Applied Insurance Analytics:A Framework for Driving More Value from Data Assets Technologies and Tools Patricia L Saporito
Analytics for Insurance:The Real Business of Big Data Tony Boobier
Fundamental Aspects of Operational Risk and Insurance Analytics:A Handbook of Operational Risk Marcelo G. Cruz/ Gareth W. Peters/ Pavel V. Shevchenko
´´Mesmerizing & fascinating...´´ --The Seattle Post-Intelligencer ´´The Freakonomics of big data.´´ --Stein Kretsinger, founding executive of Advertising.com Award-winning Used by over 30 universities Translated into 9 languages An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a ´´how to´´ for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you´re going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world´s most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction -- now in its Revised and Updated edition -- former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death -- including one health insurance company. How U.S. Bank and Obama for America calculated -- and Hillary for America 2016 plans to calculate -- the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM´s Watson computer used predictive modeling to answer questions and beat the human champs on TV´s Jeopardy! How companies ascertain untold, private truths -- how Target figures out you´re pregnant and Hewlett-Packard deduces you´re about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics.
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. * Examine fraud patterns in historical data * Utilize labeled, unlabeled, and networked data * Detect fraud before the damage cascades * Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
Residents in Boston, Massachusetts, are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don´t have to be Nate Silver to reap massive benefits from today´s new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It´s time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and l 1. Language: English. Narrator: Stephen Dexter. Audio sample: http://samples.audible.de/bk/gdan/003065/bk_gdan_003065_sample.mp3. Digital audiobook in aax.
Harness ´´Code Halos´´ to gain competitive advantage in the digital era Amazon beating Borders, Netflix beating Blockbuster, Apple beating Kodak, and the rise of companies like Google, LinkedIn, and Pandora are not isolated or random events. Today´s outliers in revenue growth and value creation are winning with a new set of rules. They are dominating by managing the information that surrounds people, organizations, processes, and products - what authors Malcolm Frank, Paul Roehrig, and Ben Pring call Code Halos. This is far beyond ´´Big Data” and analytics. Code Halos spark new commercial models that can dramatically flip market dominance from industry stalwarts to challengers. In this new book, the authors show leaders how digital innovators and traditional companies can build Code Halo solutions to drive success. The book:Examines the explosion of digital information that now surrounds us and describes the profound impact this is having on individuals, corporations, and societiesShows how the Crossroads Model can help anticipate and navigate this market shiftProvides examples of traditional firms already harnessing the power of Code Halos including GE´s ´´Brilliant Machines,´´ Disney´s theme park ´´Magic Band,´´ and Allstate´s mobile devices and analytics that transform auto insurance With reasoned insight, new data, real-world cases, and practical guidance, Code Halos shows seasoned executives, entrepreneurs, students, line-of-business owners, and technology leaders how to master the new rules of the Code Halo economy. PLEASE NOTE: When you purchase this title, the accompanying reference material will be available in your My Library section along with the audio. 1. Language: English. Narrator: Ben Pring. Audio sample: http://samples.audible.de/bk/adbl/019212/bk_adbl_019212_sample.mp3. Digital audiobook in aax.
Updated new edition of Ralph Kimball´s groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of Ralph Kimball´s The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. * Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence * Begins with fundamental design recommendations and progresses through increasingly complex scenarios * Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more * Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.