0 Cool Analytics for the Insurance Industry Geetanjali Chakraborty Advanced Analytics & Predictive Modeling Practice Deloitte Consulting IASA Conference November 21, 2014 Agenda Analytics is all around us… What is analytics? How analytics is being used in insurance? Lifestyle Based Analytics So many have already done it… The savings potential Questions Copyright © 2014 Deloitte Development LLC. All rights reserved. Analytics is all around us… Analytics is all around us… Copyright © 2014 Deloitte Development LLC. All rights reserved. Why is analytics such a hot topic? “…Perhaps the most important cultural trend today: the explosion of data about every aspect of our world and the rise of applied math gurus who know how to use it.“ “…the world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly.” The increase in digital footprint, the rise of cheap computing power and digital storage, and the seamless integration of networks are allowing the accumulation of huge amounts of data. Data is information about the past. Analytics can make it about the present and the future. Knowledge and insights about the future can drive significant business value Copyright © 2014 Deloitte Development LLC. All rights reserved. Analytics in the insurance industry Insurance is a data rich industry and has long mined its data to improve pricing and underwriting activities Copyright © 2014 Deloitte Development LLC. All rights reserved. Predictive Analytics in Insurance Pricing • Tiering, schedule plan • Class plan optimization and optimal scheduled credits/debits Traditional Applications • Enhanced underwriting decision making Underwriting • Risk selection, retention strategies, automated underwriting • Resource allocation, straight-through processing Customer Service • Queue Prioritization • Service Offerings • Resource Allocation • Targeted Lead Generation Marketing and Agency Management Claims Emerging Trends • Cross-Selling Potential • Agency/Agent Management, Training, Servicing • Automated Processing and Triage • Fraud/Salvage/Subrogation Potential Management • Duration Improvement and Litigation management Copyright © 2014 Deloitte Development LLC. All rights reserved. What is analytics? What do you think of when you hear “Analytics”? Statistics Numbers Data and Computers Business Intelligence Analysis of data Analytics is the discovery and communication of meaningful patterns in data; relying on the simultaneous application of statistics, computer programming and operations research to quantify insights. Analytics imply a wide range of possibilities in its definition, its business application, and its delivery. Copyright © 2014 Deloitte Development LLC. All rights reserved. Basic Ingredients of Analytics Data Technologies Synthetic Data Intelligence Internal External Data Data Basic ingredients of analytics include Data that contains insights, intelligence to extract those insights and act on them and Technologies to implement appropriate actions. Copyright © 2014 Deloitte Development LLC. All rights reserved. Example of Data Sources – P&C Policyholder Info Customer Data Coverage Information Experience Data Policy Records Product Coverage Policyholders Correspondence Options 3rd Party Databases Business Credit Personal Credit Insured CLUE / MVR / ISO CIB Loss Control Data Check Cashing Weather 3rd Party Billing Data Sub-Prime Lending Agency Data Credit Bureaus Information Real Estate Billing / Payment Hist Customer Marketing and Sales Accepted Applications Rejected Applications Billing Data Geographic / Geocode Demographic Data Psychographic Claims Data Policy Claims Data Coverage Information Information Losses and Frequency Bureau Data Sources Consumer / Lifestyle Timing / Patterns Medical and Pharmacy Jurisdiction Behavioral Weather Claimant information Injury/Diagnosis Treatment patterns Settlement data Claims Notes Medical Billing Data Legal Bill Data 10 Heat / Cold Extremes Precipitation Extremes Hail Wind / Storms Event Extremes Agency Information Retention Litigation Marketing / Sales Recruiting Profitability Campaign, Promotion Adjusted Premium Ratio Cust Response Scores New Business Volume Cust Segmentation Continuing Education Copyright © 2014 Deloitte Development LLC. All rights reserved. Examples of Internal Predictive Variables Insights can be revealed through both traditional and non-traditional risk characteristics. 100% Relative claim severity 80% Claimant Age 60% 40% 20% 0% -20% -40% -60% -80% < 25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 20 to 25 25 to 30 65+ Relative claim severity 40% 30% Distance: Claimant Home and Employer 20% 10% 0% -10% -20% -30% -40% <1 1 to 3 3 to 5 5 to 7 7 to 10 10 to 15 15 to 20 30+ Copyright © 2014 Deloitte Development LLC. All rights reserved. Examples of External Predictive Variables Public domain data on the financial condition of the parties involved in the claim can provide new insights into loss and expense severities. Percentage of Sports Ultility Owners External public database variables provide new insights. Even variables based on the claimant’s address have proven predictive. Loss and Expense Relativity 50% 40% 30% 20% 10% 0% -10% Low High -20% -30% Percentage of Population with High School or Less Education Loss and Expense