Business Intelligence & Data Analytics to Improve JPA Performance

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1 Business Intelligence & Data Analytics to Improve JPA Performance David Tweedy Senior Consultant, Bickmore Jose Tribuzio CEO, Systema Software Rick Krepelka Director of Finance & Technology Golden State Risk Management Authority Thurs, Sept 12, 1:45 pm 3:00 pm

2 Agenda Overview of Business Intelligence (BI) & Data Analytics Definition of Terms, Trends, and JPA Relevance Demo of BI Tools Data cubes, dynamic business reporting, data mining, and predictive modeling Real-life BI Implementation Practical Examples, Tips, and Lessons Learned Value and Benefits

3 Overview of Business Intelligence & Data Analytics David Tweedy Senior Consultant, Bickmore

4 What is Business Intelligence? Business Intelligence (BI): a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes

5 What is Data Analytics? Data Analytics (DA): the discovery and communication of meaningful patterns in data. Organizations commonly apply analytics to business data, to describe, predict, and improve business performance Predictive Modeling: the process by which a model is created or chosen to try to best predict the probability of an outcome

6 Definition of Related Terms Data Mining Native Data Structured vs. Unstructured Data Dirty Data Big Data Dashboard Key Performance Indicators (KPIs)

7 Importance to JPAs Provides factual evidence of what is actually happening in your claims operations Identifies problems, trends and opportunities Enhances better decision making

8 You don t know what you don t know Discovery Trends Pa*erns Rela-onships Anomalies In all e- data

9 Claims Processing Improvements Quickly identify claims that have no suspicious or fraudulent identifiers Move claims that require additional review quickly into secondary review Identify claims that are likely fraudulent and forward to legal for handling Increases in subrogation recovery often in the double-digit range

10 Some Available Options Off-the-shelf Software Discuss examples Vendor-assisted Software Consulting organizations RMIS vendors Insurers / TPAs

11 Overview of Benefits Manage and analyze all data in a powerful new way: Dramatically reduce IT costs Operate easily with legacy systems Put the power in the user s hands Include all unstructured and dirty data Revolutionize collaboration

12 Demo of BI Tools Jose Tribuzio CEO, Systema Software

13 Pivot Viewer

14 Excel Pivot Table

15 Powerview: Injuries by Body Part

16 Data Mining Decision Tree Model I

17 Data Mining Decision Tree Model II

18 Data Mining Neural Network

19 Dependency Network

20 Real-life BI Implementation Rick Krepelka Director of Finance & Technology Golden State Risk Management Authority

21 About Golden State RMA Member agencies include counties, cities, school districts, cemetery districts, fire agencies and other special districts and public agencies Committed to providing members with the best coverage and services available at the most reasonable cost possible Innovative programs, personalized service As of July 2012, GSRMA was comprised of 230 member agencies located in 47 California Counties Members have a combined total of over 2,900 employees, 900 volunteers and payroll in excess of $100 million General liability, workers compensation, property and miscellaneous coverage, and employee benefits program Risk management services, includes safety and loss prevention training activities, Loss Prevention Subsidy Fund

22 Why Interested in BI and Analytics? Faster, easier access to data, reporting, and analysis Identify areas to target for risk management and loss prevention Analyze and improve coverage options Spot trends and patterns Make projections and forecasts using predictive models Determine cost-effectiveness of vendors Improve decision-making abilities

23 Case Study #1: 4850 Time Analysis 4850 Time refers to Labor Code 4850 Provides up to one year of leave of absence at full pay without tax deduction for police officers, firefighters, and other safety personnel when temporarily totally disabled due to an industrial injury Costs can be significant risk Quickly determine whether members would benefit from funding their own 4850 or insure that risk

24 Case Study #2: Analysis of Providers Selection of right provider network can help to control medical costs, a significant expense in workers compensation Analysis of providers used by injured workers; identify geographic clusters Allow GSRMA to better understand why provider network not delivering optimal savings Network did not have significant providers in member areas; members and their employees forced to go to out-of-network providers Based on this information decided to changed networks Use data cubes to measure level of savings achieved?

25 Case Study #3: Member Losses Typically provide members with stewardship reports and guidance on how to reduce losses Now with data cubes can perform more in-depth analysis of member losses Analyze by member type, e.g. by city, county, cemetery, fire, special district, schools districts, etc. Determine if trends exists, target with programs

26 What s Next? Incorporating other data (exposures such as IV, payroll, premium paid, training and loss prevention efforts, etc.) into our analytic tools to better understand member loss and risk profiles Guide new member acquisition Encourage members with high cost claims to improve; possibly utilize peer pressure to affect behavior and results

27 Panel Q&A David Tweedy Senior Consultant, Bickmore Jose Tribuzio CEO, Systema Software Rick Krepelka Director of Finance & Technology Golden State Risk Management Authority

White Paper. Leveraging BI for Improved Claims Performance and Results. Jose Tribuzio Founder & CEO Systema Software

White Paper. Leveraging BI for Improved Claims Performance and Results. Jose Tribuzio Founder & CEO Systema Software White Paper Leveraging BI for Improved Claims Performance and Results Jose Tribuzio Founder & CEO Systema Software June 2014 Contents Introduction 3 Current BI Landscape 3 Definition of Key BI Terms 4