| data management in banking: Legal Data for Banking Akber Datoo, 2019-06-17 A practical, informative guide to banks’ major weakness Legal Data for Banking defines the legal data domain in the context of financial institutions, and describes how banks can leverage these assets to optimise business lines and effectively manage risk. Legal data is at the heart of post-2009 regulatory reform, and practitioners need to deepen their grasp of legal data management in order to remain compliant with new rules focusing on transparency in trade and risk reporting. This book provides essential information for IT, project management and data governance leaders, with detailed discussion of current and best practices. Many banks are experiencing recurrent pain points related to legal data management issues, so clear explanations of the required processes, systems and strategic governance provide immediately-relevant relief. The recent financial crisis following the collapse of major banks had roots in poor risk data management, and the regulators’ unawareness of accumulated systemic risk stemming from contractual obligations between firms. To avoid repeating history, today’s banks must be proactive in legal data management; this book provides the critical knowledge practitioners need to put the necessary systems and practices in place. Learn how current legal data management practices are hurting banks Understand the systems, structures and strategies required to manage risk and optimise business lines Delve into the regulations surrounding risk aggregation, netting, collateral enforceability and more Gain practical insight on legal data technology, systems and migration The legal contracts between firms contain significant obligations that underpin the financial markets; failing to recognise these terms as valuable data assets means increased risk exposure and untapped business lines. Legal Data for Banking provides critical information for the banking industry, with actionable guidance for implementation. |
| data management 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 management in banking: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data management in banking: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management in banking: A Primer in Financial Data Management Martijn Groot, 2017-05-10 A Primer in Financial Data Management describes concepts and methods, considering financial data management, not as a technological challenge, but as a key asset that underpins effective business management. This broad survey of data management in financial services discusses the data and process needs from the business user, client and regulatory perspectives. Its non-technical descriptions and insights can be used by readers with diverse interests across the financial services industry. The need has never been greater for skills, systems, and methodologies to manage information in financial markets. The volume of data, the diversity of sources, and the power of the tools to process it massively increased. Demands from business, customers, and regulators on transparency, safety, and above all, timely availability of high quality information for decision-making and reporting have grown in tandem, making this book a must read for those working in, or interested in, financial management. - Focuses on ways information management can fuel financial institutions' processes, including regulatory reporting, trade lifecycle management, and customer interaction - Covers recent regulatory and technological developments and their implications for optimal financial information management - Views data management from a supply chain perspective and discusses challenges and opportunities, including big data technologies and regulatory scrutiny |
| data management 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 management 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 management 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 management in banking: Project Management for Banks Dan Bonner, 2021-09-14 Project management processes have been intertwined within every fabric of human evolution including advances in communication, farming, construction, medicine, law, architecture, physics, and economics to name a few. At each evolutionary stage, there was a project manager who was studying the how and why of everything, trying new techniques, and documenting trials, errors and successes until a specific craft was mastered, thrusting progress forward in an upward trajectory that has been carved into human history. There are countless books and articles that focus on the practice of project management. What makes this book different is the focus placed largely on the project management processes for United States (U.S) bankers. This book starts with a look at the historical progression of project management processes but quickly focuses the material on project management processes for bankers, heavily leaning towards project managers in United States (U.S.) banks. The book also looks at the bank regulatory agencies that govern U.S. banks, regulations critical to the U.S banking system, and concludes with an overview of U.S. banking technologies and the management of a U.S. banking customer call center. The book provides a comprehensive perspective on the U.S. banking project management processes, the regulatory agencies that govern and influence those processes, how technology, and more specifically, the development and use of artificial intelligence, will create a shift in the evolutionary trajectory of U.S. banking practices, and how U.S. banking project management practices will be at the core of how quickly and how successfully this evolution unfolds. |
| data management in banking: MASTER DATA MANAGEMENT AND DATA GOVERNANCE, 2/E Alex Berson, Larry Dubov, 2010-12-06 The latest techniques for building a customer-focused enterprise environment The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works. -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc. Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume. Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance |
