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| data analysis in banking: Banking Analytics George M Haylett, 2021-03-31 The need to understand customers - their behaviours, their transactions, their intentions - has never been more important. Such understanding is the primary advantage traditional banks possess against competition from new market entrants and disruptive innovation. Unlocking that understanding requires analytics. Whether you want to build an analytics team from scratch or extract more value from the resources you already have, this book will show you how to exploit analytics successfully-identifying the capabilities, the opportunities, and the business integration model. Banking Analytics: How to Survive and Thrive addresses these issues, plus: Outlines the analytics strategy and approach for CEOs and senior execs Lays out plentiful examples of applications that work for business managers Identifies where to find the maximum value from the analytics contribution Considers execution issues, including hiring, outsourcing, governance and control |
| data analysis in banking: Financial Statistics and Data Analytics Shuangzhe Li, Milind Sathye, 2021-03-02 Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three. |
| data analysis in banking: Audit Analytics in the Financial Industry Jun Dai, Miklos A. Vasarhelyi, Ann Medinets, 2019-10-28 Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes. |
| data analysis in banking: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| data analysis in banking: Financial Data Analytics Sinem Derindere Köseoğlu, 2022-04-25 This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics. |
| data analysis in banking: FDIC Statistics on Banking , 1993 A statistical profile of the United States banking industry. |
| data analysis in banking: Big Data Analytics: Systems, Algorithms, Applications C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, L.M. Jenila Livingston, 2019-10-14 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. |
| data analysis in banking: The Global Findex Database 2017 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, Saniya Ansar, 2018-04-19 In 2011 the World Bank—with funding from the Bill and Melinda Gates Foundation—launched the Global Findex database, the world's most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers more than 140 economies around the world. The initial survey round was followed by a second one in 2014 and by a third in 2017. Compiled using nationally representative surveys of more than 150,000 adults age 15 and above in over 140 economies, The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution includes updated indicators on access to and use of formal and informal financial services. It has additional data on the use of financial technology (or fintech), including the use of mobile phones and the Internet to conduct financial transactions. The data reveal opportunities to expand access to financial services among people who do not have an account—the unbanked—as well as to promote greater use of digital financial services among those who do have an account. The Global Findex database has become a mainstay of global efforts to promote financial inclusion. In addition to being widely cited by scholars and development practitioners, Global Findex data are used to track progress toward the World Bank goal of Universal Financial Access by 2020 and the United Nations Sustainable Development Goals. The database, the full text of the report, and the underlying country-level data for all figures—along with the questionnaire, the survey methodology, and other relevant materials—are available at www.worldbank.org/globalfindex. |
| data analysis in banking: Artificial Intelligence in Banking Introbooks, 2020-04-07 In these highly competitive times and with so many technological advancements, it is impossible for any industry to remain isolated and untouched by innovations. In this era of digital economy, the banking sector cannot exist and operate without the various digital tools offered by the ever new innovations happening in the field of Artificial Intelligence (AI) and its sub-set technologies. New technologies have enabled incredible progression in the finance industry. Artificial Intelligence (AI) and Machine Learning (ML) have provided the investors and customers with more innovative tools, new types of financial products and a new potential for growth.According to Cathy Bessant (the Chief Operations and Technology Officer, Bank of America), AI is not just a technology discussion. It is also a discussion about data and how it is used and protected. She says, In a world focused on using AI in new ways, we're focused on using it wisely and responsibly. |
