Artificial Intelligence And Actuarial Science
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Artificial Intelligence And Actuarial Science
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Author : Sonal Trivedi
language : en
Publisher: C&h/CRC Press
Release Date : 2024-12
Artificial Intelligence And Actuarial Science written by Sonal Trivedi and has been published by C&h/CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12 with Business & Economics categories.
"This book aims to explore how to automate, innovate, design, and deploy emerging technologies in Actuarial work transformations for the insurance and finance sector. It examines the role of artificial intelligence with process automation in daily monitoring solvency, governance, compliance, data processes, etc. It also explores the usage of Machine learning, telematics system, AI- AI-enabled claim processing software, Big Data and Algorithms, Explainable AI, AI-enabled risk management tools in various actuarial processes. Presents case studies and best practices with real-world examples of successful and unsuccessful actuarial work transformation initiatives and transformation with emerging technologies Offers deployment solutions for different applications of AI in actuarial work Discusses how organizations can effectively incorporate AI into their current practices of Actuarial work Covers diverse emerging technologies, practices and processes of actuaries from around the globe Elaborates upon a framework for comprehending how big data and AI developments may affect insurance offers and their supervision Explains how insurance companies may review and modify their current Risk Management Framework (RMF) to take into account some of the significant differences while implementing AI use cases This reference book is for scholars, researchers and professionals interested in Artificial Intelligence and Actuarial Science"--
Artificial Intelligence And Actuarial Science
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Author : Sonal Trivedi
language : en
Publisher: CRC Press
Release Date : 2024-12-27
Artificial Intelligence And Actuarial Science written by Sonal Trivedi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
This book aims to explore how to automate, innovate, design, and deploy emerging technologies in actuarial work transformations for the insurance and finance sector. It examines the role of artificial intelligence with process automation in daily monitoring of solvency, governance, compliance, data processes, etc. It also explores the usage of machine learning, telematics system, AI-enabled claim processing software, Big Data and Algorithms, Explainable AI, and AI-enabled risk management tools in various actuarial processes. This book: • Presents case studies and best practices with real-world examples of successful and unsuccessful actuarial work transformation initiatives and transformation with emerging technologies • Offers deployment solutions for different applications of AI in actuarial work • Discusses how organizations can effectively incorporate AI into their current practices of actuarial work • Covers diverse emerging technologies, practices, and processes of actuaries from around the globe • Elaborates upon a framework for comprehending how big data and AI developments may affect insurance offers and their supervision • Explains how insurance companies may review and modify their current Risk Management Framework (RMF) to take into account some of the significant differences while implementing AI use cases This reference book is for scholars, researchers and professionals interested in Artificial Intelligence and Actuarial Science.
Ai In Actuarial Science
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Author : Ronald Richman
language : en
Publisher:
Release Date : 2018
Ai In Actuarial Science written by Ronald Richman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Rapid advances in Artificial Intelligence and Machine Learning are creating products and services with the potential not only to change the environment in which actuaries operate, but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning”. This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. After providing some background on machine learning and deep learning, and providing a heuristic for where actuaries might benefit from applying these techniques, the paper surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. The paper concludes with an outlook on the potential for actuaries to integrate deep learning into their activities.
Artificial Intelligence Driven Transformation In Insurance Security Devops And Intelligent Advisory Systems
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Author : Balaji Adusupalli
language : en
Publisher: Deep Science Publishing
Release Date : 2025-05-07
Artificial Intelligence Driven Transformation In Insurance Security Devops And Intelligent Advisory Systems written by Balaji Adusupalli and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.