| data management 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 management in banking: Blockchain and Applications Javier Prieto, Alberto Partida, Paulo Leitão, António Pinto, 2022 This book constitutes the refereed proceedings of the 1st International Congress on Blockchain and Applications 2021, BLOCKCHAIN'21, held in Salamanca, Spain, in October 2021. Among the scientific community, blockchain and artificial intelligence are a promising combination that will transform the production and manufacturing industry, media, finance, insurance, e-government, etc. Nevertheless, there is no consensus with schemes or best practices that would specify how blockchain and artificial intelligence should be used together. The 38 full papers presented were carefully reviewed and selected from over 44 submissions. They contain the latest advances on blockchain and artificial intelligence and on their application domains, exploring innovative ideas, guidelines, theories, models, technologies, and tools and identifying critical issues and challenges that researchers and practitioners must deal with in future research. |
| data management in banking: Bank Management and Control Johannes Wernz, 2013-11-08 Strategic planning, including the required quantitative methods, is an essential part of bank management and control. In this book capital, risk and yield are treated comprehensively and seamlessly. And a thorough introduction to the advanced methods of risk management for all sectors of banking is discussed. In addition, directly applicable concepts and data such as macroeconomic scenarios for strategic planning and stress testing as well as detailed scenarios for operational risk and advanced concepts for credit risk are presented in straightforward language. The book analyzes the effects of macroeconomic and regulatory developments such as the set of Basel III rules on planning, and it also presents and discusses the consequences for actively meeting these challenges, especially in terms of capital. A wealth of essential background information from practice, international observations and comparisons, along with numerous illustrative examples, make this book a useful resource for established and future professionals in bank management, risk/return management, controlling and accounting. |
| data management in banking: Data Management in a Connected World Theo Härder, Wolfgang Lehner, 2005-06-14 Data management systems play the most crucial role in building large application s- tems. Since modern applications are no longer single monolithic software blocks but highly flexible and configurable collections of cooperative services, the data mana- ment layer also has to adapt to these new requirements. Therefore, within recent years, data management systems have faced a tremendous shift from the central management of individual records in a transactional way to a platform for data integration, fede- tion, search services, and data analysis. This book addresses these new issues in the area of data management from multiple perspectives, in the form of individual contributions, and it outlines future challenges in the context of data management. These contributions are dedicated to Prof. em. Dr. Dr. -Ing. E. h. Hartmut Wedekind on the occasion of his 70th birthday, and were (co-)authored by some of his academic descendants. Prof. Wedekind is one of the most prominent figures of the database management community in Germany, and he enjoys an excellent international reputation as well. Over the last 35 years he greatly contributed to making relational database technology a success. As far back as the early 1970s, he covered—as the first author in Germany— the state of the art concerning the relational model and related issues in two widely used textbooks “Datenbanksysteme I” and “Datenbanksysteme II”. Without him, the idea of modeling complex-structured real-world scenarios in a relational way would be far less developed by now. Among Prof. |
| data management in banking: Risk Management in Banking Joël Bessis, 2015-04-30 The seminal guide to risk management, streamlined and updated Risk Management in Banking is a comprehensive reference for the risk management industry, covering all aspects of the field. Now in its fourth edition, this useful guide has been updated with the latest information on ALM, Basel 3, derivatives, liquidity analysis, market risk, structured products, credit risk, securitizations, and more. The new companion website features slides, worked examples, a solutions manual, and the new streamlined, modular approach allows readers to easily find the information they need. Coverage includes asset liability management, risk-based capital, value at risk, loan portfolio management, capital allocation, and other vital topics, concluding with an examination of the financial crisis through the utilisation of new views such as behavioural finance and nonlinearity of risk. Considered a seminal industry reference since the first edition's release, Risk Management in Banking has been streamlined for easy navigation and updated to reflect the changes in the field, while remaining comprehensive and detailed in approach and coverage. Students and professionals alike will appreciate the extended scope and expert guidance as they: Find all need-to-know risk management topics in a single text Discover the latest research and the new practices Understand all aspects of risk management and banking management See the recent crises – and the lessons learned – from a new perspective Risk management is becoming increasingly vital to the banking industry even as it grows more complex. New developments and advancing technology continue to push the field forward, and professionals need to stay up-to-date with in-depth information on the latest practices. Risk Management in Banking provides a comprehensive reference to the most current state of the industry, with complete information and expert guidance. |
| data management in banking: FDIC Statistics on Banking , 1993 A statistical profile of the United States banking industry. |