Relativity 30% 20% The populations with lower 10% education levels were over 20% 0% Low -10% High higher in terms of loss and expense severity. -20% -30% Copyright © 2014 Deloitte Development LLC. All rights reserved. Representation of a Claims Multivariate Model Model Inputs Several hundred internal and external variables are tested to identify the 50 100 with greatest predictive power Predictive modeling combines and converts available internal and external claim characteristics into a score with corresponding reason messages. In workers’ compensation, output may also be “normalized” by injury group to better understand high severity claims relative to those with similar diagnoses. Claim Segmentation Curve Sample Model Equation w1(Claimant Age) + w2(Dist_H_W) w5(CoMorbidity) + w6(Report_lag) +…. Outcomes +w3(Emerg_ Rm) + w4(Occupation) + Model Outputs 92 John Smith 1 Circle Ave. Anytown, NY Reason Messages: • Multiple co-morbidities • Claim history • Employment characteristics • Distance from work High Low Claim Complexity Copyright © 2014 Deloitte Development LLC. All rights reserved. Social Media Data Every claim investigation typically starts with a visit to social networking websites such as Facebook and Twitter to assess the validity of the claim. Copyright © 2014 Deloitte Development LLC. All rights reserved. Analytics on the Cloud Cloud computing is used by top insurance carriers to manager their claims better and faster. Copyright © 2014 Deloitte Development LLC. All rights reserved. Lifestyle Based Analytics Lifestyle Based Analytics (LBA) Traditional data is augmented with non-traditional data to create stronger correlations to the target Traditional internal data sources Applicant data Non-traditional external individual or household level data sources Consumer data EASI census Customized analysis Customer data Comorbidity data Historical Claims Marketing Marketing and and Sales Sales •Movement beyond traditional “likely to buy” •Movement beyond traditional “likely to buy” models models •Improvement in morbidity by selling only to best risks •Improvement in morbidity by selling only to and data cleansing Evaluate and Develop models Score individual Non-traditional data sources unlock new insights into employee populations •More accurately price products in situations •More you accurately products in situations where have noprice or limited medical where you have no or limited medical experience experience Data aggregation predictive Household data Pricing Pricing Predictive Analytics create variables Benchmark data Business value profiles Efficiency Innovative data sources best risks Customer Customer Retention Retention •Identify compounding components of at risk •Identify compounding components of at risk customers customers •Develop, deploy data driven intervention strategies •Develop, deploy data driven intervention strategies Medical Management / Wellness •Improved targeting of health events within a population; based on predicting propensity of having a certain clinical condition •Deeper understanding of the current & potential risks of the customers •Understand the behaviors creating the risks and monitor and develop behavior related strategies to change customers risks Lifestyle base analytics can be used to add efficiency across critical business areas Copyright © 2014 Deloitte Development LLC. All rights reserved. LBA provides Better Answers To Difficult Questions Lifestyle Based Analytics (LBA) can be used to better understand the member and prospect populations Retention: • Which members of a relatively unknown population should we target for retention? Managing Health Risk: • Which members will likely be afflicted with a specific disease? Acquisition: • Which consumers are most likely to buy? Efficiency: • Who are the best candidates to target with a specific product? • Which members are most likely to comply with health engagement programs? Future Health Risks: • What are the future health risks for members with unknown claims data? • Which members have a higher probability of having positive outcomes from medical management programs? • Which groups would it make sense to offer wellness initiatives to? Health Plans, using a new generation of lifestyle-based analytical models, may be able to predict the likelihood of significant life events with more accuracy than ever before, and it starts with something as simple as a name and an associated address Copyright © 2014 Deloitte Development LLC. All rights reserved. LBA and Improved Morbidity Risk Evaluation Lifestyle-based analytics (“LBA”) focuses on identifying increased morbidity and mortality risks for “lifestyle” based diseases. According to the US Surgeon General, lifestyle based diseases account for over 70% of US of healthcare expenses and subsequent deaths. Lorenz Curve for Neoplasm Female Sample Examples of lifestyle-based diseases include: diabetes, cardiovascular, cancer, and respiratory. This chart demonstrates LBA’s ability to identify future cancer claims in a healthy female population. The blue arrow points to LBA’s ability to predict future cancer claims in this same population. In this case, 20% of LBA’s highest risk members accounted for over 60% of the future cancer claims. The red arrow points