Text analytics opportunities in the Insurance domain

2,663 views
2,341 views

Published on

Text Analytics Opportunities in the Insurance Domain - March 2012

Published in: Technology, Economy & Finance

Text analytics opportunities in the Insurance domain

  1. 1. Insurance Domain – An Intro Areas - Life and Non-Life (Health, Property, Motor, Accident) Parties involved – Insurer, Agent, ClientProblems being faced $70 billion and $230 billion of medical care spending is fraudulent in the year 2011 Rate of fraud based on exposure to health data was 7 percent in 2009, up from 3 percent in 2008 in spite of existing analytics Source:HealthDataManagementSolution using data mining Predictive modeling Outlier detection Social network analysis
  2. 2. Category Definition Quantitative Text Mining Data Mining In the Insurance Text Mining In the Insurance Domain Domain SAS TAS SAS IBM SPSS Attensity IBM SPSS HP Autonomy
  3. 3. CompetitorsCompany Product name Features & Benefits Strategy Social Analytics Sentiment Analysis Strong market share in text mining for Attensity Insurance Industry Solution Detect early warnings the Insurance domain Fraud detection Customer service Sentiment Analysis IBM SPSS Text analytics for surveys Theme Identification Categorization Market leader in data mining IBM OmniFind Analytics Intelligent text mining IBM SPSS Predictive analysis Clustering SAS TextMiner Data importing Sentiment Analysis SAS Sentiment Analysis Market leader in domain Reporting independent text mining SAS Ontology Management Ontology Dev and Management Classification SAS Enterprise Content Categorisation Entity extraction Insurance datamodel Data management Solution for quantitative data mining in SAS SAS Insurance Analytics Architecture Reporting the Insurance domain BI Database marketing Fraud detection Megaputer Polyanalyst Text mining solution in a niche space Customer service Subrogation Prediction Hearsay Hearsay Social Social Analytics Text mining solution in a niche space
  4. 4. Customers (Insurers)2010-2011 data in India 48 registered life and non-life insurers Rs.30,000 crores paid up in capital Rs.3L crores in premium Source: Insight2010-2011 data world-wide $4.3 trillion in premium Source: Wikipedia
  5. 5. Market Segments Segmentation Criteria • IT spending capability • Popularity • Growth and Operating Margin TOP Country TOP Indian Tie-upsInsurers Insurers with CNP France LIC India AXA France Aviva UK Aviva UK Metlife US ING Vysya NetherlandsState Farm US Birla Sun Life Canada Max New York Life US ING Netherlands Bajaj Allianz UK Alianz UK Bharti AXA France ICICI Lombard AIG US Canada General Tata AIG US
  6. 6. Target Segments1. *Europe2. UK3. US* excluding UK Specific to Insurance Attensity Product Positioning Quantitative Text Mining Data Analytics IBM/SAS IBM/SAS Domain independent
  7. 7. Use-Cases in Insurance domain • Agency force attrition Agency department • Agent productivity and agent success factorsRenewals department • High lapse in the initial years of the policy • Identification of customer segment for cross-selling, Marketing & sales • CRM department • Analysis of customer needs & behavior • Information identification/extraction Operations • Fraud detection patterns using Text Analytics and department Social Networks • Product enhancements Products department • Market research • Competition analysis
  8. 8. • Augment the product line by focusing on the Insurance domain• Products that will help customers (Insurers) with large client base• Products that will optimize operating costs
  9. 9. Use-case solved in the market Risk management To enhance product requirements Claim analysis Discover emerging patterns Premium renewals & Customer Retention
  10. 10. Selecting Potential Use-Cases • Agency force attrition Agency department • Agent productivity and agent success factorsRenewals department • High lapse in the initial years of the policy • Identification of customer segment for cross-selling, Marketing & sales • CRM department • Analysis of customer needs & behavior • Information identification/extraction Operations • Fraud detection patterns using Text Analytics and department Social Networks link analysis • Product enhancements Products department • Market research • Competition analysis
  11. 11. Shortlisted Use-cases Solution Value by Insurers Technology Requirements • Sentiment Analysis Products • Classification • Entity Extraction • Rule Engine Fraud detection using • Mapping Data Social Network Analytics • Pattern Detection CRM • Web Crawling • Ontology Development • FAQ database Agents • User Experience • Workflows
  12. 12. Product VALUE • Automatic online responses to agents and customers • Identification of patterns that are responsible for agent productivity Features • Automated warnings about competition, market and customers • Map customer data sourced from social networks to inhouse database • Enhancing agents productivity • Encouraging agents and customers to feed-in questions to the Benefits database • Call center workload reduced • Track customers for frauds from yet another angle • Improved customer and agent satisfaction Value • Better visibility of environment • Increased bottom-line revenues
  13. 13. Pricing Model• SAAS based subscription or enterprise license products Pricing discounts when bundled Professional services effort for custom ontology building Rate plans based on number of customers the product satisfies Data usage based differential pricing
  14. 14. Amarnath Bhandariamarnathbhandari@yahoo.com

×