Challenge. Solutions. Early results. Personal Lines Case Study Celina Insurance Reduces Expenses & Improves Processes Across the Business.

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1 Celina Insurance Reduces Expenses & Improves Processes Across the Business About Celina Insurance Group Founded in 1914, Celina Insurance Group is composed of four mutual property and casualty insurance companies. Celina underwrites auto, non-standard auto, farm, home, commercial auto and property insurance, as well as umbrella and casualty coverage in six Midwestern states. Celinainsurance.com Facing profitability issues, regional Property & Casualty carrier Celina Insurance Group s Chief Actuary couldn t help but notice the prevalence of predictive analytics use among his peers, especially at the national carriers with whom Celina competes. Celina determined that predictive analytics could provide solutions to their short-term expense reduction needs as well as their longer-term profitability challenges. In the process, they could achieve a cultural change to become more analytics-driven across the organization. s Celina embarked on a strategic vendor selection process, which is documented in the Celent Report, Celina Insurance's Predictive Analytics Initiative: The Machine Learning Factor. Summary Regional carrier needed to improve its ability to compete with national carriers in all lines. Early results: A homeowners and auto segmentation project generated new rates that were approved in all four personal lines states. An MVR expense reduction project delivered fast ROI and savings of more than double the targeted results. Additional applications: Risk scoring on 2 /3 of their book of business provides underwriting quality scores and pricing scores. An agency appointment score guide will help more strategically identify areas to appoint new agents. Celina initially engaged EagleEye to segment their Homeowners portfolio to identify profitable risk groups, as well as to address any inadequate pricing revealed by the segmentation. As Celina gained confidence using the system, they completed a Motor Vehicle Report (MVR) expense reduction project intended to help them master predictive modeling techniques and deliver quick ROI on the software investment. Early results Segmentation of Personal Auto & Homeowners Business: Celina s first business applications of the predictive analytics system were to their Homeowners and Personal Auto programs, which make up 63% of premium volume. Using company data, Celina identified segments of the business that were either over- or underpriced by the existing rating structure. Rating factors to address pricing deficiencies were put into effect in each states homeowners and standard and preferred personal auto programs. MVR Expense Reduction: Using the Talon machine learning predictive analytics system, Celina modeled the auto policies where Motor Vehicle Reports made a significant difference in underwriting outcome, identifying segments where the reports didn t add value. Celina targeted savings of approximately $10,000 per month by limiting MVR orders; their actual savings exceeded $22,000 per month.

2 The Marketing team s request to develop a guide to help find areas for profitable growth, then marry that data with agency appointment strategies, was a clear example of how the entire organization fully appreciates the power of advanced analytics to inform better decision making. Additional Predictive Analytics Applications Celina has continued to increase the sophistication of their predictive modeling techniques, and has integrated real-time risk scoring into the underwriting process for two-thirds of the total book of business. With the scoring implementation, new business and renewal underwriters see a frequency score and a pricing score for each risk. These provide invaluable information to measure the quality of business and improve underwriting accuracy. Celina is closely monitoring the results of these changes, and expects to be able to share quantified results in the near future. After seeing the positive impact that predictive analytics has had in pricing and underwriting, Celina s Marketing team also decided to integrate predictive analytics into their agency appointment strategy. A new model based on key performance indicators for five programs including auto, homeowners, and commercial business ranks the 4,200 zip codes in which Celina writes business. By targetting agency appointments in the zip codes with the highest performance indicators, Celina can pursue profitable growth opportunities in previously under-represented areas. Vince Franz, Chief Actuary, Celina Insurance Group Sample Data