| data analysis in banking: Business Intelligence Demystified Anoop Kumar V K, 2021-09-25 Clear your doubts about Business Intelligence and start your new journey KEY FEATURES ● Includes successful methods and innovative ideas to achieve success with BI. ● Vendor-neutral, unbiased, and based on experience. ● Highlights practical challenges in BI journeys. ● Covers financial aspects along with technical aspects. ● Showcases multiple BI organization models and the structure of BI teams. DESCRIPTION The book demystifies misconceptions and misinformation about BI. It provides clarity to almost everything related to BI in a simplified and unbiased way. It covers topics right from the definition of BI, terms used in the BI definition, coinage of BI, details of the different main uses of BI, processes that support the main uses, side benefits, and the level of importance of BI, various types of BI based on various parameters, main phases in the BI journey and the challenges faced in each of the phases in the BI journey. It clarifies myths about self-service BI and real-time BI. The book covers the structure of a typical internal BI team, BI organizational models, and the main roles in BI. It also clarifies the doubts around roles in BI. It explores the different components that add to the cost of BI and explains how to calculate the total cost of the ownership of BI and ROI for BI. It covers several ideas, including unconventional ideas to achieve BI success and also learn about IBI. It explains the different types of BI architectures, commonly used technologies, tools, and concepts in BI and provides clarity about the boundary of BI w.r.t technologies, tools, and concepts. The book helps you lay a very strong foundation and provides the right perspective about BI. It enables you to start or restart your journey with BI. WHAT YOU WILL LEARN ● Builds a strong conceptual foundation in BI. ● Gives the right perspective and clarity on BI uses, challenges, and architectures. ● Enables you to make the right decisions on the BI structure, organization model, and budget. ● Explains which type of BI solution is required for your business. ● Applies successful BI ideas. WHO THIS BOOK IS FOR This book is a must-read for business managers, BI aspirants, CxOs, and all those who want to drive the business value with data-driven insights. TABLE OF CONTENTS 1. What is Business Intelligence? 2. Why do Businesses need BI? 3. Types of Business Intelligence 4. Challenges in Business Intelligence 5. Roles in Business Intelligence 6. Financials of Business Intelligence 7. Ideas for Success with BI 8. Introduction to IBI 9. BI Architectures 10. Demystify Tech, Tools, and Concepts in BI |
| data analysis in banking: The Digital Journey of Banking and Insurance, Volume III Volker Liermann, Claus Stegmann, 2021-10-27 This book, the third one of three volumes, focuses on data and the actions around data, like storage and processing. The angle shifts over the volumes from a business-driven approach in “Disruption and DNA” to a strong technical focus in “Data Storage, Processing and Analysis”, leaving “Digitalization and Machine Learning Applications” with the business and technical aspects in-between. In the last volume of the series, “Data Storage, Processing and Analysis”, the shifts in the way we deal with data are addressed. |
| data analysis in banking: Data Analytics for Management, Banking and Finance Foued Saâdaoui, Yichuan Zhao, Hana Rabbouch, 2023-09-19 This book is a practical guide on the use of various data analytics and visualization techniques and tools in the banking and financial sectors. It focuses on how combining expertise from interdisciplinary areas, such as machine learning and business analytics, can bring forward a shared vision on the benefits of data science from the research point of view to the evaluation of policies. It highlights how data science is reshaping the business sector. It includes examples of novel big data sources and some successful applications on the use of advanced machine learning, natural language processing, networks analysis, and time series analysis and forecasting, among others, in the banking and finance. It includes several case studies where innovative data science models is used to analyse, test or model some crucial phenomena in banking and finance. At the same time, the book is making an appeal for a further adoption of these novel applications in the field of economics and finance so that they can reach their full potential and support policy-makers and the related stakeholders in the transformational recovery of our societies. The book is for stakeholders involved in research and innovation in the banking and financial sectors, but also those in the fields of computing, IT and managerial information systems, helping through this new theory to better specify the new opportunities and challenges. The many real cases addressed in this book also provide a detailed guide allowing the reader to realize the latest methodological discoveries and the use of the different Machine Learning approaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use and evaluate performance of new data science tools and frameworks |
| data analysis in banking: FDIC Quarterly , 2009 |
| data analysis in banking: Credit Risk Analytics Bart Baesens, Daniel Roesch, Harald Scheule, 2016-10-03 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. |
