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