3 Personal Lines Pricing Case Study Buckeye Insurance Group Achieves Sustainable Profitable Growth Buckeye s auto business was struggling to grow and remain profitable. They knew they needed to take rate action, but wanted to go beyond the traditional rate revision process. Buckeye wanted to take a leap forward with their pricing segmentation and sophistication, not just catch up to the market. Buckeye contacted their reinsurance broker, Guy Carpenter, in search of a predictive analytics solution. After evaluating several vendors, they chose EagleEye Analytics for both their Machine Learning software platform and actuarial consulting expertise. Case Study Summary 1. Auto profits were declining. 2. Buckeye wanted not just to update rating structure but to take a leap forward 3. EagleEye applied proprietary Machine Learning to the data. 4. Resulting new rate tables were approved in all of Buckeye s states. 5. One year after rate changes, written premium increased by more than 10% while loss ratio decreased by 4 points. 6. Buckeye returned to profitable growth and KPIs point to sustainable profitability. Buckeye and EagleEye partnered to create an innovative rating program. The new rate structure used standard rating factors derived with Generalized Linear Modeling (GLM), combined with new rating tables created by EagleEye s proprietary Machine Learning algorithms. About Buckeye Ohio-based Buckeye Insurance Group specializes in providing personalized insurance coverage to customers living in rural communities in several states. The GLM laid down an elementary base for Buckeye s rating structure and the Machine Learning discoveries took them to the next level by incorporating complex variable interactions. These variable interactions described fully-credible segments of loss characteristics and became new rating factors, allowing Buckeye to price and select risk more profitably. These risk patterns had always existed in their data, but machine learning technology made their discovery possible. EagleEye completed the data preparation and modeling in just three months. The Machine Learning analysis uncovered significant segments of underpriced business, and Buckeye chose to take targeted rate action. The new rating plan was launched after receiving Department of Insurance approval in each of their states. One year after implementation, Buckeye s written premium had increased by more than 10% while their loss ratio decreased by 4 points. Key Performance Indicators (KPIs) pointed to sustainable success: Top-line premium increased. New business conversion improved. Loss ratio decreased. Retention remained steady to slightly up. What s next? Buckeye is already expanding its relationship with EagleEye to implement new rating plans for other lines of business. In addition, they are deepening their machine learning sophistication with EagleEye Scoring. The expected result: Buckeye will move even further ahead of their competition in pricing accuracy, with granularity that allows for more accurate insight into individual risks and less rate volatility when policy characteristics shift.

4 Large Auto Carrier Adds 27% Lift to GLM Model The insurer, which is the largest domestic property and casualty insurance company in its state, decided to completely revise their rating plan and introduce it as a new program. In designing the new rating plan, all factors were considered: New base rates Rating factors Territory definitions and relativities The approach incorporated the strengths of both Talon GLM and Talon Complete Segmentation. Talon s GLM was used to build an initial model for the bulk of the class plan and to control for certain other fields (e.g. geographic effects). Residuals from this initial GLM were analyzed by Talon s Complete Segmentation to discover the compound variables undetected by the GLM framework. About EagleEye Analytics EagleEye Analytics offers the most advanced predictive analytics system available today. Developed by actuaries and data scientists with long careers in insurance, Talon insurance-data aware machine learning produces real-time scoring to inform decisions in claims management, pricing and underwriting, marketing and distribution. Given the new compound variables along with the initial GLM, Talon s Complete Segmentation was used again to explore geographic fields to determine new territory definitions. This project was extensive, involving territory definitions, base rates, and rating plan factors for five different coverages. The timeline spanned approximately six months using EagleEye s powerful software solution suite. The complete segmentation analysis added 27% lift to the GLM model, with an average correlation of 95.8%, indicating a very high degree of consistency on unseen validation data. The new rating plan was filed and approved by the state s Department of Insurance. With more accurate rates in place, the insurer is positioned for profitable growth. EagleEye Advantages Talon s GLM uniqueness and superior results derive from its integration with Talon s powerful Complete Segmentation capabilities. The combined solution streamlines and accelerates the process of building rating plans while providing greater accuracy than traditional GLMs.

5 Auto Carrier Uses Segmentation to Reduce the New Business Penalty A large Canadian Personal Auto insurer wanted to improve profitability on new business writings. EagleEye suggested using a new business quote filter that would gradually eliminate the most unprofitable risks from the insurer s portfolio. Using a complete segmentation on policy level data with the loss ratio as the objective, the expected profitability of any risk can be determined accurately. The insurer can achieve the desired profitability increase by filtering out the highest expected loss ratio segment. The data preparation and modeling exercise were completed within 90 days of data receipt. The quote filter was implemented by the insurer within two months of the final presentation, with the results then being monitored closely to ensure the filter was working properly. Four months after implementation, the proportion of highest loss ratio risks in the new business portfolio decreased from 8 points to 3 points, which translates into a 5 point improvement in expected loss ratio for new business. EagleEye Advantages By using machine learning methods, the model development process is much faster compared to traditional approaches, such as GLMs. Multiple iterations, using different allowable predictors and model complexities, can be built to account for implementation or regulatory constraints. The segments derived by Talon have clear, easy to understand descriptions, which guides acceptability of the model. About EagleEye Analytics EagleEye Analytics offers the most advanced predictive analytics system available today. Developed by actuaries and data scientists with long careers in insurance, Talon insurancedata aware machine learning produces real-time scoring to inform decisions in claims management, pricing and underwriting, marketing and distribution. The result: Profitable growth and competitive advantage. Visit eeanalytics.com for more info.