| data analysis in banking: Big Data and Analytics Vincenzo Morabito, 2015-01-31 This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics. |
| data analysis in banking: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| data analysis in banking: Historical Statistics on Banking , 1934 |
| data analysis in banking: The FDIC Quarterly Banking Profile , 1995 |
| data analysis in banking: Financial Risk Management in Banking Shahsuzan Zakaria, Sardar Islam, 2019-08-08 As risk-taking is an essential part of the banking industry, banks must practise efficient risk management to ensure survival in uncertain financial climates. Banking operations are specifically affected by fluctuations in interest rates which cause financial imbalance; thus banks are now required to put in place an effective management structure that incorporates risk management efficiency measures that help mitigate the wide range of risks they face. In this book, the authors have developed a new modelling approach to determine banks’ financial risk management by offering detailed insights into the integrated approach of dollar-offset ratio and Data Envelopment Analysis (DEA), based on derivatives usage. It further analyses the efficiency measurement under stochastic DEA approaches, namely (i) Bootstrap DEA (BDEA), (ii) Sensitivity Analysis and (iii) Chance-Constrained DEA (CCDEA). As demonstrated in the modelling exercise, this integrated approach can be applied to other cases that require risk management efficiency measurement strategies. Additionally, this is the first book to comprehensively review the derivative markets of both the developed and developing countries in the Asia-Pacific region, by examining the differences of risk management efficiency of the banking institutions in these countries. Based on this measurement approach, strategies are provided for banks to improve their strategic risk management practices, as well as to reduce the impacts from external risks, such as changes in interest rates and exchange rates. Furthermore, this book will help banks to keep abreast of recent developments in the field of efficiency studies in management accounting, specifically in relation to hedge accounting, used by banks in the Asia-Pacific region. |
| data analysis in banking: Revisiting Risk-Weighted Assets Vanessa Le Leslé, Ms.Sofiya Avramova, 2012-03-01 In this paper, we provide an overview of the concerns surrounding the variations in the calculation of risk-weighted assets (RWAs) across banks and jurisdictions and how this might undermine the Basel III capital adequacy framework. We discuss the key drivers behind the differences in these calculations, drawing upon a sample of systemically important banks from Europe, North America, and Asia Pacific. We then discuss a range of policy options that could be explored to fix the actual and perceived problems with RWAs, and improve the use of risk-sensitive capital ratios. |
| data analysis in banking: A Complete Book on Data Interpretation & Data Analysis (eBook) Adda247 Publications, 2019-02-01 -2000+ Questions Based on Latest Pattern with detailed Solutions -Covers all the types of DI such as Table| Pie | Bar | Line | Caselet |Radar -Includes Arithmetic Based & Missing DI asked in IBPS/SBI Mains Examinations -Includes Previous year questions asked in SBI Po mains 2018, IBPS PO mains 2018 and other exams. -Essential for both Prelims and Mains exams A Complete Book on Data Interpretation and Analysis eBook’ is an effort to assist all the government job aspirants with a comprehensive, reliable and satisfactory source of offline practice materials to improve their proficiency in Quantitative Aptitude. This ebook is a unique approach towards fulfilling the needs of our dedicated aspirants who wish to clear any obstacle with ease. We should never be confined by the limits of our brain and this eBook which is thoroughly revised and covers every crucial aspect of all the Banking and Insurance examinations assures you that it will help you in transcending your limits. The ebook comprises more than 300 DIs which include 2000+ Questions covering all the patterns and topics that the IBPS, SBI and other banking exams have been surprising us with for last few years. The ebook is elegantly divided into different chapters namely Table, Bar Graph, Line Graph, Pie Graph, Mixed Graph, Arithmetic and Caselets. Each chapter is further categorized into four parts – Solved Examples, Previous years’ exercises, Level 1 exercise (Basic to Moderate) and Level 2 exercise (Advance). There are new methods and approach to solving the latest pattern questions within a short time limit. Detailed solutions are provided to every question for better CONCEPTUAL learning. In the second edition, we have includes more than 500 Questions based on latest pattern and questions asked in recent exams like SBI PO 2018, IBPS PO 2018, RRB PO 2018 and other exams. The questions are duly framed and prepared by our best faculties in this field. While preparing, all the necessities including minute details have been taken care of. The questions are preferably selected based on their quality, inculcating different levels and types that are being asked in the banking and insurance examinations. The ebook will be extremely helpful in preparing for all the Banking and Insurance examinations like IBPS PO, SBI PO, BANK OF BARODA PO, SYNDICATE BANK PO, RBI ASSISTANT, OICL, UIIC, etc. |
