clients will dramatically increase. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  11. 11. Incorporating Geo-Spatial Data into the Reference Architecture enhances Financial Services Solutions Geographic Political Economic Crime Social Media Natural Perils Client Internal Data Sources Accounts Products © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Dashboards with Drill Down Analytics Client External Data Sources Customer Claims Enables advanced spatial reasoning to support applications in pricing, claims, including reserving, and fraud. Provides for the integration of other types of external data Geo-Spatial Data HAVE n Data Integration, Analytics, ETL and data store
  12. 12. In the UK, Ordnance Survey Data has been incorporated into the Big Data Reference Architecture The Ordnance Survey supplies data for FS in the UK by providing geographic information available to: • Develop Policy • Plan • Deliver Services • Monitor Success and Risk • The Points of Interest (PoI) database contains over 4 million unique places with over 600 classifications • As a strategic alliance partner Capgemini have full access to all historic data sets for free on a 3 year contract Key Uses: • Identify the use and function of different premises to enable accurate risk assessment • Monitor, track and analyse the changing retail space of city centres over time • Locate crime hotspots by PoI • Advanced OS API mapping tool for triangulation of risk factors • Link to core unstructured data sets © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  13. 13. Highly-Granular Geo-Spatial Data provides enhances risk analysis • Points of Interest (PoI): Identification of hundreds thousands of PoIs provides for more accurate risk assessments: • Proximity of risks • Nature of risks • Going beyond the Postcode Level: Building level data provides additional data to the assessor supporting individualized pricing as well as claims: • Distance of building from property line and access road • Height above sea/ground level • Estimated building size • Vector Mapping: Providing for complex spatial analysis to determine risk and exposure © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  14. 14. Commercial Insurance Risk Analytics on HP HAVEn
  15. 15. “60% of insurance firms affirm that underwriting systems technology provides high or very high value to their company1.” “86% Insurers agree that analyzing multiple-source data together, rather than separately, is crucial to making accurate predictions2.” Commercial Insurance Risk Analytics: Harnessing Big Data for Underwriting Efficiencies Source: 1 CEB FSI Technology Survey, 2013–2014 2 Ordnance Survey “ The big data rush: how data analytics can yield underwriting gold”. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  16. 16. Introducing a “one-stop shop” for collecting, synthesizing, and analyzing risk data Capgemini Commercial Insurance Risk Analytics (CIRA), powered by HP, gives underwriting professionals unprecedented access to accurate, granular information on individual risk factors for a much more informed, faster risk assessment and the ability to lower overall operating cost across the portfolio. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Multiple sources integrated for real-time decision making Supporting risk assessment on an individual policy basis for enhanced accuracy Providing the right data for the right decisions Enabling a focus on the business of underwriting
  17. 17. “Plug and play” capabilities display risk data exactly how you want it Through the integration of big data and our Rapid Data Visualization capabilities, Capgemini brings the right data in the right format, customized for underwriters and providing for comprehensive decision support. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Finely-grained risk data from multiple external sources ( such as social media), integrated with the insurer’s own data (such as policy and claims) Dashboard displays with full drill down analytics capability into the underlying data Our Rapid Data Visualisation methodology will be used to define a set of dashboards measuring risk grouping that are drillable to policy risks and further to supporting data.
  18. 18. Architected to provide a powerful, single data resource HAVE n Geographic Political Economic Crime Risk Density Customer Claims © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. CIRA Dashboard Data Integration, Analytics, ETL and data store Structured and unstructured data sources Integration of Multiple data sources for real-time decision making Granular Risk Data for increased accuracy
  19. 19. Big Data Cloud Mobility Security 100% of your data 1000x faster answers 1.2 month ROI * H A V E n 1,000,000+ machine events per second Hadoop/ HDFS © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 30x More data per server 700+ connectors * Source: Forrester Consulting, April 2013 Autonomy IDOL Vertica Enterprise Security nApps Catalog massive volumes of distributed data Process and index all information Analyze at extreme scale in real-time Collect & unify machine data with ArcSight Logger Powering HP Software + your apps Social media Video Audio Email Texts Mobile Transactional data Documents IT/OT Search engine Images HP HAVEn – Making Sense of the Noise
  20. 20. Backed by a business-driven approach, CIRA directly addresses real client challenges Capgemini intellectual property (IP) development originates from ideas, pain points, and issues of our insurance clients and involves clients and independent industry experts throughout the IP lifecycle. Business-driven approach to the definition and development of intellectual property removes a significant amount of risk for our clients CIRA core concept originated in a workshop with one of our global insurance clients Independent underwriting firm qualified to QA the CIRA - Proof of Concept (PoC) PoC is being demonstrated to multiple insurers in the EU and NA for feedback, shaping the next stage development Accelerated time to market with ability to move from concept to prototype within 45 days. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Capgemini Financial Services • 20 years of Insurance experience • More than 6,000 dedicated insurance professionals • Currently serving 11 of the top 15 insurance companies* • 3000+ BIM experts dedicated to financial services *Ranked by revenue; Forbes ‘The Global 2000’ for 2013
  21. 21. Solution demonstration
  22. 22. CIRA – The Commercial Insurance Risk Analytics Platform Information on CIRA is also available on YouTube https://www.youtube.com/watch?v=Qr8tAEsRI0Y © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
  23. 23. More information • Capgemini CIRA web : www.capgemini.com/cira/hp • HP HAVEn: www.hp.com/HAVEn • CIRA Solution Brief: http://bit.ly/1nVPdoM • CIRA demo video: http://bit.ly/1rmqXsX • Webinar: Empower Commercial Lines Underwriters with Data, Analytics, and Secret Sauce http://bit.ly/1pEkHMH • Request a live demonstration of CIRA: HAVEnAlliancesMarketing@hp.com • Visit the HP HAVEn Partner Solution booth at HP Discover © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

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