| data management in banking: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management in banking: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| data management 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 management in banking: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data management in banking: Financial Risk Management in Banking Dennis Uyemura, Uyemura Dennis, Gelder Joni Van, 1992-11 Presents an in-depth review of the tremendous risk and volatility in bank financial management. This book provides a comprehensive overview of aggressive asset and liability management (ALM) and demonstrates how ALM can strengthen the capital position of a financial institution. |
| data management 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 management in banking: International Convergence of Capital Measurement and Capital Standards , 2004 |
| data management in banking: Banking/Trading-Operations Management Gerrit Jan van den Brink, 2003-12-19 Banking/Trading-Operations Management is aimed at the practitioner and covers all the issues an operations manager has to address. Gerrit Jan van den Brink and a team of highly experienced contributors examine the current situation and extract best practice from a variety of situations. They look at trends in operations management, and at how operations link into risk and risk adjusted performance measurement, and examine the impact of e-business and the Internet on operational processes. |
| data management in banking: BIAN 2nd Edition – A framework for the financial services industry BIAN eV, 2021-07-09 The Banking Industry Architecture Network (BIAN) is a global, not-for-profit association of banks, solution providers, consultancy companies, integrators and academic partners, with the shared aim of defining a semantic standard for the banking industry covering all banking activity and almost all of the well-known architectural layers. BIAN’s Reference Architecture for the Financial Industry provides its users with a set of building blocks that, when used in different combinations, can support all of the functionality and information a bank needs for both its internal functioning and its collaboration with partners in an Open Finance and Open API economy. BIAN’s Reference Architecture for the Financial Industry is freely available on the BIAN website. This website also provides a wealth of information on both the theory and practice of the standard. So why this book? Importantly, it summarizes all of the above information and guides the reader through it on a step-by-step basis. It provides the reader with a thorough understanding of BIAN’s architecture and how it can be used to support an organization on its journey to becoming an agile business organization and developing an application platform. BIAN is a semantic standard. It provides business building blocks and defines them in business terms. It provides a business view on both the business and application architectures. This second edition not only includes the more recent deliverables, it also takes a stepped approach through the different topics. It aims to be more appealing to a business audience by addressing the building blocks of BIAN and their possible use in business terms, whilst also including many real-life examples of BIAN’s usage. As such, it should not only appeal to application and business architects, but also to their managers, their business partners and other stakeholders who work closely with them. The first part of the book focuses on the theory: BIAN’s organization, the principles and patterns on which its architecture is based, and its building blocks. The second part of the book explains – in methodology-independent terms – how BIAN can be applied in different architectural layers by different disciplines, in co-operation with architects. This part of the book includes a number of practical examples intended to improve the reader’s understanding of the building blocks of the BIAN architecture and encourage them to apply it for the benefit of their own organization. The final part of the book should inspire the reader even further by clearly illustrating the synergy between the content that BIAN delivers and the architecture methodology provided by TOGAF. |
| data management in banking: Code of Federal Regulations, Title 12, Banks and Banking, PT. 200-219, Revised as of January 1, 2010 Office of the Federal Register (U S ), 2010-03 The Code of Federal Regulations is a codification of the general and permanent rules published in the Federal Register by the Executive departments and agencies of the United States Federal Government. |
| data management in banking: Big Data Technologies and Applications Borko Furht, Flavio Villanustre, 2016-09-16 The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors. |
| data management in banking: Code of Federal Regulations, Title 12, Banks and Banking, PT. 1-199, Revised as of January 1, 2012 Office of the Federal Register (U.S.) Staff, 2012-04-24 |
| data management in banking: Modern Enterprise Business Intelligence and Data Management Alan Simon, 2014-08-28 Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the Big Data Era...and most see a critical need to revitalize their current capabilities. Whether they find the frustrating and business-impeding continuation of a long-standing silos of data problem, or an over-reliance on static production reports at the expense of predictive analytics and other true business intelligence capabilities, or a lack of progress in achieving the long-sought-after enterprise-wide single version of the truth – or all of the above – IT Directors, strategists, and architects find that they need to go back to the drawing board and produce a brand new BI/data warehousing roadmap to help move their enterprises from their current state to one where the promises of emerging technologies and a generation's worth of best practices can finally deliver high-impact, architecturally evolvable enterprise-scale business intelligence and data warehousing. Author Alan Simon, whose BI and data warehousing experience dates back to the late 1970s and who has personally delivered or led more than thirty enterprise-wide BI/data warehousing roadmap engagements since the mid-1990s, details a comprehensive step-by-step approach to building a best practices-driven, multi-year roadmap in the quest for architecturally evolvable BI and data warehousing at the enterprise scale. Simon addresses the triad of technology, work processes, and organizational/human factors considerations in a manner that blends the visionary and the pragmatic. - Takes a fresh look at true enterprise-scale BI/DW in the Dawn of the Big Data Era - Details a checklist-based approach to surveying one's current state and identifying which components are enterprise-ready and which ones are impeding the key objectives of enterprise-scale BI/DW - Provides an approach for how to analyze and test-bed emerging technologies and architectures and then figure out how to include the relevant ones in the roadmaps that will be developed - Presents a tried-and-true methodology for building a phased, incremental, and iterative enterprise BI/DW roadmap that is closely aligned with an organization's business imperatives, organizational culture, and other considerations |
