Actuarial Models For Understanding Driver Behavior With Telematics Data
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| Author | : Robert N. Rodriguez |
| Publisher | : SAS Institute |
| Total Pages | : 464 |
| Release | : 2023-04-18 |
| Genre | : Computers |
| ISBN | : 1951684001 |
Advance your skills in building predictive models with SAS! Building Regression Models with SAS: A Guide for Data Scientists teaches data scientists, statisticians, and other analysts who use SAS to train regression models for prediction with large, complex data. Each chapter focuses on a particular model and includes a high-level overview, followed by basic concepts, essential syntax, and examples using new procedures in both SAS/STAT and SAS Viya. By emphasizing introductory examples and interpretation of output, this book provides readers with a clear understanding of how to build the following types of models: general linear models quantile regression models logistic regression models generalized linear models generalized additive models proportional hazards regression models tree models models based on multivariate adaptive regression splines Building Regression Models with SAS is an essential guide to learning about a variety of models that provide interpretability as well as predictive performance.
| Author | : Edward W. Frees |
| Publisher | : Cambridge University Press |
| Total Pages | : 337 |
| Release | : 2016-07-27 |
| Genre | : Business & Economics |
| ISBN | : 1107029880 |
This second volume examines practical real-life applications of predictive modeling to forecast future events with an emphasis on insurance.
| Author | : Edward W. Frees |
| Publisher | : Cambridge University Press |
| Total Pages | : 337 |
| Release | : 2016-07-27 |
| Genre | : Business & Economics |
| ISBN | : 1316720527 |
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.
| Author | : Stuart A. Klugman |
| Publisher | : John Wiley & Sons |
| Total Pages | : 758 |
| Release | : 2012-01-25 |
| Genre | : Business & Economics |
| ISBN | : 0470391332 |
An update of one of the most trusted books on constructing and analyzing actuarial models Written by three renowned authorities in the actuarial field, Loss Models, Third Edition upholds the reputation for excellence that has made this book required reading for the Society of Actuaries (SOA) and Casualty Actuarial Society (CAS) qualification examinations. This update serves as a complete presentation of statistical methods for measuring risk and building models to measure loss in real-world events. This book maintains an approach to modeling and forecasting that utilizes tools related to risk theory, loss distributions, and survival models. Random variables, basic distributional quantities, the recursive method, and techniques for classifying and creating distributions are also discussed. Both parametric and non-parametric estimation methods are thoroughly covered along with advice for choosing an appropriate model. Features of the Third Edition include: Extended discussion of risk management and risk measures, including Tail-Value-at-Risk (TVaR) New sections on extreme value distributions and their estimation Inclusion of homogeneous, nonhomogeneous, and mixed Poisson processes Expanded coverage of copula models and their estimation Additional treatment of methods for constructing confidence regions when there is more than one parameter The book continues to distinguish itself by providing over 400 exercises that have appeared on previous SOA and CAS examinations. Intriguing examples from the fields of insurance and business are discussed throughout, and all data sets are available on the book's FTP site, along with programs that assist with conducting loss model analysis. Loss Models, Third Edition is an essential resource for students and aspiring actuaries who are preparing to take the SOA and CAS preliminary examinations. It is also a must-have reference for professional actuaries, graduate students in the actuarial field, and anyone who works with loss and risk models in their everyday work. To explore our additional offerings in actuarial exam preparation visit www.wiley.com/go/actuarialexamprep.