| data analysis in banking: Statistics on Banking , 2000 Provides comprehensive industry data about FDIC-insured depository institutions, including information on the number of banks and branches as well as financial data on FDIC-insured commercial banks and savings institutions. |
| data analysis in banking: 2017 International Conference on I SMAC (IoT in Social, Mobile, Analytics and Cloud) (I SMAC) IEEE Staff, 2017-02-10 I SMAC will provide an outstanding international forum for sharing knowledge and results in all future fields of Internet of Things in Social, Mobile, Analytics and Cloud I SMAC provides quality key experts who provide an opportunity in bringing up innovative ideas |
| data analysis in banking: ICT for an Inclusive World Youcef Baghdadi, Antoine Harfouche, Marta Musso, 2020-01-30 This book discusses the impact of information and communication technologies (ICTs) on organizations and on society as a whole. Specifically, it examines how such technologies improve our life and work, making them more inclusive through smart enterprises. The book focuses on how actors understand Industry 4.0 as well as the potential of ICTs to support organizational and societal activities, and how they adopt and adapt these technologies to achieve their goals. Gathering papers from various areas of organizational strategy, such as new business models, competitive strategies and knowledge management, the book covers a number of topics, including how innovative technologies improve the life of the individuals, organizations, and societies; how social media can drive fundamental business changes, as their innovative nature allows for interactive communication between customers and businesses; and how developing countries can use these technologies in an innovative way. It also explores the impact of organizations on society through sustainable development and social responsibility, and how ICTs use social media networks in the process of value co-creation, addressing these issues from both private and public sector perspectives and on national and international levels, mainly in the context of technology innovations. |
| data analysis in banking: Banking Business Models Rym Ayadi, 2019-04-23 This book is a result of several years of research to provide readers with a novel and comprehensive analysis on business models in banking, essential to understanding bank businesses pre- and post- financial crisis and how they evolve in the financial system. This book will provide depositors, creditors, credit rating agencies, investors, regulators, supervisors, and other market participants with a comprehensive analytical framework and analysis to better understand the nature of risk attached to the bank business models and its contribution to systemic risk throughout the economic cycle. The book will also guide post-graduate students and researchers delving into this topic. |
| data analysis in banking: International Convergence of Capital Measurement and Capital Standards , 2004 |
| data analysis in banking: CU 2.0 Kirk Drake, 2017-06-14 In recent decades, credit unions have seen unprecedented threats, due in large part to an eighty-year-old business model and an inability to adapt quickly to a digital economy. But Kirk Drake has devised a powerful plan to revitalize these noble institutions, making them more competitive, more creative, more connected with their membership, and more in tune with the times. A serial entrepreneur focused on credit-union technology, Drake has written a must-read manual for every CU board member, CEO, and management team in America. The first and only book of its kind, CU 2.0 offers essential strategies for leveraging the latest technologies to facilitate organizational growth and foster more even competition with the banking industry. With the tools provided here, the CU of tomorrow will be better equipped to empower its employees, while giving its members the superior financial service they want and need. It's time to be innovative and bold, to challenge long-standing inefficiencies and move away from the old school methods of doing business. CU 2.0 provides the skills, the savvy, and the fresh ideas necessary to finally transport the credit union out of the twentieth century and into the twenty-first. |
| data analysis in banking: Guide to Big Data Applications S. Srinivasan, 2017-05-25 This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data. |