| data management in banking: Basic Python for Data Management, Finance, and Marketing Art Yudin, 2021-09-07 Learn how to gather, manipulate, and analyze data with Python. This book is a practical guide to help you get started with Python from ground zero and to the point where you can use coding for everyday tasks. Python, the most in-demand skill by employers, can be learned in a matter of months and a working knowledge will help you to advance your career. This book will teach you to crunch numbers, analyze big-data, and switch from spreadsheets to a faster and more efficient programming language. You'll benefit from the numerous real-life examples designed to meet current world challenges and from step-by-step guidance to become a confident Python user. Python is used in all aspects of financial industry, from algo trading, reporting and risk management to building valuations models and predictive machine learning programs. Basic Python for Data Management, Finance, and Marketing highlights how this language has become a useful skill with digital marketers, allowing them to analyze data more precisely and run more successful campaigns. What You'll Learn Get started with Python from square one Extend what's possible on excel with Python Automate tasks with Python Analyze data more precisely Who This Book Is For Professionals who want to find a job in the modern world or advance their careers within field of Python programming language. |
| data management in banking: Code of Federal Regulations, Title 12, Banks and Banking, PT. 200-219, Revised as of January 1, 2012 Office of the Federal Register (U.S.) Staff, 2012-03-16 |
| data management 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 management in banking: Code of Federal Regulations, Title 12, Banks and Banking, PT. 1-199, Revised as of January 1, 2010 Office of the Federal Register (U S ), 2010-04 The Code of Federal Regulations is a codification of the general and permanent rules published in the Federal Register by the Executive departments and agencies of the United States Federal Government. |
| data management in banking: Monetizing Data Management Peter Aiken, Juanita Billings, 2013-10 What’s the Return on Investment (ROI) on data management? Sound like an impossible question to answer? Not if you read this book and learn the value-added approach to managing enterprise resources and assets. This book defines the five interrelated best practices that comprise data management, and shows you how by example to successfully communicate data management ROI to senior management. The 17 cases we share will help you to identify opportunities to introduce data management into the strategic conversations that occur in the C-suite. You will gain a new perspective regarding the stewardship of your data assets and insulate your operations from the chaos, losses and risks that result from traditional approaches to technological projects. And you will learn how to protect yourself from legal challenges resulting from outsourced information technology projects gone badly due to incorrect project sequencing and focus. With the emerging acceptance and adoption of revised performance standards, your organization will be better prepared to face the coming big data deluge! The book contains four chapters: • Chapter 1 gives a somewhat unique perspective to the practice of leveraging data. We describe the motivations and delineate the specific challenges preventing most organizations from making substantial progress in this area. • Chapter 2 presents 11 cases where leveraging data has produced positive financial results that can be presented in language of immediate interest to C-level executives. To the degree possible, we have quantified the effect that data management has had in terms that will be meaningful to them also. • Chapter 3 describes five instances taken from the authors' experiences with various governmental defense departments. The lessons in this section however can be equally applied to many non-profit and non-defense governmental organizations. • Chapter 4 speaks specifically to the interaction of data management practices, in terms of both information technology projects and legal responsibilities. Reading it can help your organization avoid a number of perils, stay out of court and better vet contractors, experts and other helpers who play a role in organization information technology development. From John Bottega Foreword: Data is the new currency. Yes, an expression that is being used quite a bit of late, but it is very relevant in discussing the importance of data and the methodologies by which we manage it. And like any currency, how we manage it determines its true value. Like any currency, it can be managed wisely, or it can be managed foolishly. It can be put to good use, or it can be squandered away. The question is – what factors determine the path that we take? How do we properly manage this asset and realize its full value and potential? In Monetizing Data Management, Peter and Juanita explore the question of how to understand and place tangible value on data and data management. They explore this question through a series of examples and real-world use cases to exemplify how the true value of data can be realized. They show how bringing together business and technology, and applying a data-centric forensic approach can turn massive amounts of data into the tools needed to improve business processes, reduce costs, and better serve the customer. Data monetization is not about turning data into money. Instead, it’s about taking information and turning it into opportunity. It’s about the need to understand the real meaning of data in order to extract value from it. And it’s about achieving this objective through a partnership with business and technology. In Monetizing Data Management, the authors demonstrate how true value can be realized from our data through improved data centric approaches. |
