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Author: Jens Perch Nielsen Publisher: MDPI ISBN: 3039364472 Category : Business & Economics Languages : en Pages : 260
Book Description
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: Jens Perch Nielsen Publisher: MDPI ISBN: 3039364472 Category : Business & Economics Languages : en Pages : 260
Book Description
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: Marine Corlosquet-Habart Publisher: John Wiley & Sons ISBN: 1119489296 Category : Business & Economics Languages : en Pages : 139
Book Description
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: Glenn Fung Publisher: Frontiers Media SA ISBN: 2889718115 Category : Science Languages : en Pages : 135
Book Description
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: Kiran Sood Publisher: Emerald Group Publishing ISBN: 1802626077 Category : Business & Economics Languages : en Pages : 283
Book Description
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: 9783039364480 Category : Languages : en Pages : 260
Book Description
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: Kiran Sood Publisher: CRC Press ISBN: 1003811477 Category : Computers Languages : en Pages : 396
Book Description
This book is a unique guide to the disruptions, innovations, and opportunities that technology provides the insurance sector and acts as an academic/industry-specific guide for creating operational effectiveness, managing risk, improving financials, and retaining customers. It also contains the current philosophy and actionable strategies from a wide range of contributors who are experts on the topic. It logically explains why traditional ways of doing business will soon become irrelevant and therefore provides an alternative choice by embracing technology. Practitioners and students alike will find value in the support for understanding practical implications of how technology has brought innovation and modern methods to measure, control, and evaluation price risk in the insurance business. It will help insurers reduce operational costs, strengthen customer interactions, target potential customers to provide usage-based insurance, and optimize the overall business. Retailers and industry giants have made significant strides in adopting digital platforms to deliver a satisfying customer experience. Insurance companies must adjust their business models and strategies to remain competitive and take advantage of technology. Insurance companies are increasingly investing in IT and related technologies to improve customer experience and reduce operational costs. Innovation through new technologies is a key driver of change in the financial sector which is often accompanied by uncertainty and doubt. This book will play a pivotal role in risk management through fraud detection, regulatory compliances, and claim settlement leading to overall satisfaction of customers.
Author: Ajay Agrawal Publisher: University of Chicago Press ISBN: 0226833127 Category : Business & Economics Languages : en Pages : 172
Book Description
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.
Author: Adam Bohr Publisher: Academic Press ISBN: 0128184396 Category : Computers Languages : en Pages : 385
Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Author: Jens Perch Nielsen
Author: Jens Perch Nielsen
Author: Marine Corlosquet-Habart
Author: Glenn Fung
Author: Kiran Sood
Author: Marco Aleandri
Author: Jens Perch Nielsen
Author: Lucio Fernandez-Arjona
Author: Kiran Sood
Author: Ajay Agrawal
Author: Adam Bohr