Analytics & Insurance. Serene Zawaydeh
1 like754 views
The document discusses several topics related to using big data analytics in the healthcare and insurance industries: - Annual losses to healthcare fraud in the US and EU total between $70-260 billion and $30-100 billion respectively. Analytics can help detect, prevent, and combat fraudulent activity. - A bank used customer transaction data and social media to provide personalized, targeted messages and gifts to valued customers. - Health insurers use claims data and text analysis to identify candidates for disease management programs and determine data quality issues. - Insurers are exploring using social media to investigate claims and potentially set insurance rates, which regulators may not permit. - SAS provides analytics solutions to help insurers with claims
Analytics & Insurance. Serene Zawaydeh
- 2. Healthcare Fraud – Annual Losses USA: $70 billion to $260 billion Estimates of The National Health Care Anti-Fraud Association and the Federal Bureau of Investigation EU: $30 billion to $100 billion How can we best detect, prevent and combat fraudulent activity in health care? SAS White Paper
- 3. Big Data Variety Structured versus Unstructured Data Volume Velocity Real Time ValueValue Privacy and Security of Data
- 4. The Power of Analytics TD Bank Commercial July 2014 Over 9 million views ATM Automatic Teller Machine Turned Into Automated Thanking Machine Pre-selected customersPre-selected customers Customized, personalized the message. Recognized the customers Thanked them for being faithful customers Analyzed customers’ transactions Recognized their needs. Identified supported family members (Sick daughter; Kids) Provided a gift that is customized to customers’ needs, and the supported family members. Tickets to Trinidad to Visit Daughter Human Touch Disney Land for the kids
- 5. Big Data Governance & Insurance Industry Examples Big Data Governance, An Emerging Imperative Sunil Soares MC Press 2012
- 6. Claims Analytics – Data Quality Large health plan processed over 500 million claims per year Each claims record consisting of 600 to 1,000 attributes Used Predictive Analytics to determine if certain proactive care was required Business Intelligence Team Limited Effectiveness of Predictive Analytics:Limited Effectiveness of Predictive Analytics: Physicians used inconsistent procedure codes to submit claims Analyzed text within claims documents Determined candidates for disease management programs. “Blood sugar monitoring”… Diabetes
- 7. Insurance Investigation Use Facebook to validate auto claims Claim filed for hit and run with auto insurer Comments on daughter’s Facebook sayingComments on daughter’s Facebook saying daughter was responsible for the accident Policyholder convicted of filing a fraudulent insurance claim
- 8. Underwriting Should insurers be able to use social media for underwriting purposes to set rates for policies? ExamplesExamples Use Facebook information of an athletic 50 year old tennis player to reduce the premium due to lower risk Or increase premium of a Skydiver due to higher risk?
- 9. Regulation USA Each state has department of insurance to regulate insurance industry within its jurisdiction. Each department of insurance has an important role in protecting the privacy of policyholders in that state.that state. US States once barred insurers from using credit scores to predict the likelihood of claims, most now allow it, with California being an exception. Will regulators permit insurers to use social media to set rates?