| data management in banking: Management of Banking and Financial Services: Padmalatha Suresh, Justin Paul, Management of Banking and Financial Services focuses on the basic concepts of banking and financial services, and how these concepts are applied in the global banking environment as well as in India. In addition to presenting the big picture of the |
| data management in banking: Code of Federal Regulations, Title 12, Banks and Banking, PT. 300-499, Revised as of January 1, 2010 Office of the Federal Register (U S ), 2010-04 The Code of Federal Regulations is a codification of the general and permanent rules published in the Federal Register by the Executive departments and agencies of the United States Federal Government. |
| data management in banking: Smarter Modeling of IBM InfoSphere Master Data Management Solutions Jan-Bernd Bracht, Joerg Rehr, Markus Siebert, Rouven Thimm, IBM Redbooks, 2012-08-09 This IBM® Redbooks® publication presents a development approach for master data management projects, and in particular, those projects based on IBM InfoSphere® MDM Server. The target audience for this book includes Enterprise Architects, Information, Integration and Solution Architects and Designers, Developers, and Product Managers. Master data management combines a set of processes and tools that defines and manages the non-transactional data entities of an organization. Master data management can provide processes for collecting, consolidating, persisting, and distributing this data throughout an organization. IBM InfoSphere Master Data Management Server creates trusted views of master data that can improve applications and business processes. You can use it to gain control over business information by managing and maintaining a complete and accurate view of master data. You also can use InfoSphere MDM Server to extract maximum value from master data by centralizing multiple data domains. InfoSphere MDM Server provides a comprehensive set of prebuilt business services that support a full range of master data management functionality. |
| data management in banking: Legal Data for Banking Akber Datoo, 2019-04-26 A practical, informative guide to banks’ major weakness Legal Data for Banking defines the legal data domain in the context of financial institutions, and describes how banks can leverage these assets to optimise business lines and effectively manage risk. Legal data is at the heart of post-2009 regulatory reform, and practitioners need to deepen their grasp of legal data management in order to remain compliant with new rules focusing on transparency in trade and risk reporting. This book provides essential information for IT, project management and data governance leaders, with detailed discussion of current and best practices. Many banks are experiencing recurrent pain points related to legal data management issues, so clear explanations of the required processes, systems and strategic governance provide immediately-relevant relief. The recent financial crisis following the collapse of major banks had roots in poor risk data management, and the regulators’ unawareness of accumulated systemic risk stemming from contractual obligations between firms. To avoid repeating history, today’s banks must be proactive in legal data management; this book provides the critical knowledge practitioners need to put the necessary systems and practices in place. Learn how current legal data management practices are hurting banks Understand the systems, structures and strategies required to manage risk and optimise business lines Delve into the regulations surrounding risk aggregation, netting, collateral enforceability and more Gain practical insight on legal data technology, systems and migration The legal contracts between firms contain significant obligations that underpin the financial markets; failing to recognise these terms as valuable data assets means increased risk exposure and untapped business lines. Legal Data for Banking provides critical information for the banking industry, with actionable guidance for implementation. |
| data management 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 management in banking: Biomedical Research and Integrated Biobanking: An Innovative Paradigm for Heterogeneous Data Management Massimiliano Izzo, 2016-03-17 This doctoral thesis reports on an innovative data repository offering adaptive metadata management to maximise information sharing and comprehension in multidisciplinary and geographically distributed collaborations. It approaches metadata as a fluid, loosely structured and dynamical process rather than a fixed product, and describes the development of a novel data management platform based on a schemaless JSON data model, which represents the first fully JSON-based metadata repository designed for the biomedical sciences. Results obtained in various application scenarios (e.g. integrated biobanking, functional genomics and computational neuroscience) and corresponding performance tests are reported on in detail. Last but not least, the book offers a systematic overview of data platforms commonly used in the biomedical sciences, together with a fresh perspective on the role of and tools for data sharing and heterogeneous data integration in contemporary biomedical research. |
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 enable a …
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 to …
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 …
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 …
Belmont Forum Adopts Open Data Principles for Environme…
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 …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
