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Transforming Insurance Analytics with Big Data and Automated Machine Learning

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3 Things to Learn About:

*How to create a next generation data platform and why it is important
*How to monetize this data using predictive modeling and machine learning
*Automated machine learning as a sustainable, cost-effective and efficient solution

Publicada em: Software
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Transforming Insurance Analytics with Big Data and Automated Machine Learning

  1. 1. Transforming Insurance Analytics with Big Data and Automated Machine Learning A formula for higher ROI
  2. 2. Agenda Mihaela Risca Sr. Solutions Marketing Manager Financial Services Cloudera Unlocking the Value of Insurance Data Satadru Sengupta Gen Mgr. Insurance DataRobot Automated Machine Learning – A Formula for Higher ROI for Insurers
  3. 3. There are two different alignments of these components in the market: • When data and analytics capability are bundled with capital, we have an insurance company. • When it is bundled with demand, we have an advisor or broker Data is at the center of the Insurance market
  4. 4. Explosion of Data
  5. 5. Why Machine Learning? • Analytics return $13 for every $1 invested (Nucleus Research) • Only 12% of data is leveraged for analytics (Forrester)
  6. 6. What is Machine Learning?
  7. 7. Why Big Data + Machine Learning? • Machine learning thrives on growing data sets • Bring disparate data sources together • Real time streaming
  8. 8. Machine Learning Use Cases in Insurance Pricing Customer Acquisition Underwriting Marketing, customer retention, prioritization. Equating risk and price, driving life-time value (LTV) Prevent Claim Fraud Underwriting triage: select the top 10% of the available risk for further analysis . Identifying claims with highest likelihood of being fraudulent.
  9. 9. Poll the Audience Where in your organization you see the most value for introducing machine learning? 1. Customer acquisition and retention 2. Underwriting/Actuarial 3. Quoting/Claims management 4. Fraud detection and prevention 5. Other
  10. 10. Key Data Management Challenges for Insurers Fragmented Systems and Data Silos Limited Access to Right Data at the Right Time Strategic Decisions Based on Subsets of Data Unable to Tap into New Data Sources or Correlate Data from Multiple Sources Simultaneously Disparate View of Customers, Markets and Risks Poor Data Quality and Lack of Governance
  11. 11. One Data Platform for Many Applications Handle real-time data ingest from diverse sources Governance and Security Data Streams Deployment Flexibility Machine Learning Capabilities Diverse Analytical Options Combine Data from Different Sources Data Mgmt. Hub Scale easily & Cost effectively Batch or Real- time Data Streams Data Sources Data Sources Data Storage & Processing Reporting, Analytics & Auditing Data Ingest Other Data Governance (Data Lineage, Data Protection) Fitness Car Telematics Applications
  12. 12. "New technology is transforming the way we work, and it is allowing the competition to do better than what we can. The strange thing is we know the urgency, and yet there is inertia." Inga Beale, CEO of Lloyd's of London February 2017
  13. 13. 1. Technology 2. Consumer & Market Economics 3. Data Science & Machine Learning … and they are interconnected. Three Strategic Areas of Focus
  14. 14. Machine Learning Applications in Insurance 1. Risk Selection & Pricing 2. Claims, Fraud and Litigation Management 3. Operations and Expenses Management “machine learning is the secret sauce for the product of tomorrow.” Google, 2015
  15. 15. Profitable Growth & Managing Expenses Becoming a 21st Century Insurance Company
  16. 16. Life Insurance Example 1 Underwriting Triage • Predicted low risk to fast track process • Predicted high risk to traditional underwriting for manual review Business Impact • Cost reduction through automation of reviews of applicants • Increased likelihood of acquisition due to fast track underwriting • Higher underwriting profitability by targeting the review process on underwriting loss avoidance Specific examples from clients • Predict the likelihood of an insured being in a preferred class or not – as determined by risk factors such as smoking status, existing condition, terminal disease • Predict the most likely class among several classes
