Analytics for Insurance

Author: Tony Boobier
Publisher: John Wiley & Sons
ISBN: 1119141079
Format: PDF
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The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential – yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business – but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data – but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.

Applied Insurance Analytics

Author: Patricia L Saporito
Publisher: FT Press
ISBN: 0133760731
Format: PDF
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Insurers: use analytics to drive far more value from your most important asset -- data! Today, many insurers radically underutilize their data, leaving them vulnerable to traditional and non-traditional competitors alike. Now, drawing on 25 years of industry experience, Patricia Saporito shows how to systematically leverage analytics to improve business performance and customer satisfaction throughout any insurance business. Applied Insurance Analytics demonstrates how to use analytics to systematically improve operations ranging from underwriting and risk management to claims. Even more important: it will help you drive more value everywhere by defining a focused enterprise-wide analytics strategy, and overcoming the challenges that stand in your way. Saporito helps you assess your current analytics maturity, choose the new applications that offer the most value, and master best practices from throughout the industry and beyond. Throughout, she helps you gain more value from data assets, technologies and tools you've already invested in. You'll find new case studies, practical tools, and easy templates for improving the "Analytics IQ" of your entire enterprise. For every insurance industry professional and manager concerned with analytics, including users, IT pros, sales/marketing specialists, and data scientists. This book will also be valuable to students in any MBA or other program focused on insurance or risk management, and to many students in IT or analytics-specific programs.

Big Data Analytics

Author: P. Krishna Reddy
Publisher: Springer
ISBN: 3319724134
Format: PDF, ePub, Mobi
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This book constitutes the refereed conference proceedings of the 5th International Conference on Big Data Analytics, BDA 2017, held in Hyderabad, India, in December 2017. The 21 revised full papers were carefully reviewed and selected from 80 submissions and cover topics on big data analytics, information and knowledge management, mining of massive datasets, computational modeling, data mining and analysis.

Fundamental Aspects of Operational Risk and Insurance Analytics

Author: Marcelo G. Cruz
Publisher: John Wiley & Sons
ISBN: 1118573005
Format: PDF, Docs
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A one-stop guide for the theories, applications, andstatistical methodologies essential to operational risk Providing a complete overview of operational risk modeling andrelevant insurance analytics, Fundamental Aspects of OperationalRisk and Insurance Analytics: A Handbook of Operational Riskoffers a systematic approach that covers the wide range of topicsin this area. Written by a team of leading experts in the field,the handbook presents detailed coverage of the theories,applications, and models inherent in any discussion of thefundamentals of operational risk, with a primary focus on BaselII/III regulation, modeling dependence, estimation of risk models,and modeling the data elements. Fundamental Aspects of Operational Risk and Insurance Analytics:A Handbook of Operational Risk begins with coverage on the fourdata elements used in operational risk framework as well asprocessing risk taxonomy. The book then goes further in-depth intothe key topics in operational risk measurement and insurance, forexample diverse methods to estimate frequency and severity models.Finally, the book ends with sections on specific topics, such asscenario analysis; multifactor modeling; and dependence modeling. Aunique companion with Advances in Heavy Tailed Risk Modeling: AHandbook of Operational Risk, the handbook also features: Discussions on internal loss data and key risk indicators,which are both fundamental for developing a risk-sensitiveframework Guidelines for how operational risk can be inserted into afirm’s strategic decisions A model for stress tests of operational risk under the UnitedStates Comprehensive Capital Analysis and Review (CCAR)program A valuable reference for financial engineers, quantitativeanalysts, risk managers, and large-scale consultancy groupsadvising banks on their internal systems, the handbook is alsouseful for academics teaching postgraduate courses on themethodology of operational risk.

Big Data Analytics

Author: Srinath Srinivasa
Publisher: Springer
ISBN: 3319138200
Format: PDF, Kindle
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This book constitutes the refereed conference proceedings of the Third International Conference on Big Data Analytics, BDA 2014, held in New Delhi, India, in December 2014. The 11 revised full papers and 6 short papers were carefully reviewed and selected from 35 submissions and cover topics on media analytics; geospatial big data; semantics and data models; search and retrieval; graphics and visualization; application-specific big data.

Fundamental Aspects of Operational Risk and Insurance Analytics and Advances in Heavy Tailed Risk Modeling Handbooks of Operational Risk Set

Author: Marcelo G. Cruz
Publisher: Wiley
ISBN: 9781118909577
Format: PDF, ePub, Mobi
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Two cutting-edge guides for the theories, applications, and statistical methodologies essential to operational risk and heavy tailed risk modeling Focusing on the quantitative aspects of heavy tailed loss processes in operational risk and relevant insurance analytics, Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk presents comprehensive coverage of the latest research on the theories and applications in risk measurement and modeling techniques. Featuring a unique balance of mathematical and statistical perspectives, the handbook begins by introducing the motivation for heavy tailed risk processes in high consequence low frequency loss modeling. With a companion, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk, the book provides a complete framework for all aspects of operational risk management. Fundamental Aspects of Operational Risk and Insurance Analytics covers the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements.

Enterprise Analytics

Author: Thomas H. Davenport
Publisher: FT Press
ISBN: 0133039463
Format: PDF, ePub
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Normal 0 false false false MicrosoftInternetExplorer4 The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

Encyclopedia of Business Analytics and Optimization

Author: Wang, John
Publisher: IGI Global
ISBN: 1466652039
Format: PDF
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As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.

Advanced Analytics and AI

Author: Tony Boobier
Publisher: John Wiley & Sons
ISBN: 1119390923
Format: PDF, ePub, Docs
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Be prepared for the arrival of automated decision making Once thought of as science fiction, major corporations are already beginning to use cognitive systems to assist in providing wealth advice and also in medication treatment. The use of Cognitive Analytics/Artificial Intelligence (AI) Systems is set to accelerate, with the expectation that it’ll be considered ‘mainstream’ in the next 5 – 10 years. It’ll change the way we as individuals interact with data and systems—and the way we run our businesses. Cognitive Analysis and AI prepares business users for the era of cognitive analytics / artificial intelligence. Building on established texts and commentary, it specifically prepares you in terms of expectation, impact on personal roles, and responsibilities. It focuses on the specific impact on key industries (retail, financial services, utilities and media) and also on key professions (such as accounting, operational management, supply chain and risk management). Shows you how users interact with the system in natural language Explains how cognitive analysis/AI can source ‘big data’ Provides a roadmap for implementation Gets you up to speed now before you get left behind If you’re a decision maker or budget holder within the corporate context, this invaluable book helps you gain an advantage from the deployment of cognitive analytics tools.