| data analysis in banking: The Growth of Shadow Banking Matthias Thiemann, 2018-05-31 The 'shadow banking system' refers to a system of credit-provision occurring outside of the official regulatory perimeter of commercial banks. Facilitated by securitization vehicles, mutual funds, hedge funds, investment banks and mortgage companies, the function and regulation of these shadow banking institutions has come under increasing scrutiny after the subprime crisis of 2007–8. Matthias Thiemann examines how regulators came to tolerate the emergence of links between the banking and shadow banking systems. Through a comparative analysis of the US, France, the Netherlands and Germany, he argues that fractured domestic and global governance systems determining the regulatory approach to these links ultimately aggravated the recent financial crisis. Since 2008, shadow banking has even expanded and the incentives for banks to bend the rules have only increased with increasing regulation. Thiemann's empirical work suggests how state-finance relations could be restructured to keep the banking system under state control and avoid future financial collapses. |
| data analysis in banking: An Introduction to Analysis of Financial Data with R Ruey S. Tsay, 2014-08-21 A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets. |
| data analysis in banking: Coordinated Portfolio investment Survey International Monetary Fund, 1997-01-01 This paper presents a coordinated portfolio investment survey guide provided to assist national compilers in the conduct of the Coordinated Portfolio Investment Survey, conducted under the auspices of the IMF with reference to the year-end 1997. The guide covers a variety of conceptual issues that a country must address when conducting a survey. It also covers the practical issues associated with preparing for a national survey. These include setting a timetable, taking account of the legal and confidentiality issues raised, developing a mailing list, and maintaining quality control checks. |
| data analysis in banking: Event- and Data-Centric Enterprise Risk-Adjusted Return Management Kannan Subramanian R, Dr. Sudheesh Kumar Kattumannil, 2022-01-06 Take a holistic view of enterprise risk-adjusted return management in banking. This book recommends that a bank transform its siloed operating model into an agile enterprise model. It offers an event-driven, process-based, data-centric approach to help banks plan and implement an enterprise risk-adjusted return model (ERRM), keeping the focus on business events, processes, and a loosely coupled enterprise service architecture. Most banks suffer from a lack of good quality data for risk-adjusted return management. This book provides an enterprise data management methodology that improves data quality by defining and using data ontology and taxonomy. It extends the data narrative with an explanation of the characteristics of risk data, the usage of machine learning, and provides an enterprise knowledge management methodology for risk-return optimization. The book provides numerous examples for process automation, data analytics, event management, knowledge management, and improvements to risk quantification. The book provides guidance on the underlying knowledge areas of banking, enterprise risk management, enterprise architecture, technology, event management, processes, and data science. The first part of the book explains the current state of banking architecture and its limitations. After defining a target model, it explains an approach to determine the gap and the second part of the book guides banks on how to implement the enterprise risk-adjusted return model. What You Will Learn Know what causes siloed architecture, and its impact Implement an enterprise risk-adjusted return model (ERRM) Choose enterprise architecture and technology Define a reference enterprise architecture Understand enterprise data management methodology Define and use an enterprise data ontology and taxonomy Create a multi-dimensional enterprise risk data model Understand the relevance of event-driven architecture from business generation and risk management perspectives Implement advanced analytics and knowledge management capabilities Who This Book Is For The global banking community, including: senior management of a bank, such as the Chief Risk Officer, Head of Treasury/Corporate Banking/Retail Banking, Chief Data Officer, and Chief Technology Officer. It is also relevant for banking software vendors, banking consultants, auditors, risk management consultants, banking supervisors, and government finance professionals. |
| data analysis in banking: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data analysis in banking: Analyzing Banking Risk Sonja Brajovic Bratanovic, 2020-06-15 This publication aims to complement existing methodologies by establishing a comprehensive framework for the assessment of banks, not only by using financial data but also by considering corporate governance. |