to traditional underwritings ability to predict cancer claims in this healthy population. In this case, 20% of the highest risk members accounted for 30% of the future cancer claims. The black arrow points to a random distribution. In this case, 20% of the people will have 20% of the future cancer claims. Copyright © 2014 Deloitte Development LLC. All rights reserved. Result for Claims Cost Average Claims Relativity 80% Actual Claims Relativity 60% 40% 20% 0% 1 2 3 4 5 6 7 8 9 10 -20% Members with the worst algorithm scores experienced actual claims 60% higher than average -40% -60% -80% Predicted Claims Decile Observations Algorithm was constructed using a 40/30/30 train/test/validate methodology Lift above demonstrated on blind validation after final algorithm was chosen Age/Gender correction made (neutral) Individual variables: ‒ Selected disease state algorithms (both binary and cost-weighted) ‒ Selected 3rd party ailment indicators ‒ Selected individual characteristics Copyright © 2014 Deloitte Development LLC. All rights reserved. Data Visualization Front End Tools We have the ability to display modeling results in graphic, front-end tools that allow users to select different dimensions for additional analyses. The exhibit below depicts member risk levels for Cardiovascular Disease for a sample of individuals in the greater New Jersey area. MEMBERS Copyright © 2014 Deloitte Development LLC. All rights reserved. Front End Tools (continued) The exhibit below shows the trend of policies and premiums across 10 buckets grouped by high to low loss ratio for Auto insurance renewal business. Copyright © 2014 Deloitte Development LLC. All rights reserved. So many have already done it… What Underwriters & Claims Executives are Saying Science, enabled by technology, now plays an integral role in our value proposition. Pricing risks and establishing optimal claim outcomes for our Insureds are being aided by sophisticated analytics such as predictive modeling Our claim scoring models review new claim notices daily to identify red flags and suspicious claims for investigation. … better outcomes, through enhanced automation from first notice of loss to claims resolution …first Workers’ Compensation Company to apply advanced analytics to claims Copyright © 2014 Deloitte Development LLC. All rights reserved. Who’s Attending Predictive Modeling Seminars? • Insurance Company of the West (ICW) • Utica National • Companion Property & Casualty • WR Berkley • ACE Ltd. • Sentry • Acuity • NJM • XL • Auto Owners • Westfield Group • Main Street America • Grange Mutual • RLI • Louisiana Workers Compensation Corp • Unitrin • Fireman’s Fund • AFICA • California State Fund • Plymouth Rock • Secura • Beazley Group • Allstate • American Family • State Farm • Meadowbrook • QBE • Nationwide • Farmers • Church Mutual Copyright © 2014 Deloitte Development LLC. All rights reserved. The savings potential Benefits Realized Deloitte successfully designed and implemented Workers’ Compensation claim severity predictive model into multiple clients’ claims operations to help injured workers return to work sooner. Claim Routing & Assignment Fraud Detection Right claim, right resource Reduce lag time of SIU referrals Improve routing to auto-adjudication Improve mix of claims referred to SIU Increase triage consistency through automation Deterrence of “soft-fraud” Projected Business Impact 4-8% reduction in loss and expense 5-10% improvement in SIU managed claims 3-7% improvement in nurse managed claims 20-25% redeployment of supervisory resources Medical Management Prompt assignment of nurses on those cases that need it most Integrate behavior issues into nurse assignment Cost effective use of field case management Top Line Growth Demonstrated ability to close claims faster and cheaper leads to competitive market advantage Improved client satisfaction strengthens the relationship and brand Copyright © 2014 Deloitte Development LLC. All rights reserved. Benefits Case – Calculation & Allocation Tool Once broad benefit target areas, amounts and associated metrics are defined, we use our Benefits Calculation Tool to provide a highly tailored and approach/framework to refine, allocate and aggregate benefits. Illustrative Benefits Calculations Copyright © 2014 Deloitte Development LLC. All rights reserved. Questions? Questions 31 Copyright © 2014 Deloitte Development LLC. All rights reserved. Biography Geetanjali Chakraborty Email : gechakraborty@deloitte.com Tel (US) : +1 617 437 2393 Geet specializes in the development and application of predictive analytics and business intelligence for the financial services and insurance industries. With a background in mathematics, Geet has worked with many Fortune 500 companies to leverage data analytics and technology to contain costs and gain operational efficiencies. She has lead various analytics teams through the end-to-end process of model design, build, and implementation, and co-develop Deloitte’s Advanced Analytics solutions for Healthcare Insurance. She has publications in Claims P&C magazine, Claims 360 degree magazine and through IIMA Analytics conference 32 Copyright © 2014 Deloitte Development LLC. All rights reserved. End of Presentation 33 © 2009 Deloitte Development LLC