Innovation and Technology Emerging Data Analytics Techniques with Actuarial Applications July 2019 2 Emerging Data Analytics Techniques with Actuarial Applications MARIE-CLAIRE KOISSI PhD, Professor SPONSOR Actuarial Innovation & Technology Actuarial Science Program Steering Committee University of Wisconsin-Eau Claire HERSCHEL DAY FSA, MAAA, Associate Professor Actuarial Science Program University of Wisconsin-Eau Claire VICKI WHITLEDGE PhD, Professor Actuarial Science Program University of Wisconsin-Eau Claire Caveat and Disclaimer The opinions expressed and conclusions reached by the authors are their own and do not represent any official position or opinion of the Society of Actuaries or its members. The Society of Actuaries makes no representation or warranty to the accuracy of the information Copyright © 2019 by the Society of Actuaries. All rights reserved. Copyright © 2019 Society of Actuaries 3 CONTENTS Abstract .................................................................................................................................................................... 4 Executive Summary .................................................................................................................................................. 4 Section 1: Acknowledgments .................................................................................................................................... 5 Section 2: Introduction ............................................................................................................................................. 6 2.1 DATA ANALYTICS FRAMEWORK .......................................................................................................................... 6 2.2 DATA SOURCES .................................................................................................................................................... 8 2.3 DATA EXPLORATION AND VISUALIZATION ......................................................................................................... 9 Section 3: Data Analytics Techniques ...................................................................................................................... 10 3.1 SUPERVISED LEARNING ..................................................................................................................................... 10 3.1.1 REGRESSION AND GENERALIZED LINEAR MODELS (GLMS) ............................................................ 10 3.1.2 TREES ................................................................................................................................................. 11 3.1.3 NEURAL NETWORKS ......................................................................................................................... 13 3.1.4 PREDICTIVE MODELING .................................................................................................................... 14 3.2 UNSUPERVISED TECHNIQUES ........................................................................................................................... 14 3.2.1 PRINCIPAL COMPONENT ANALYSIS ................................................................................................. 14 3.2.2 CLUSTER ANALYSIS ............................................................................................................................ 14 3.2.3 GENETIC ALGORITHMS ..................................................................................................................... 15 3.2.4 NEURAL NETWORKS ......................................................................................................................... 16 3.3 OTHER DATA ANALYTICS TECHNIQUES ............................................................................................................ 16 3.3.1 MARKOV CHAIN MONTE CARLO (MCMC) SIMULATION ................................................................. 16 3.3.2 BAYESIAN ANALYSIS .......................................................................................................................... 17 Section 4: Emerging Data Analytic Technologies ..................................................................................................... 18 4.1 LIFE: MACHINE LEARNING TECHNOLOGIES FOR MORTALITY RATE FORECASTING ....................................... 18 4.2 HEALTH CARE: MACHINE LEARNING TECHNOLOGIES FOR HEALTH CARE CLAIMS MODELING .................... 18 4.3 LIFE / NON-LIFE: MACHINE LEARNING TECHNOLOGIES FOR RESERVES......................................................... 19 4.4 NON-LIFE: MACHINE LEARNING TECHNOLOGIES FOR CLAIM MODELING ..................................................... 20 4.5 LIFE / NON-LIFE: MACHINE LEARNING TECHNOLOGIES FOR INSURANCE FRAUD AND OTHER AREAS............................................................................................................................................................................ 20 4.6 SOME ACTUARIAL PACKAGES IN R AND PYTHON ............................................................................................ 21 4.6.1 SOME ACTUARIAL PACKAGES IN R ................................................................................................... 21 4.6.2 SOME PACKAGES IN PYTHON WITH ACTUARIAL APPLICATIONS .................................................... 22 Section 5: Case Studies ........................................................................................................................................... 24 5.1 CASE STUDY 1: CHAINLADDER IN R .................................................................................................................. 24 5.2 CASE STUDY 2: CLAIMS FREQUENCY IN MOTOR INSURANCE ......................................................................... 32 5.3 CASE STUDY 3: MORTALITY (LIFE INSURANCE) ................................................................................................ 37 Section 6: Conclusion .............................................................................................................................................. 43 References .............................................................................................................................................................. 44 Appendices ............................................................................................................................................................. 53 A. Appendix A: R-Code for Case Study 1 ............................................................................................................... 53 B. Appendix B: R-Code for Case Study 2 ............................................................................................................... 54 C. Appendix C: R-Code for Case Study 3 ............................................................................................................... 57 About The Society of Actuaries ............................................................................................................................... 60 Copyright © 2019 Society of Actuaries 4 Emerging Data Analytics Techniques with Actuarial Applications Abstract Data analytics strongly rely on data and available computing tools. Recent years have seen an increase in data availability and volume. Advanced computational methods and machine-learning tools have been developed to handle this continuous flow of valuable information. The aim of this research is to survey emerging data analytics techniques and discuss their evolution and growing use in the actuarial profession. Data analytics’ applications in life and non-life insurance will also be provided. Executive Summary Data analytics involves a set of tools and techniques used to extract meaningful information from a dataset (SOA, 2012). It encompasses several disciplines such as actuarial science, statistics, computer science, mathematics, and marketing. Recent years have seen an increase in data availability and volume, leading to an explosion in the concept of “Big Data” (AAA, 2018). Actuaries rely heavily on data to perform analysis, make general inferences, inform decisions, and guide predictions. They have a long history in conducting data analysis in areas such as underwriting, claim management, pricing, risk analysis, and auditing (Shapiro and Jain, 2003; SOA, 2012). In the past, data analysis was mainly descriptive and actuaries predominantly used programs such as Excel (SOA, 2012, Appendix G) and C++ (Pauza and Bellomo, 2014). Although descriptive analytics is in use today, it now represents an initial step in a more complex and data-driven analysis. Recent studies predict substantial changes in the analytical tools used by actuaries and other professionals (Sondergeld and Purushotham, 2019; Guo, 2003; Wedel and Kannan, 2016). Advanced data analytics packages (such as SAS, SPSS, Matlab, R, and Python) allow the user to extract more information from a dataset, make a diagnostic analysis, and use non-standard models to make relevant predictions. This paper aims at surveying emerging data analytics techniques with potential actuarial applications. The remaining part of the paper is organized as follows: Section 1 acknowledges the contributions of this report’s Project Oversight Group (POG). Section 2 deals with the change in data source and volume. This section also reviews some of the data visualization techniques available to actuaries. In Section 3, we give a brief overview of several data analytic techniques. In Section 4, we review