Predictive Modeling Applications in Actuarial Science: Volume 2, Case Studies in Insurance Case Studies in Insurance

Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. Predictive Modeling Applications in Actuarial Science emphasizes life-long learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.

1. Pure premium modeling using generalized linear models Ernesto Schirmacher; 2. Applying generalized linear models to insurance data - frequency-severity vs pure premium modeling Dan Tevet; 3. GLMs as predictive claim models Greg Taylor and James Sullivan; 4. Frameworks for general insurance ratemaking - beyond the generalized linear model Peng Shi and James Guszczaz; 5. Using multilevel modeling for group health insurance ratemaking - a case study from the Egyptian market Mona S. A. Hammad and Galal A. H. Harby; 6. Clustering in general insurance pricing Ji Yao; 7. Advanced unsupervised learning methods applied to insurance claims data Louise A. Francis; 8. The predictive distribution of loss reserve estimates over a finite time horizon Glenn Meyers; 9. Finite mixture model and workers compensation large loss regression analysis Luyang Fu and Xianfang Liu; 10. A framework for managing claim escalation using predictive modeling Mohamad A. Hindawi and Claudine H. Modlin; 11. Predictive modeling for usage-based auto insurance Udi Makov and Jim Weiss.

Edward W. (Jed) Frees is the Hickman-Larson Chair of Actuarial Science at the University of Wisconsin, Madison. He received his PhD in Mathematical Statistics in 1983 from the University of North Carolina, Chapel Hill and is a Fellow of both the Society of Actuaries (SoA) and the American Statistical Association (the only Fellow of both organizations). Regarding his research, Professor Frees has won several awards for the quality of his work, including the Halmstad Prize for best paper published in the actuarial literature (four times).
Glenn Meyers, FCAS, MAAA, CERA, and PhD, retired from ISO at the end of 2011 after a 37 year career as an actuary. He holds a BS in Mathematics and Physics from Alma College, Michigan, an MA in Mathematics from Oakland University, Michigan, and a PhD in Mathematics from the State University of New York, Albany. A frequent speaker at Casualty Actuarial Society (CAS) meetings, he has served and continues to serve the CAS and the International Actuarial Association on various research and education committees. He has also served on the CAS Board of Directors. He has several published articles in the Proceedings of the Casualty Actuarial Society, Variance and the Actuarial Review. His research contributions have been recognized by the CAS through his being a three-time winner of the Woodward–Fondiller Prize, a two-time winner of the Dorweiler Prize, the DFA Prize, the Reserves Prize, the Matthew Rodermund Service Award and the Michelbacher Significant Achievement Award. In retirement he still spends some of his time on his continuing passion for actuarial research.
Richard A. Derrig is founder and principal of OPAL Consulting LLC., a firm that provides research and regulatory support to property casualty insurance clients. Primary areas of expertise include financial pricing models, database and data mining design, fraud detection planning and implementation, and expert testimony for regulation and litigation purposes.