This document discusses how big data and social media can be useful for insurance companies. It provides two case studies of how insurers can use social media data: (1) to better assess health risks of applicants by checking their social media, and (2) to help with disaster damage assessments by identifying affected areas and tracking social sentiment during natural disasters. The document emphasizes that insurers need the right people, technology, data, leadership, and problems to focus on to successfully utilize big data.

1. Upsize – Big Data
For Insurance
Company
Murphy Choy
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own any of the graphics.

2. What do we think about actuarial
practitioners in insurance company?
Very unfortunately, this is what a layperson will think…

3. What do actuarial practitioners in
insurance company really do?
See the difference?

4. What is big data?
Basically, lots and lots of data…

5. Do Actuaries deal with big data?
Kinda of…

6. What is the next big data for
insurance companies?
Social Media…

7. Case Study 1: Assessing the risk of
the applicant.
Can we do better than a check up?

8. Checking the facebook
Assessing their health…in real time…

9. Checking the facebook
Pre filtering people with serious issues…

10. Big Data and Social Media
• The rise of social media has increased the
propensity for people to actually post their activities
online.
• This allows insurance companies to have an
additional source of information to work on.
• This is crucial for determining the risk in real time
which is not possible in the past.
• Let us look at several case study where big data
can be very useful to General Insurance
practitioners.

11. Case Study 2: Disaster Damage
Assessment

12. Sendai Earthquake

13. Hurricane Haiyan

14. Hurricane Sandy

15. Approach
• Identification of areas affected
• Through photographic and social media
information to identify places heavily damaged.
• Use social sentiment to track where people are
severely affected by the disasters.
• Many research papers have demonstrated how
social media can be useful in the midst of disasters.

16. How do we start to be in
the big data business?

17. Right People

18. Right Technology

19. Right Data

20. Right Leadership

21. Right Problem