The insurance industry is undergoing a radical transformation driven by the exponential growth of artificial intelligence (AI) and digital technologies. Once viewed as a traditional, paperwork-heavy sector, insurance is now embracing intelligent systems to streamline operations, enhance customer experiences, and manage risks more effectively. This book, AI-Driven Transformation in Insurance: Security, DevOps, and Intelligent Advisory Systems, explores the dynamic convergence of AI, cybersecurity, DevOps, and next-generation advisory platforms that are reshaping the insurance landscape. In a world increasingly defined by real-time data and digital interactions, insurance providers must adapt rapidly to stay competitive. AI is no longer a future ambition—it is a present-day imperative. From underwriting automation and fraud detection to personalized policy recommendations and predictive analytics, AI is enabling insurers to make smarter decisions faster. However, this transformation also introduces complex challenges related to data security, system integration, and regulatory compliance. This book takes a holistic view of the AI-powered insurance ecosystem. It discusses how secure DevOps practices—often referred to as DevSecOps—ensure that continuous integration and delivery pipelines are not only agile but also robust against evolving cyber threats. Additionally, it examines the rise of intelligent advisory systems that leverage natural language processing, machine learning, and contextual awareness to provide proactive and highly customized customer support. Through real-world case studies, industry insights, and a blend of technical and strategic analysis, readers will gain a deeper understanding of the tools and frameworks driving this new era of digital insurance. Whether you're a technology leader, insurance executive, data scientist, or researcher, this book offers a timely and practical guide to navigating the AI revolution in insurance. As the boundaries between technology and insurance continue to blur, the future belongs to those who can harness AI not just to automate, but to innovate. We invite you to explore the road ahead—where intelligent systems are not just supporting insurance operations, but redefining them entirely.
Emerging Trends In Data Science Machine Learning Iot And Artificial Intelligence
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Author : Shaweta Narula
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-03-11
Emerging Trends In Data Science Machine Learning Iot And Artificial Intelligence written by Shaweta Narula and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-11 with Computers categories.
Shaweta Narula, Assistant Professor, Department of Electronics and Communication, Nutan College of Engineering and Research, Talegaon Dabhade, Pune Maharashtra,India. Vivek Narula, Quality Manager, Multinational Company, Automotive Industry, Pune, Maharashtra, India.
Artificial Intelligence And Insurance Solutions For The Modern Property Landscape
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Author : LAHARI PANDIRI
language : en
Publisher: GLOBAL PEN PRESS UK PUBLICATION
Release Date :
Artificial Intelligence And Insurance Solutions For The Modern Property Landscape written by LAHARI PANDIRI and has been published by GLOBAL PEN PRESS UK PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Bayesian Machine Learning In Quantitative Finance
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Author : Wilson Tsakane Mongwe
language : en
Publisher: Springer Nature
Release Date : 2025-07-23
Bayesian Machine Learning In Quantitative Finance written by Wilson Tsakane Mongwe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-23 with Business & Economics categories.
This book offers a comprehensive discussion of the Bayesian inference framework and demonstrates why this probabilistic approach is ideal for tackling the various modelling problems within quantitative finance. It demonstrates how advanced Bayesian machine learning techniques can be applied within financial engineering, investment portfolio management, insurance, municipal finance management as well as banking. The book covers a broad range of modelling approaches, including Bayesian neural networks, Gaussian processes and Markov Chain Monte Carlo methods. It also discusses the utility of Bayesian inference in quantitative finance and discusses future research goals in the applications of Bayesian machine learning in quantitative finance. Chapters are rooted in the theory of quantitative finance and machine learning while also outlining a range of practical considerations for implementing Bayesian techniques into real-world quantitative finance problems. This book is ideal for graduate researchers and practitioners at the intersection of machine learning and quantitative finance, as well as those working in computational statistics and computer science more broadly.
Effective Statistical Learning Methods For Actuaries Iii
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Author : Michel Denuit
language : en
Publisher: Springer
Release Date : 2019-11-13
Effective Statistical Learning Methods For Actuaries Iii written by Michel Denuit and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-13 with Business & Economics categories.
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.
Big Data And Artificial Intelligence
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Author : Anirban Dasgupta
language : en
Publisher: Springer Nature
Release Date : 2025-03-03
Big Data And Artificial Intelligence written by Anirban Dasgupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-03 with Computers categories.
This book constitutes the proceedings of the 12th International Conference on Big Data and Artificial Intelligence, BDA 2024, held in Hyderabad, India, during December 17–20, 2024. The 16 full papers and 12 short papers presented here were carefully reviewed and selected from 106 submissions. These papers have been categorized under the following topical sections: Image Classification; Graph Analytics; Big Data Analytics; Applications; Data Science; Health-Care Analytics; eLearning; Prediction and Forecasting.
Machine Learning With R
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Author : Brett Lantz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-29
Machine Learning With R written by Brett Lantz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-29 with Computers categories.
Learn how to solve real-world data problems using machine learning and R Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with this clear, hands-on guide by machine learning expert Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data. You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data. What you will learn Learn the end-to-end process of machine learning from raw data to implementation Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks Prepare, transform, and clean data using the tidyverse Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow Who this book is for This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.