| Author | : Michel Denuit |
| Publisher | : Springer Nature |
| Total Pages | : 452 |
| Release | : 2019-09-03 |
| Genre | : Business & Economics |
| ISBN | : 3030258203 |
This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
| Author | : Michel Denuit |
| Publisher | : John Wiley & Sons |
| Total Pages | : 384 |
| Release | : 2007-07-27 |
| Genre | : Mathematics |
| ISBN | : 9780470517413 |
There are a wide range of variables for actuaries to consider when calculating a motorist’s insurance premium, such as age, gender and type of vehicle. Further to these factors, motorists’ rates are subject to experience rating systems, including credibility mechanisms and Bonus Malus systems (BMSs). Actuarial Modelling of Claim Counts presents a comprehensive treatment of the various experience rating systems and their relationships with risk classification. The authors summarize the most recent developments in the field, presenting ratemaking systems, whilst taking into account exogenous information. The text: Offers the first self-contained, practical approach to a priori and a posteriori ratemaking in motor insurance. Discusses the issues of claim frequency and claim severity, multi-event systems, and the combinations of deductibles and BMSs. Introduces recent developments in actuarial science and exploits the generalised linear model and generalised linear mixed model to achieve risk classification. Presents credibility mechanisms as refinements of commercial BMSs. Provides practical applications with real data sets processed with SAS software. Actuarial Modelling of Claim Counts is essential reading for students in actuarial science, as well as practicing and academic actuaries. It is also ideally suited for professionals involved in the insurance industry, applied mathematicians, quantitative economists, financial engineers and statisticians.
| Author | : Pierpaolo Marano |
| Publisher | : Springer Nature |
| Total Pages | : 439 |
| Release | : 2021 |
| Genre | : Bank marketing |
| ISBN | : 3030527387 |
This open access volume of the AIDA Europe Research Series on Insurance Law and Regulation offers the first comprehensive legal and regulatory analysis of the Insurance Distribution Directive (IDD). The IDD came into force on 1 October 2018 and regulates the distribution of insurance products in the EU. The book examines the main changes accompanying the IDD and analyses its impact on insurance distributors, i.e., insurance intermediaries and insurance undertakings, as well as the market. Drawing on interrelations between the rules of the Directive and other fields that are relevant to the distribution of insurance products, it explores various topics related to the interpretation of the IDD - e.g. the harmonization achieved under it; its role as a benchmark for national legislators; and its interplay with other regulations and sciences - while also providing an empirical analysis of the standardised pre-contractual information document. Accordingly, the book offers a wealth of valuable insights for academics, regulators, practitioners and students who are interested in issues concerning insurance distribution.--
| Author | : Christopher D Armstrong |
| Publisher | : SAE International |
| Total Pages | : 316 |
| Release | : 2018-11-02 |
| Genre | : Technology & Engineering |
| ISBN | : 0768092477 |
The last ten years have seen explosive growth in the technology available to the collision analyst, changing the way reconstruction is practiced in fundamental ways. The greatest technological advances for the crash reconstruction community have come in the realms of photogrammetry and digital media analysis. The widespread use of scanning technology has facilitated the implementation of powerful new tools to digitize forensic data, create 3D models and visualize and analyze crash vehicles and environments. The introduction of unmanned aerial systems and standardization of crash data recorders to the crash reconstruction community have enhanced the ability of a crash analyst to visualize and model the components of a crash reconstruction. Because of the technological changes occurring in the industry, many SAE papers have been written to address the validation and use of new tools for collision reconstruction. Collision Reconstruction Methodologies Volumes 1-12 bring together seminal SAE technical papers surrounding advancements in the crash reconstruction field. Topics featured in the series include: • Night Vision Study and Photogrammetry • Vehicle Event Data Recorders • Motorcycle, Heavy Vehicle, Bicycle and Pedestrian Accident Reconstruction The goal is to provide the latest technologies and methodologies being introduced into collision reconstruction - appealing to crash analysts, consultants and safety engineers alike.
| Author | : David Shinar |
| Publisher | : Emerald Group Publishing |
| Total Pages | : 1262 |
| Release | : 2017-06-22 |
| Genre | : Transportation |
| ISBN | : 1786352214 |
This comprehensive 2nd edition covers the key issues that relate human behavior to traffic safety. In particular it covers the increasing roles that pedestrians and cyclists have in the traffic system; the role of infotainment in driver distraction; and the increasing role of driver assistance systems in changing the driver-vehicle interaction.
| Author | : Jens Perch Nielsen |
| Publisher | : MDPI |
| Total Pages | : 260 |
| Release | : 2020-12-02 |
| Genre | : Business & Economics |
| ISBN | : 3039364472 |
Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.