- 10. Monitoring Social Media Health plan established a dedicated team to monitor mentions of company in social media If member or physician posted complaints about health plan on Twitter, someone at company reviewed it and responded.responded. Objective: Introduce the company’s response into public record Anybody who saw the complaint would see the response. HIPPA privacy regulations – social media comments Moved conversation to phone ASAP
- 11. Centralized Insurance Claims Database European Country Big Data and private insurance Rate not high enough Many insurers don’t use customer-focused or marketing-focused data warehouses Claims losses… Fraud InvestigationClaims losses… Fraud Investigation Solitary claim for theft of luxury car from relatively low-income postal district. However… 30 different people in the same postal district had theft claims for luxury cars with other insurers over the previous two months
- 12. Centralized Insurance Claims Database European Country Insurance industry in a European Country banded together to pool claims information for fraud investigation Database does fuzzy matches of People living in the same street, with the same or similarPeople living in the same street, with the same or similar birth dates, or with couple of numbers transposed, with accounts with the same bank, with similar names, and so on. Mostly done after claims were paid Might be too late to recover the money
- 13. Need for Big Data Governance Need to reassure participating insurers that - Their data is secure - Will never be disclosed to competitors - Will not be used to damage relationship with loyal clients. - Security and Confidentiality of claims analytics - Real time or near real time access to historical Investigator data - Insurers investigate the right claims upfront, versus after they have been paid
- 14. Whiplash Claims Minor accident but the passengers claim large compensation for neck injuries, which are very hard to prove Claims investigators have ability to compare claims from same area across multiple insurers where claimants have the same or similar names or postal codes and shared bank accounts
- 16. SAS White Papers Claims Fraud Detect and prevent both opportunistic and professional fraud throughout claims process Underwriting Fraud Prevent premium leakage at point of sale and renewalPrevent premium leakage at point of sale and renewal Rate evasion Spot rate evasion tactics during the quote process before issuing a policy
- 17. Analytics for Insurance What Does Big Data Really Mean for Insurers? New Paradigms and New Analytic Opportunities Featuring as an example: SAS® High-Performance Analytics An SMA Perspective. Authors: Deb Smallwood, Founder ; Mark Breading, Partner Published Date: August, 2012. This perspective is based on SMA’s ongoing research on data and analytics in insurance.
- 18. SAS Benefits Deliver information that is consistent, accurate, verifiable and up- to-date. SAS Insurance Analytics Architecture enables access to accurate data consistently, when and where it is needed, giving increased confidence in accuracy and timeliness of your data.confidence in accuracy and timeliness of your data. Complete, integrated view of all enterprise data. Always have access to data needed, when needed
- 19. SAS Predictive modeling Social network analysis Structured and unstructured data analysis Real time Data Cleansing Data mining, Clustering, Neural networks, decisionData mining, Clustering, Neural networks, decision trees Regression analysis Scorecards Data Dictionary covers all key insurance subject areas – e.g., customer, policy, claim, financial accounting and reinsurance, predefined logical and physical data models Standardizes more than 5,000 insurance data elements.
- 20. Serene ZawaydehMBA, French – Scholarship (2006-2008) B.Sc. Electrical Engineering (1993-1998) CFA Level 1 (2011) University of Toronto, School of Continuing Studies (January 2014 – Present) Enterprise Data Analytics (Big Data) Courses: Big Data Tools and Techniques Mining Financial, Operational, and Social Network Data (Sept - Dec 2014) Value Proposition and Technologies of Enterprise Data Analytics Foundations of Enterprise Data Analytics – Concepts and Controls Leadership Essentials Be an Effective Negotiator Critical Thinking International Foundation of Employee Benefit Plans (IFEBP) and Dalhousie University E-courses (August & Sept 2014) - The Group Insurance Landscape- Canadian Course - Group Benefits Design and Administration - Canadian Course- Group Benefits Design and Administration - Canadian Course - Group Benefits Funding and Pricing - Canadian Course - Life Cycle of a Group Benefits Plan - Canadian Course • Data Analyst, Sun Life Financial (May 2014-September 2014) • Technology Transfer Intern – UOIT (Oshawa) (2012-Mar 2013) Patent search and intellectual property protection; Engineering innovations; applications for funding; identifying potential licensees Head of Research, Equity Financial Analysis; Equity Valuation (2008-2011) Stocks listed in Jordan Telecom Market Research Consultant; Senior Research Analyst; Research Analyst (2003-2007) Middle East and North Africa (Arabic and French language skills) One month Scholarship to Germany – Intensive course in German language (2001) Zentrale Mittelstufenpruefung Zertifikat Deutsch
- 21. Thank You Serene Zawaydeh [email protected] Cell: 647-466-2422 ca.linkedin.com/in/serenezawaydeh