Machine Learning In Insurance

Author : Jens Perch Nielsen
Publisher : MDPI
ISBN 13 : 3039364472
Total Pages : 260 pages
Book Rating : 4.73/5 ( download)

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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 : Glenn Fung
Publisher : Frontiers Media SA
ISBN 13 : 2889718115
Total Pages : 135 pages
Book Rating : 4.15/5 ( download)

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Download or read book Artificial Intelligence in Insurance and Finance written by Glenn Fung and published by Frontiers Media SA. This book was released on 2022-01-04 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Luisa Fernanda Polania Cabrera is an Experienced Professional at Target Corporation (United States). Victor Wu is a Product Manager at GitLab Inc, San Francisco, United States. Sou-Cheng Choi is a Consulting Principle Data Scientist at Allstate Corporation. Lawrence Kwan Ho Ma is the Founder, Director and Chief Scientist of Valigo Limited and Founder, CEO and Chief Scientist of EMALI.IO Limited. Glenn M. Fung is the Chief Research Scientist at American Family Insurance.

Author : Marco Aleandri
Publisher :
ISBN 13 : 9788825528657
Total Pages : 276 pages
Book Rating : 4.55/5 ( download)

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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 : Marine Corlosquet-Habart
Publisher : John Wiley & Sons
ISBN 13 : 1786300737
Total Pages : 190 pages
Book Rating : 4.37/5 ( download)

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Download or read book Big Data for Insurance Companies written by Marine Corlosquet-Habart and published by John Wiley & Sons. This book was released on 2018-03-13 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will be a "must" for people who want good knowledge of big data concepts and their applications in the real world, particularly in the field of insurance. It will be useful to people working in finance and to masters students using big data tools. The authors present the bases of big data: data analysis methods, learning processes, application to insurance and position within the insurance market. Individual chapters a will be written by well-known authors in this field.

Author : Kiran Sood
Publisher : Emerald Group Publishing
ISBN 13 : 1802626077
Total Pages : 283 pages
Book Rating : 4.70/5 ( download)

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Download or read book Big Data written by Kiran Sood and published by Emerald Group Publishing. This book was released on 2022-07-19 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Striking a balance between the technical characteristics of the subject and the practical aspects of decision making, spanning from fraud analytics in claims management, to customer analytics, to risk analytics in solvency, the comprehensive coverage presented makes Big Data an invaluable resource for any insurance professional.

Author : Jens Perch Nielsen
Publisher :
ISBN 13 : 9783039364480
Total Pages : 260 pages
Book Rating : 4.80/5 ( download)

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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 : Sabine L.B VanderLinden
Publisher : John Wiley & Sons
ISBN 13 : 1119362245
Total Pages : 328 pages
Book Rating : 4.41/5 ( download)

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Download or read book The INSURTECH Book written by Sabine L.B VanderLinden and published by John Wiley & Sons. This book was released on 2018-04-10 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive compendium for the Insurance Digital Revolution From slow beginnings in 2014, InsurTech has captured US$7billion in investment since 2010 — a 10% annual compound growth rate is predicted until at least 2020. Three in four insurance companies believe some part of their business is at risk of disruption and understanding the trends, drivers and emerging technologies behind Insurance’s Digital Revolution is a business-critical priority for all growth-minded firms. The InsurTech Book offers essential updates, critical thinking and actionable insight — globally — from start-ups, incumbents, investors, tech companies, advisors and other partners in this evolving ecosystem, in one volume. For some, Insurance is either facing an existential threat; for others, it is a sector on the brink of transforming itself. Either way, business models, value chains, customer understanding and engagement, organisational structures and even what Insurance is for, is never going to be the same. Be informed, be part of it. Learn from diverse experiences, mindsets and applications of technologies Discover new ways of defining and grasping growth opportunities Get the inside track from innovators, disruptors and incumbents Be updated on the evolution of InsurTech, why it is happening and how it will evolve Explore visions of the future of Insurance to help shape yours The InsurTech Book is your indispensable guide to a sector in transformation.

Author : Theo Lynn
Publisher : Springer
ISBN 13 : 3030023303
Total Pages : 194 pages
Book Rating : 4.00/5 ( download)

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Download or read book Disrupting Finance written by Theo Lynn and published by Springer. This book was released on 2018-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
ISBN 13 : 1788471474
Total Pages : 461 pages
Book Rating : 4.73/5 ( download)

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Download or read book Scala Machine Learning Projects written by Md. Rezaul Karim and published by Packt Publishing Ltd. This book was released on 2018-01-31 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming. Key Features Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework. Book Description Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment. What you will learn Apply advanced regression techniques to boost the performance of predictive models Use different classification algorithms for business analytics Generate trading strategies for Bitcoin and stock trading using ensemble techniques Train Deep Neural Networks (DNN) using H2O and Spark ML Utilize NLP to build scalable machine learning models Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application Learn how to use autoencoders to develop a fraud detection application Implement LSTM and CNN models using DeepLearning4j and MXNet Who this book is for If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.

Author : Ajay Agrawal
Publisher : University of Chicago Press
ISBN 13 : 0226833127
Total Pages : 172 pages
Book Rating : 4.25/5 ( download)

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Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2024-03-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.