The Global Financial Crisis and the Eurozone crisis that has followed have drawn attention to weaknesses in financial records, information and data. These weaknesses have led to operational risks in financial institutions, flawed bankruptcy and foreclosure proceedings following the Crisis, and inadequacies in financial supervisors’ access to records and information for the purposes of a prudential response. Research is needed to identify the practices that will provide the records, information and data needed to support more effective financial analysis and risk management. The unique contribution of this volume is in bringing together researchers in distinct domains that seldom interact to identify theoretical, technological, policy and practical issues related to the management of financial records, information and data. The book will, therefore, appeal to researchers or advanced practitioners in the field of finance and those with an interest in risk management, computer science, cognitive science, sociology, management information systems, information science, and archival science as applied to the financial domain.

This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.

This book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management. Analytics in Finance and Risk Management covers statistical techniques for data analysis in finance It explores applications in finance and risk management, covering empirical properties of financial systems. It addresses data science involving the study of statistical and computational models and includes basic and advanced concepts. The chapters incorporate the latest methodologies and challenges facing financial and risk management and illustrate related issues and their implications in the real world. The primary users of this book will include researchers, academicians, postgraduate students, professionals in engineering and business analytics, managers, consultants, and advisors in IT firms, financial markets, and services domains.

This book is written to empower risk professionals to turn analytics and models into deployable solutions with minimal IT intervention. Corporations, especially financial institutions, must show evidence of having quantified credit, market and operational risks. They have databases but automating the process to translate data into risk parameters remains a desire.Modelling is done using software with output codes not readily processed by databases. With increasing acceptance of open-source languages, database vendors have seen the value of integrating modelling capabilities into their products. Nevertheless, deploying solutions to automate processes remains a challenge. While not comprehensive in dealing with all facets of risks, the author aims to develop risk professionals who will be able to do just that.

The Handbook of News Analytics in Finance is a landmarkpublication bringing together the latest models and applications ofNews Analytics for asset pricing, portfolio construction, tradingand risk control. The content of the Hand Book is organised to provide arapid yet comprehensive understanding of this topic. Chapter 1 setsout an overview of News Analytics (NA) with an explanation of thetechnology and applications. The rest of the chapters are presentedin four parts. Part 1 contains an explanation of methods and modelswhich are used to measure and quantify news sentiment. In Part 2the relationship between news events and discovery of abnormalreturns (the elusive alpha) is discussed in detail by the leadingresearchers and industry experts. The material in this part alsocovers potential application of NA to trading and fund management.Part 3 covers the use of quantified news for the purpose ofmonitoring, early diagnostics and risk control. Part 4 is entirelyindustry focused; it contains insights of experts from leadingtechnology (content) vendors. It also contains a discussion oftechnologies and finally a compact directory of content vendor andfinancial analytics companies in the marketplace of NA. Thebook draws equally upon the expertise of academics andpractitioners who have developed these models and is supported bytwo major content vendors - RavenPack and Thomson Reuters - leadingproviders of news analytics software and machine readablenews. The book will appeal to decision makers in the banking, finance andinsurance services industry. In particular: asset managers;quantitative fund managers; hedge fund managers; algorithmictraders; proprietary (program) trading desks; sell-side firms;brokerage houses; risk managers and research departments willbenefit from the unique insights into this new and pertinent areaof financial modelling.

State of the art risk management techniques and practices—supplemented with interactive analytics All too often risk management books focus on risk measurement details without taking a broader view. Quantitative Risk Management delivers a synthesis of common sense management together with the cutting-edge tools of modern theory. This book presents a road map for tactical and strategic decision making designed to control risk and capitalize on opportunities. Most provocatively it challenges the conventional wisdom that "risk management" is or ever should be delegated to a separate department. Good managers have always known that managing risk is central to a financial firm and must be the responsibility of anyone who contributes to the profit of the firm. A guide to risk management for financial firms and managers in the post-crisis world, Quantitative Risk Management updates the techniques and tools used to measure and monitor risk. These are often mathematical and specialized, but the ideas are simple. The book starts with how we think about risk and uncertainty, then turns to a practical explanation of how risk is measured in today's complex financial markets. Covers everything from risk measures, probability, and regulatory issues to portfolio risk analytics and reporting Includes interactive graphs and computer code for portfolio risk and analytics Explains why tactical and strategic decisions must be made at every level of the firm and portfolio Providing the models, tools, and techniques firms need to build the best risk management practices, Quantitative Risk Management is an essential volume from an experienced manager and quantitative analyst.

The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.