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Author : Marco Aleandri
Publisher :
ISBN 13 : 9788825528657
Total Pages : 276 pages
Book Rating : 4.55/5 ( download)
Download or read book Data Science and Machine Learning in Insurance. A Gentle Introduction for Actuaries written by Marco Aleandri and published by . This book was released on 2019 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michel Denuit
Publisher : Springer Nature
ISBN 13 : 3030258270
Total Pages : 250 pages
Book Rating : 4.76/5 ( download)
Download or read book Effective Statistical Learning Methods for Actuaries III written by Michel Denuit and published by Springer Nature. This book was released on 2019-10-31 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third 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 : Jens Perch Nielsen
Publisher : MDPI
ISBN 13 : 3039364472
Total Pages : 260 pages
Book Rating : 4.73/5 ( download)
Download or read book Machine Learning in Insurance written by Jens Perch Nielsen and published by MDPI. This book was released on 2020-12-02 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Michel Denuit
Publisher : Springer Nature
ISBN 13 : 303057556X
Total Pages : 228 pages
Book Rating : 4.64/5 ( download)
Download or read book Effective Statistical Learning Methods for Actuaries II written by Michel Denuit and published by Springer Nature. This book was released on 2020-11-16 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. 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 numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second 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.
Author :
Publisher :
ISBN 13 : 9782717871371
Total Pages : 403 pages
Book Rating : 4.73/5 ( download)
Download or read book Insurance Data Analytics written by and published by . This book was released on 2020-09-04 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jens Perch Nielsen
Publisher :
ISBN 13 : 9783039364480
Total Pages : 260 pages
Book Rating : 4.80/5 ( download)
Download or read book Machine Learning in Insurance written by Jens Perch Nielsen and published by . This book was released on 2020 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Arthur Charpentier
Publisher : Springer
ISBN 13 : 9783031497827
Total Pages : 0 pages
Book Rating : 4.21/5 ( download)
Download or read book Insurance, Biases, Discrimination and Fairness written by Arthur Charpentier and published by Springer. This book was released on 2024-02-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an introduction to the technical foundations of discrimination and equity issues in insurance models, catering to undergraduates, postgraduates, and practitioners. It is a self-contained resource, accessible to those with a basic understanding of probability and statistics. Designed as both a reference guide and a means to develop fairer models, the book acknowledges the complexity and ambiguity surrounding the question of discrimination in insurance. In insurance, proposing differentiated premiums that accurately reflect policyholders' true risk—termed "actuarial fairness" or "legitimate discrimination"—is economically and ethically motivated. However, such segmentation can appear discriminatory from a legal perspective. By intertwining real-life examples with academic models, the book incorporates diverse perspectives from philosophy, social sciences, economics, mathematics, and computer science. Although discrimination has long been a subject of inquiry in economics and philosophy, it has gained renewed prominence in the context of "big data," with an abundance of proxy variables capturing sensitive attributes, and "artificial intelligence" or specifically "machine learning" techniques, which often involve less interpretable black box algorithms. The book distinguishes between models and data to enhance our comprehension of why a model may appear unfair. It reminds us that while a model may not be inherently good or bad, it is never neutral and often represents a formalization of a world seen through potentially biased data. Furthermore, the book equips actuaries with technical tools to quantify and mitigate potential discrimination, featuring dedicated chapters that delve into these methods.
Author : Yongzhao Chen
Publisher : Wiley
ISBN 13 : 9781119863373
Total Pages : 512 pages
Book Rating : 4.76/5 ( download)
Download or read book Financial Data Analytics with Machine Learning, Optimization and Statistics written by Yongzhao Chen and published by Wiley. This book was released on 2023-06-06 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
Author : Andreas Hinterhuber
Publisher : Edward Elgar Publishing
ISBN 13 : 1035307316
Total Pages : 373 pages
Book Rating : 4.19/5 ( download)
Download or read book Elgar Encyclopedia of Pricing written by Andreas Hinterhuber and published by Edward Elgar Publishing. This book was released on 2024-07-05 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Elgar Encyclopedia of Pricing presents a holistic view of cutting-edge topics, practical insights, and global perspectives on pricing. In-depth entries cover everything from behavioral pricing and artificial intelligence to sustainability pricing strategies and dynamic online pricing.
Author : Mark Goldburd
Publisher :
ISBN 13 : 9780996889728
Total Pages : 106 pages
Book Rating : 4.28/5 ( download)
Download or read book Generalized Linear Models for Insurance Rating written by Mark Goldburd and published by . This book was released on 2016-06-08 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:
