| data domains in financial services: 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 domains in financial services: The Data Model Resource Book, Volume 2 Len Silverston, 2001-03-21 A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful First Edition, while adding a companion volume to take care of more specific requirements of different businesses. Each volume is accompanied by a CD-ROM, which is sold separately. Each CD-ROM provides powerful design templates discussed in the books in a ready-to-use electronic format, allowing companies and individuals to develop the databases they need at a fraction of the cost and a third of the time it would take to build them from scratch. With each business function boasting its own directory, this CD-ROM provides a variety of data models for specific implementations in such areas as financial services, insurance, retail, healthcare, universities, and telecom. |
| data domains in financial services: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| data domains in financial services: Multi-Domain Master Data Management Mark Allen, Dalton Cervo, 2015-03-21 Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data. |
| data domains in financial services: Handbook of Financial Data and Risk Information II Margarita S. Brose, Mark D. Flood, Dilip Krishna, Bill Nichols, 2014-01-09 A comprehensive resource for understanding the issues involved in collecting, measuring and managing data in the financial services industry. |
| data domains in financial services: The Case for the Chief Data Officer Peter Aiken, Michael M. Gorman, 2013-04-22 Data are an organization's sole, non-depletable, non-degrading, durable asset. Engineered right, data's value increases over time because the added dimensions of time, geography, and precision. To achieve data's full organizational value, there must be dedicated individual to leverage data as assets - a Chief Data Officer or CDO who's three job pillars are: - Dedication solely to leveraging data assets, - Unconstrained by an IT project mindset, and - Reports directly to the business Once these three pillars are set into place, organizations can leverage their data assets. Data possesses properties worthy of additional investment. Many existing CDOs are fatally crippled, however, because they lack one or more of these three pillars. Often organizations have some or all pillars already in place but are not operating in a coordinated manner. The overall objective of this book is to present these pillars in an understandable way, why each is necessary (but insufficient), and what do to about it. - Uncovers that almost all organizations need sophisticated, comprehensive data management education and strategies. - Delivery of organization-wide data success requires a highly focused, full time Chief Data Officer. - Engineers organization-wide data advantage which enables success in the marketplace |
| data domains in financial services: 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 domains in financial services: Data Governance Ismael Caballero, Mario Piattini, 2024-01-28 This book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors. To this end, this book is composed of two parts. Part I on “Data Governance Fundamentals” begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on “Data Governance Applied” consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter. The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems. |
| data domains in financial services: 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 domains in financial services: Visualizing Financial Data Julie Rodriguez, Piotr Kaczmarek, 2016-05-02 A fresh take on financial data visualization for greater accuracy and understanding Your data provides a snapshot of the state of your business and is key to the success of your conversations, decisions, and communications. But all of that communication is lost — or incorrectly interpreted — without proper data visualizations that provide context and accurate representation of the numbers. In Visualizing Financial Data, authors Julie Rodriguez and Piotr Kaczmarek draw upon their understanding of information design and visual communication to show you how to turn your raw data into meaningful information. Coverage includes current conventions paired with innovative visualizations that cater to the unique requirements across financial domains, including investment management, financial accounting, regulatory reporting, sales, and marketing communications. Presented as a series of case studies, this highly visual guide presents problems and solutions in the context of real-world scenarios. With over 250 visualizations, you’ll have access to relevant examples that serve as a starting point to your implementations. • Expand the boundaries of data visualization conventions and learn new approaches to traditional charts and graphs • Optimize data communications that cater to you and your audience • Provide clarity to maximize understanding • Solve data presentation problems using efficient visualization techniques • Use the provided companion website to follow along with examples The companion website gives you the illustration files and the source data sets, and points you to the types of resources you need to get started. |
| data domains in financial services: Practical Data Analytics for BFSI Bharat Sikka, Dr. Priyender Yadav, Dr. Prashant Verma, 2023-09-02 Revolutionizing BFSI with Data Analytics KEY FEATURES ● Real-world examples and exercises will ground you in the practical application of analytics techniques specific to BFSI. ● Master Python for essential coding, SQL for data manipulation, and industry-leading tools like IBM SPSS and Power BI for sophisticated analyses. ● Understand how data-driven strategies generate profits, mitigate risks, and redefine customer support dynamics within the BFSI sphere. DESCRIPTION Are you looking to unlock the transformative potential of data analytics in the dynamic world of Banking, Financial Services, and Insurance (BFSI)? This book is your essential guide to mastering the intricate interplay of data science and analytics that underpins the BFSI landscape. Designed for intermediate-level practitioners, as well as those aspiring to join the ranks of BFSI analytics professionals, this book is your compass in the data-driven realm of banking. Address the unique challenges and opportunities of the BFSI sector using Artificial Intelligence and Machine Learning models for a data driven analysis. This book is a step by step guide to utilize tools like IBM SPSS and Microsoft Power BI. Hands-on examples that utilize Python and SQL programming languages make this an essential guide. The book features numerous case studies that illuminate various use cases of Analytics in BFSI. Each chapter is enriched with practical insights and concludes with a valuable multiple-choice questionnaire, reinforcing understanding and engagement. This book will uncover how these solutions not only pave the way for increased profitability but also navigate risks with precision and elevate customer support to unparalleled heights. WHAT WILL YOU LEARN ● Delve into the world of Data Science, including Artificial Intelligence and Machine Learning, with a focus on their application within BFSI. ● Explore hands-on examples and step-by-step tutorials that provide practical solutions to real-world challenges faced by banking institutions. ● Develop skills in essential programming languages such as Python (fundamentals) and SQL (intermediate), crucial for effective data manipulation and analysis. ● Gain insights into how businesses adapt data-driven strategies to make informed decisions, leading to improved operational efficiency. ● Stay updated on emerging trends, technologies, and innovations shaping the future of data analytics in the BFSI industry. WHO IS THIS BOOK FOR? This book is tailored for professionals already engaged in or seeking roles within Data Analytics in the BFSI industry. Additionally, it serves as a strategic resource for business leaders and upper management, guiding them in shaping data platforms and products within their organizations. The book also serves as a starting point for individuals interested in the BFSI sector. Prior experience with coding tools such as Python, SQL, Power BI is beneficial but not required as it covers all dimensions from the basics. TABLE OF CONTENTS 1. Introduction to BFSI and Data Driven Banking 2. Introduction to Analytics and Data Science 3. Major Areas of Analytics Utilization 4. Understanding Infrastructures behind BFSI for Analytics 5. Data Governance and AI/ML Model Governance in BFSI 6. Domains of BFSI and team planning 7. Customer Demographic Analysis and Customer Segmentation 8. Text Mining and Social Media Analytics 9. Lead Generation Through Analytical Reasoning and Machine Learning 10. Cross Sell and Up Sell of Products through Machine Learning 11. Pricing Optimization 12. Data Envelopment Analysis 13. ATM Cash Forecasting 14. Unstructured Data Analytics 15. Fraud Modelling 16. Detection of Money Laundering and Analysis 17. Credit Risk and Stressed Assets 18. High Performance Architectures: On-Premises and Cloud 19. Growing Trends in the Data-Driven Future of BFSI |
| data domains in financial services: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector. |
| data domains in financial services: Handbook of Financial Data and Risk Information I Margarita S. Brose, Mark D. Flood, Dilip Krishna, Bill Nichols, 2014 Volume I examines the business and regulatory context that makes risk information so important. A vast set of quantitative techniques, internal risk measurement and governance processes, and supervisory reporting rules have grown up over time, all with important implications for modeling and managing risk information. Without an understanding of the broader forces at work, it is all too easy to get lost in the details. -- Back cover. |
| data domains in financial services: Financial Services and General Government Appropriations for 2009 United States. Congress. House. Committee on Appropriations. Subcommittee on Financial Services and General Government, 2008 |
| data domains in financial services: From Opinion Mining to Financial Argument Mining Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen, Xinxi Chen, 2021 Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions. |
| data domains in financial services: BIAN Data Architecture & Design Specialist Courseware Laleh Rafati, Rene Vleeschauwer, 2022-10-25 The BIAN Data Architecture & Design Specialist exam leads to the official BIAN Data Architecture & Design Specialist Certification by the Banking Industry Architecture Network. It is carried out by Van Haren Learning Solutions. The BIAN Data Architecture & Design Specialist Certification exam tests the training participant’s knowledge of the approach used to create and manage the BIAN Reference Data Architecture for the Financial Industry. t tests their ability to describe the added value this model can provide for the Financial Industry and its service providers. By successfully passing the BIAN Data Architecture & Design Specialist Exam, participants will achieve the BIAN Data Architecture & Design Specialist level certification which assures they have been audited and have successfully mastered the required BIAN Data Architecture & Design Specialist level. The BIAN Data Architecture & Design Specialist Certification level includes the knowledge and understanding of the general design principles and elements of BIAN’s Reference Architecture for the Financial Industry. But the main objective is to understand and be able to apply BIAN’s approach to creating and managing its Business Object Model. This includes an understanding of the documentation conventions in the ArchiMate and UML languages, used to represent and manage the BIAN Business Object Model as a Reference Data Architecture for the Financial Industry. It includes an understanding of, and the ability to apply, the Business Object Modeling approach and its Patterns. The BIAN Data Architecture & Design Specialist Certification level includes the ability to describe the added value the BIAN Object Model, as an enterprise data model, can provide to the financial industry and its service providers. The BIAN certification exam is intended for professionals in the financial services industry such as: data architects and data modelers at both enterprise and solution level as well as consultants and service providers that operate in the financial services industry. Key Benefits of the BIAN Data Architecture & Design Specialist Certification It enables data professionals to leverage the benefits of BIAN and the BIAN BOM It increases the knowledge and general skills of professionals regarding data modeling, the BIAN BOM and Control Records and enables the creation of a more effective and transparent data architecture. It provides professionals and their organizations with a competitive advantage. It is a hallmark for the professionalism of banking professionals and banking architects active with data. Number of questions: 60 Duration(minute) of exam: 60 min. Pass Mark –: 70% Open/Closed book: closed Language: English Paper based & online availability: online availability |
| data domains in financial services: Next Generation Data Centers in Financial Services Tony Bishop, 2009-09-02 Financial markets are witnessing an unprecedented explosion in the availability of data, and the firms that survive will be able to leverage this information to increase their profit and expand their opportunities in a global world. Financial firms have two options: to build their own data centers or to outsource them to hosting services such as Google and Amazon ‘cloud’ services. While outsourcing data centers is a trend for small firms, it is not applicable to bigger firms who want more control over their huge amounts of data. Large firms thus build their own data centers. In such an environment, the CIO’s ability is crucial to lead an effective data strategy to capture, process and connect data to all the relevant lines of business. At the core of this strategy lies the data center – the repository of all information. In recognition of the importance of information, firms are rushing to invest in data centers, but they are finding that just throwing technology at the problem is not good enough. Despite the investments, data centers prove frustrating in terms of inefficiencies and rising costs, directly cutting into the profitability of lines of business that they serve. While there are books that discuss the mechanics, hardware and technicalities of data centers, no book has yet made the connection between enterprise strategy and data center investment, design and management. This book is a solution driven book for management demonstrating how to leverage technology to manage the seemingly infinite amount of data available today. Each chapter offers cutting-edge management and technology solutions to effectively manage data through data centers. • Feature: Presents cutting-edge technology solutions not available in one place until now • Benefit: Saves time going to numerous websites, calling vendors, going to conferences • Feature: Includes step-by-step instructions on how to implement a data center strategy based on the author’s recent success with Wachovia’s data center • Benefit: Readers can follow these steps with confidence that they will work and not have to re-invent the wheel • Feature: Demonstrates how business and IT can be aligned in financial services • Benefit: Demonstrating this alignment is crucial for any proposal for IT related resources today |
| data domains in financial services: Linked Data adoption and application within financial business processes Kathrin Kalcheva, 2015-10-07 This book is the first part of a two book series. It is based on a combination of Finance and IT. More precisely, it applies the concept of Linked Data (hence LD), which originates from the IT landscape, to the specifics of the financial world. LD is a new concept for efficient handling of data, which could be used for dealing with a complex data set and data structures, as well as Big Data. The focus of this book is on the adoption of LD and its application within financial business processes. First, LD is briefly explained and framed in the context of the financial services domain. Second, modeling the determinants of LD adoption needed a clear statement over its advantages and disadvantages, amongst others within the financial domain. Despite the high interest towards the LD concept, no such overview existed before this work. Fourth, the model on LD adoption is applied to business (financial) reporting, illustrated with the XBRL case. Finally, semi-structured interviews with financial experts reconfirm and extend the findings. The main potentials are described in detail. |
| data domains in financial services: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management. |
| data domains in financial services: Cybersecurity and Data Protection in the Financial Sector United States. Congress. Senate. Committee on Banking, Housing, and Urban Affairs, 2012 |
| data domains in financial services: Handbook of Technology in Financial Services Jessica Keyes, 1998-12-18 The calculus of IT support for the banking, securities and insurance industries has changed dramatically and rapidly over the past few years. Unheard of just a few years ago, corporate intranets are now used for everything from job postings to enhanced team communications. Whole new departments are being created to support e-commerce. And the Internet/Intranet/Extranet triple-whammy is the most critical component of most financial IT shops. At the same time, intelligent agents stand ready to take on such diverse functions as customer profiling and data mining. |
| data domains in financial services: Encyclopedia of Data Warehousing and Mining Wang, John, 2005-06-30 Data Warehousing and Mining (DWM) is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead. The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining (DWM). This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of DWM in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of DWM. |
| data domains in financial services: 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 domains in financial services: Data Mining Mehmed Kantardzic, 2019-10-23 Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field. |
| data domains in financial services: Secure Semantic Service-Oriented Systems Bhavani Thuraisingham, 2010-12-14 As the demand for data and information management continues to grow, so does the need to maintain and improve the security of databases, applications, and information systems. In order to effectively protect this data against evolving threats, an up-to-date understanding of the mechanisms for securing semantic Web technologies is essential. Reviewi |
| data domains in financial services: Industry 4.0 Technologies for Business Excellence Shivani Bali, Sugandha Aggarwal, Sunil Sharma, 2021-12-31 This book captures deploying Industry 4.0 technologies for business excellence and moving towards Society 5.0. It addresses applications of Industry 4.0 in the areas of marketing, operations, supply chain, finance, and HR to achieve business excellence. Industry 4.0 Technologies for Business Excellence: Frameworks, Practices, and Applications focuses on the use of AI in management across different sectors. It explores the benefits through a human-centered approach to resolving social problems by integrating cyberspace and physical space. It discusses the framework for moving towards Society 5.0 and keeping a balance between economic and social gains. This book brings together researchers, developers, practitioners, and users interested in exploring new ideas, techniques, and tools and exchanging their experiences to provide the most recent information on Industry 4.0 applications in the field of business excellence. Graduate or postgraduate students, professionals, and researchers in the fields of operations management, manufacturing, healthcare, supply chain, marketing, finance, and HR will find this book full of new ideas, techniques, and tools related to Industry 4.0. |
| data domains in financial services: Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents Kwong S. Leung, Lai-wan Chan, Helen Meng, 2003-07-31 X Table of Contents Table of Contents XI XII Table of Contents Table of Contents XIII XIV Table of Contents Table of Contents XV XVI Table of Contents K.S. Leung, L.-W. Chan, and H. Meng (Eds.): IDEAL 2000, LNCS 1983, pp. 3›8, 2000. Springer-Verlag Berlin Heidelberg 2000 4 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 5 6 J. Sinkkonen and S. Kaski Clustering by Similarity in an Auxiliary Space 7 0.6 1.5 0.4 1 0.2 0.5 0 0 10 100 1000 10000 10 100 1000 Mutual information (bits) Mutual information (bits) 8 J. Sinkkonen and S. Kaski 20 10 0 0.1 0.3 0.5 0.7 Mutual information (mbits) Analyses on the Generalised Lotto-Type Competitive Learning Andrew Luk St B&P Neural Investments Pty Limited, Australia Abstract, In generalised lotto-type competitive learning algorithm more than one winner exist. The winners are divided into a number of tiers (or divisions), with each tier being rewarded differently. All the losers are penalised (which can be equally or differently). In order to study the various properties of the generalised lotto-type competitive learning, a set of equations, which governs its operations, is formulated. This is then used to analyse the stability and other dynamic properties of the generalised lotto-type competitive learning. |
| data domains in financial services: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-18 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| data domains in financial services: Data Analytics and Digital Transformation Erik Beulen, Marla A. Dans, 2023-12-01 Understanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies. |
| data domains in financial services: Open Banking Linda Jeng, 2022-01-07 Open banking is a silent revolution transforming the banking industry. It is the manifestation of the revolution of consumer technology in banking and will dramatically change not only how we bank, but also the world of finance and how we interact with it. Since the United Kingdom along with the rest of the European Union adopted rules requiring banks to share customer data to improve competition in the banking sector, a wave of countries from Asia to Africa to the Americas have adopted various forms of their own open banking regimes. Among Basel Committee jurisdictions, at least fifteen jurisdictions have some form of open banking, and this number does not even include the many jurisdictions outside the Basel Committee membership with open banking activities. Although U.S. banks and market participants have been sharing customer-permissioned data for the past twenty years and there have been recent policy discussions, such as the Obama administration's failed Consumer Data Privacy Bill and the Data Aggregation Principles of the Consumer Financial Protection Bureau, open banking is still a little-known concept among consumers and policymakers in the States. This book defines the concept of 'open banking' and explores key legal, policy, and economic questions raised by open banking. |
| data domains in financial services: Beautiful Data Toby Segaran, Jeff Hammerbacher, 2009-07-14 In this insightful book, you'll learn from the best data practitioners in the field just how wide-ranging -- and beautiful -- working with data can be. Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video. With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include: Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran |
| data domains in financial services: Safeguarding Financial Data in the Digital Age Naz, Farah, Karim, Sitara, 2024-07-22 Despite advancements in cybersecurity measures, the financial sector continues to grapple with data breaches, fraud, and privacy concerns. Traditional security measures are often insufficient to combat sophisticated cyber threats, leading to financial losses, reputational damage, and regulatory non-compliance. Moreover, the rapid pace of technological change makes it challenging for organizations to keep up with emerging threats and implement effective data protection strategies. This calls for a proactive and multidisciplinary approach to address financial data security's complex and evolving landscape. Safeguarding Financial Data in the Digital Age offers a timely and comprehensive solution to the challenges faced by the financial sector in securing sensitive information. By bringing together insights from finance, cybersecurity, and technology, this book provides a holistic understanding of the threats and opportunities in financial data security. It equips academics, industry professionals, policymakers, and students with the knowledge and tools needed to enhance financial data protection measures through detailed analyses, case studies, and practical recommendations. By fostering collaboration and knowledge exchange, this book serves as a valuable resource for shaping the future of financial data security in the digital age. |
| data domains in financial services: Open Government: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2019-09-06 Open government initiatives have become a defining goal for public administrators around the world. As technology and social media tools become more integrated into society, they provide important frameworks for online government and community collaboration. However, progress is still necessary to create a method of evaluation for online governing systems for effective political management worldwide. Open Government: Concepts, Methodologies, Tools, and Applications is a vital reference source that explores the use of open government initiatives and systems in the executive, legislative, and judiciary sectors. It also examines the use of technology in creating a more affordable, participatory, and transparent public-sector management models for greater citizen and community involvement in public affairs. Highlighting a range of topics such as data transparency, collaborative governance, and bureaucratic secrecy, this multi-volume book is ideally designed for government officials, leaders, practitioners, policymakers, researchers, and academicians seeking current research on open government initiatives. |
| data domains in financial services: Big Data Analytics Anirban Mondal, Himanshu Gupta, Jaideep Srivastava, P. Krishna Reddy, D.V.L.N. Somayajulu, 2018-12-11 This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India, in December 2018. The 29 papers presented in this volume were carefully reviewed and selected from 93 submissions. The papers are organized in topical sections named: big data analytics: vision and perspectives; financial data analytics and data streams; web and social media data; big data systems and frameworks; predictive analytics in healthcare and agricultural domains; and machine learning and pattern mining. |
| data domains in financial services: BIAN Foundation Certification Courseware Ingrid Stap, Raymond Slot, 2019-09-09 Besides the BIAN Foundation Certification Courseware (ISBN: 9789401804721) publication you are advised to obtain the publication BIAN Edition 2019 – A framework for the financial services industry (ISBN: 9789401803151). This is the official courseware for accredited BIAN Foundation training. Intended for professionals that aim to pass the BIAN Foundation Exam. The BIAN Foundation Exam is the exam for official BIAN Foundation level certification by the Banking Industry Architecture Network and is carried out by Van Haren Learning Solutions. The BIAN Foundation Certification Exam is to test the delegate’s knowledge about the BIAN standard. This is the common framework for banking interoperability issues. By successfully passing the BIAN Foundation Exam delegates will achieve the BIAN Foundation level certification which ensures that they have been audited and have successfully mastered the required BIAN Foundation level. This includes their ability to describe and recognize certain knowledge about BIAN such as: the benefits BIAN provides to financial services providers, the BIAN design principles and artefacts, the abilities to reduce integration cost and maximize interoperability. The BIAN certification exam is intended for professionals in the financial services industry such as: enterprise- and solution architects, consultants, that all operate in the financial services industry. Key Benefits • This certification qualifies professionals and demonstrates their knowledge of BIAN • It is necessary for professionals to have this basic knowledge in order to model a bank to the BIAN standard • It qualifies professionals in the financial services industry to demonstrate that they • have the proper understanding of the standards in architecture required for financial organizations. Number of questions: 60 Duration(minute) of exam: 60 min. Pass Mark – X% (X marks): 70% Open/Closed book: closed Language: English Paper based & online availability: online availability |
| data domains in financial services: Enhancing Data Security United States. Congress. House. Committee on Financial Services. Subcommittee on Financial Institutions and Consumer Credit, 2006 |
| data domains in financial services: Digital Innovation in Financial Services Phoebus L. Athanassiou, 2016-04-24 Consumer behaviour is rapidly trending towards the use of digital devices as instruments through which to transact day-to-day business. This original and timely book shows how this trend creates new opportunities not only for retail consumers but also for financial service providers, regulators and central banks. The author offers a comprehensive overview of these opportunities and their countervailing legal and regulatory challenges. The author describes and analyses in unprecedented detail the application of digital financial innovation (FinTech), and some of its core manifestations, including virtual currencies, Blockchain and distributed ledger technologies to the delivery of financial services, in areas such as: – payments; – securities clearing and settlement; – central banking; – real-time access to financial information; – instant completion of core financial transactions; – data validation and reconciliation processes; and – digital contracting (smart contracts). Also clarified are the legal and other barriers to be overcome – including cybersecurity and risks to privacy – before any widespread adoption of digital innovation in the highly regulated financial sector context can occur. As an informed assessment of the legal merits and risks of technological innovation for financial service providers and central banks, and as a contribution to establishing a conceptual framework within which to analyse and better understand the applications of digital innovation to the financial sector, this practical work is bound to be welcomed by legal practitioners and legal scholars alike with an interest in financial services. Policymakers and regulators will also appreciate its guidance on how to temper the less benevolent aspects of FinTech with targeted, risk-focused regulation, so as to promote innovation and preserve the potential benefits for financial markets and their participants alike. |
| data domains in financial services: Data Warehousing in the Age of Big Data Krish Krishnan, 2013-05-02 Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. - Learn how to leverage Big Data by effectively integrating it into your data warehouse. - Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies - Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements |
| data domains in financial services: 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 domains in financial services: Banking Technology Handbook Jessica Keyes, 1998-12-29 This desk reference for IT professionals in the banking industry provides information about the latest technologies to improve efficiency and security. Topics include imaging electronic exchange Internet-based technologies other automating systems issues affecting all financial service sectors, such as the year 2000 problem Banking Technology Handbook is geared toward all levels of technology management and financial services management responsible for developing and implementing cutting-edge technology. |
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 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 …