  17. 17. Predict mortality risks among patients in remission of cancer: ○ Simplify Underwriting Process: Patients with good health prospects don’t need to go through a manual medical verification and avoid adverse selection ○ Reduce Costs of Claim by identifying high-risk patients and create more accurate underwriting rules ML model predicts patients with a very high risk of mortality ● 5 times more risky than average ● Around 10% of patients Life Insurance Example 2
  18. 18. … InsurTech and Future of Insurance
  19. 19. Machine Learning Strategy: Where It Is Failing? • A lack of data vision • Hiring and retaining good data scientists is impossible • Lack of Inclusiveness: Targeted end-users are not included in the machine learning problem solving process. HBR Article : “Stop searching for that elusive Data Scientist”
  20. 20. New Technology Opens Up New Possibilities To Executives Artificial Intelligence & Automation makes Machine Learning Affordable, Pervasive and Inclusive
  21. 21. Poll the Audience How do you primarily develop and deploy machine learning solutions in your organization today? 1. Multiple, small data science teams 2. One, big enterprise data science team 3. Outsource to consulting 4. We use automated machine learning 5. We currently don’t use machine learning
  22. 22. Elements of Automated Machine Learning Smart ● Accurate ● Appeal to experienced data scientists ● Control buttons are accessible to the users Easy to Use ● Intuitive, fully automated workflow ● Needs minimum inputs but has guardrails ● Interpretable & transparent ● Deployment focused
  23. 23. A 10 min journey to Automated Machine Learning (AML) using DataRobot Platform can we predict which patient is coming back to hospital within the first 30 days? Demo
  24. 24. What capabilities for DataRobot on Cloudera? HDFS ingest: DR can utilize data stored in HDFS directly Hadoop Modeling: Train ML models on the Cloudera data nodes directly Hadoop scoring: Any model can then be deployed on Hadoop directly Distributed (each node scores a data split) Uses Spark
  25. 25. Cloudera/DataRobot Integration Details DataRobot has the highest level of integration with Cloudera Cloudera Parcels A few click to install DR in Cloudera Manager! Cloudera CSDs Can use all the functionalities of Cloudera Manager (monitoring, resource mgmt…) Kerberos / Sentry Secured authentication YARN All the resources consumed by DataRobot are managed by YARN Spark DataRobot uses Spark for Hadoop scoring
  26. 26. Cloudera/DataRobot Integration Details
  27. 27. Apache Spark Ecosystem with Spark ML lib Spark MLlib API is available in Scala, Java, and Python programming languages
  28. 28. Training from Cloudera and DataRobot ● Introduction to Machine Learning - Cloudera Training https://www.cloudera.com/more/training/courses/intro-machine-learning.html ● Data Science for Executives - DataRobot Training https://www.datarobot.com/education/for-executives/ ● Machine Learning with DataRobot - DataRobot Training https://www.datarobot.com/education/for-business-analysts/
  29. 29. Learn More & Contact Us https://www.cloudera.com/solutions/insurance.html Cloudera Follow us: @Cloudera mihaela@cloudera.com Taneja Group Spark Market Adoption Report : LINK DataRobot Overview: LINK https://www.datarobot.com/go/insurance/ Follow us: @DataRobot satadru@datarobot.com DataRobot Executive Briefing: LINK The Machine Learning Renaissance: LINK Register for Wrangle Conference: July 20, San Francisco http://wrangleconf.com/
  30. 30. Thank you
  31. 31. Appendices Some screenshots Cloudera - DataRobot Integration
  32. 32. DataRobot - Ease of Deployment on Cloudera ● Deployment ● Mgmt/Monitoring
  33. 33. The DataRobot Service on Cloudera
  34. 34. DataRobot – HDFS Ingest
  35. 35. Copyright © DataRobot, Inc. - All Rights Reserved DataRobot Modeling on Hadoop Storage Application DR Edge Node … … Worker 2 Worker 1 Worker 3 Hadoop Data Node 1 Hadoop Data Node 2 YARN container 60GB (Worker 2) YARN container 60GB (Worker 3) YARN container 60GB (Worker 1) • YARN allocates memory on a data node when a worker wants to train a model • Each model is trained in memory on an available data node
  36. 36. DataRobot – Cloudera “in-place” Scoring
  37. 37. DataRobot & Cloudera – Seamless LDAP Authentication