| data analysis in banking: Banking 5.0 Bernardo Nicoletti, 2021-07-06 Bill Gates’ quote, “Banking is necessary, but banks are not,” showcases the opportunity for financial services digital transformation. The next transition from industry 4.0 to 5.0 will impact all sectors, including banking. It will combine information technology and automation, based on artificial intelligence, person-robot collaboration, and sustainability. It is time to analyze this transformation in banking deeply, so that the sector can adequately change to the ‘New Normal’ and a wholly modified banking model can be properly embedded in the business. This book presents a conceptual model of banking 5.0, detailing its implementation in processes, platforms, people, and partnerships of financial services organizations companies. The last part of the book is then dedicated to future developments. Of interest to academics, researchers, and professionals in banking, financial technology, and financial services, this book also includes business cases in financial services. |
| data analysis in banking: Reshaping Accounting and Management Control Systems Katia Corsi, Nicola Giuseppe Castellano, Rita Lamboglia, Daniela Mancini, 2017-03-21 This book examines the relationship between digital innovations on the one hand, and accounting and management information systems on the other. In particular it addresses topics including cloud computing, data mining, XBRL, and digital platforms. It presents an analysis of how new technologies can reshape accounting and management information systems, enhancing their information potentialities and their ability to support decision-making processes, as well as several studies that reveal how managerial information needs can affect and reshape the adoption of digital technologies. Focusing on the four major aspects data management, information system architecture, external and internal reporting, the book offers a valuable resource for CIOs, CFOs and more generally for business managers, as well as for researchers and scholars. It is mainly based on a selection of the best papers - original double blind reviewed contributions - presented at the 2015 Annual Conference of the Italian Chapter of the Association for Information Systems (AIS). |
| data analysis in banking: Data Envelopment Analysis in the Financial Services Industry Joseph C. Paradi, H. David Sherman, Fai Keung Tam, 2017-11-21 This book presents the methodology and applications of Data Envelopment Analysis (DEA) in measuring productivity, efficiency and effectiveness in Financial Services firms such as banks, bank branches, stock markets, pension funds, mutual funds, insurance firms, credit unions, risk tolerance, and corporate failure prediction. Financial service DEA research includes banking; insurance businesses; hedge, pension and mutual funds; and credit unions. Significant business transactions among financial service organizations such as bank mergers and acquisitions and valuation of IPOs have also been the focus of DEA research. The book looks at the range of DEA uses for financial services by presenting prior studies, examining the current capabilities reflected in the most recent research, and projecting future new uses of DEA in finance related applications. |
| data analysis in banking: Python for Finance Yves Hilpisch, 2014-12-11 The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies |
| data analysis in banking: Bank Risk, Governance and Regulation Elena Beccalli, Federica Poli, 2015-08-18 This book presents research from leading researchers in the European banking field to explore three key areas of banking. In Bank Risk, Governance and Regulation, the authors conduct micro- and macro- level analysis of banking risks and their determinants. They explore areas such as credit quality, bank provisioning, deposit guarantee schemes, corporate governance and cost of capital. The book then goes on to analyse different aspects of the relationship between bank risk management, governance and performance. Lastly the book explores the regulation of systemic risks posed by banks, and examines the effects of novel regulatory sets on bank conduct and profitability. The research in this book focuses on aspects of the European banking system; however it also offers wider insight into the global banking space and offers comparisons to international banking systems. The study provides in-depth insight into many areas of bank risk, governance and regulation, before finally addressing the question: which banking strategies are actually feasible? |
| data analysis in banking: Cyber Security Intelligence and Analytics Zheng Xu, Reza M. Parizi, Mohammad Hammoudeh, Octavio Loyola-González, 2020-03-10 This book presents the outcomes of the 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020), an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary field of cyber security, particularly focusing on threat intelligence, analytics, and countering cyber crime. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings, and novel techniques, methods and applications on all aspects of Cyber Security Intelligence and Analytics. The 2020 International Conference on Cyber Security Intelligence and Analytics (CSIA 2020) is held at Feb. 28-29, 2020, in Haikou, China, building on the previous successes in Wuhu, China (2019) is proud to be in the 2nd consecutive conference year. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
