IFM Analytics

1. FICO® Insurance Fraud Manager User Group: San Diego, CA | May 7--8, 2014 IFM Analytics What’s New? Supriti Singh Senior Scientist, Insurance Fraud ManagerFICO

2. FICO® Insurance Fraud Manager User Group: San Diego, CA | May 7--8, 2014 IFM Analytics: What’s New? Speakers Nitin Basant Senior ScientistFICO Jeremy Greene PhD Lead ScientistFICO Robin Scientist IIFICO

3. Network Analytics For Insurance (IRE)-Nitin • Member Centric Analytics-Nitin • Focused Analytics-Robin • IFM Claims Model Analytics: Peak and Valley Detection-Jeremy • Discussion- Supriti

4. Network Analytics for Insurance (IRE)

5. The future of Analytics Detect Networks • Detect provider/ pharmacy/ facility networks to profile and score Learn New Fraud Tags • Including from Outlier Detection Models and Link Analysis Continuous Scoring Learn Learn Learn Known Patterns to Filter • E.g. combinations of specific procedure codes and modifiers Learn Additional Characteristics • E.g. # number of providers in the network Learn

6. Network Analytics Available in IFM Scoring Engine Claims Cases Pharmacies Investigational Review Prescribers Members 3rd Party Private 3rd Party Public Matches and Visualization Suspect(s) Search/Match 560 650 960 Claims Cases High Scoring Claims, Providers, pharmacies, etc. Pharmacies Proactive Detection Prescribers Search Criteria Members 960 3rd Party Private 3rd Party Public Proactive Batch Network Scoring Prioritized Suspicious Network Leads Search/Match All Claims, providers, pharmacies, etc.

7. Network Analytics • Focused Analytics • Networks with high concentration of suspicious claims/ providers • Seemingly unrelated providers who share a lot of members • Providers with multiple NPIs • Etc • Networks Model • Identify all the networks in the data • Develop a model to score the networks as an entity

8. Member-centric Models Using medical, pharmacy and facility data together

9. Member-centric models • A high priority for IFM • Follow the member using member ID across different claim types like medical, pharmacy, facility, etc. • Consistent and reliable data across claim types improve the scope and quality of analytics • Example – Member ID, Rendering Provider ID • Model development using medical and facility claims underway

10. Focused Analytics

11. Focused Analytics • High Units Identifies claim lines which have unusually high number of units as compared to the norm. Example 1- 402 is a lot of EKG’s especially when they are done for an “unspecified general medical exam”. Example 2- 50 x-rays of the knee for knee joint  at a cost of $2169.00 paid is too High. One or two would be more appropriate.

12. Focused Analytics • Procedure Upcoding Identifies providers who have unusually high tendency to perform/ bill for more expensive procedures. Procedure groups created using data driven cluster on textual description of procedure codes and diagnosis codes. Provider Specialty: Orthopedic Surgery

13. Focused Analytics The analysis aims to identify providers with unusually high Relative Value Units (RVU) sum on a particular day. RVU examples - 32854 - Lung transplant with cardiopulmonary bypass 90.00 RVU 99201 - Office/outpatient visit new patient 0.48 RVU Provider Specialty: Chiropractic 1.1 Million claim lines in a year 22 Million dollars paid in the same year Service Date 11/19/2012 12/17/2012 RVU Sum 2,977.22 2,828.64 Claim Lines6,2276,039 Members 2,9972,911 • High RVU

14. Focused Analytics Identifies providers who billed services adding up to an excessive number of hours in a single day. Uses time-based procedure codes . Timed Procedure examples - 96102 - Psychological testing by technician 60 minutes 96150 - Health and Behavior Assessment 15 minutes Provider Specialty: Pediatrics 280,875 minutes from 248 days averaging 19 hours a day Service Date 2/8/2011 1/19/2011 Total Minutes2,000 (~33 hours)1,965 Total Paid 4,498.02 4,408.98 Claim Lines192 204 Members 112 116 • Impossible Day

15. Focused Analytics • Additional analytics available as a service • Models currently available only for professional claims • Flexible input file format • Currently packaged/ready for deployment • Accelerated innovation-to-value for clients • More to come • Geospatial – members visiting providers geographically distant • Procedure Unbundling

16. IFM Claims Model Analytics: Peak and Valley Detection

17. Current IFM Approach • The IFM Medical Claims model currently has 6 analytics: High Dollar Procedure, High Dollar Day, Procedure Rate, Procedure Repetition, Unusual Modifier, and Missing Modifier. • All 6 analytics use yourdata. • However, the two dollar analytics currently make some statistical assumptions. • A very common example of a statistical assumption is to assume that data follows a “normal distribution” (see below) since normal distributions have nice mathematical properties.

18. Current IFM Approach: High Dollar Procedure • Points that fall inside the purple circle will currently receive a high score. • Example: The “normal distribution” assumption is in red. • However, sometimes these are just contracted fee schedules for a subset of providers (i.e., false positives). Average $ for procedure code # claim lines $ for procedure code

19. New Solution: “Peak and Valley” Detection • NO STATISTICAL ASSUMPTIONS • We make a smooth curve out of the histogram. • We identify peaks and valleys and flag the high dollar valleys as outliers. Average $ for procedure code Valley not flagged as outliers Valleys flagged as outliers # claim lines $ for procedure code

20. Results • We have tested this new algorithm on the High Dollar Procedure analytic. • 28% more fraud detected • 17.5% increase in savings

21. Our Analytics Team in San Diego and Bangalore Supriti Singh Nitin Basant Jeremy Greene Jessy Su Robin Snehal Katre Vivek Bhardwaj Himanshu Jain

22. FICO® Insurance Fraud Manager User Group: San Diego, CA | May 7--8, 2014 Thank You Supriti Singh858-382-6370supritisingh@fico.com