Data Analytics For Management Banking And Finance
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Data Analytics For Management Banking And Finance
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Author : Foued Saâdaoui
language : en
Publisher: Springer Nature
Release Date : 2023-09-19
Data Analytics For Management Banking And Finance written by Foued Saâdaoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Business & Economics categories.
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
Financial Statistics And Data Analytics
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Author : Shuangzhe Li
language : en
Publisher: MDPI
Release Date : 2021-03-02
Financial Statistics And Data Analytics written by Shuangzhe Li and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-02 with Business & Economics categories.
Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.
The Digital Journey Of Banking And Insurance Volume Iii
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Author : Volker Liermann
language : en
Publisher: Springer Nature
Release Date : 2021-10-27
The Digital Journey Of Banking And Insurance Volume Iii written by Volker Liermann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-27 with Business & Economics categories.
This book, the third one of three volumes, focuses on data and the actions around data, like storage and processing. The angle shifts over the volumes from a business-driven approach in “Disruption and DNA” to a strong technical focus in “Data Storage, Processing and Analysis”, leaving “Digitalization and Machine Learning Applications” with the business and technical aspects in-between. In the last volume of the series, “Data Storage, Processing and Analysis”, the shifts in the way we deal with data are addressed.
Financial Data Analytics With Machine Learning Optimization And Statistics
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Author : Yongzhao Chen
language : en
Publisher: Wiley
Release Date : 2023-06-06
Financial Data Analytics With Machine Learning Optimization And Statistics written by Yongzhao Chen and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-06 with Business & Economics categories.
An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
A Primer In Financial Data Management
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Author : Martijn Groot
language : en
Publisher: Academic Press
Release Date : 2017-05-10
A Primer In Financial Data Management written by Martijn Groot and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-10 with Technology & Engineering categories.
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
Financial Statistics And Data Analytics
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Author : Shuangzhe Liu
language : en
Publisher:
Release Date : 2021
Financial Statistics And Data Analytics written by Shuangzhe Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.
Business Analytics And Business Intelligence Machine Learning Model To Predict Bank Loan Defaults
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Author : dr. V.V.L.N. Sastry
language : en
Publisher: Idea Publishing
Release Date : 2020-05-29
Business Analytics And Business Intelligence Machine Learning Model To Predict Bank Loan Defaults written by dr. V.V.L.N. Sastry and has been published by Idea Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-29 with Computers categories.
Predictive Analytics offers a unique opportunity to identify future trends and allows organizations to act upon them. In this book we are dealing with ‘loan default’ which is always a threat to banks and financial institutions and should be predicted in advance based on various features of the borrowers or applicants. In this book we aim at applying machine learning models to classify the borrowers with and without loan default from a group of predicting variables and evaluate their performance. As a part of building a model to predict loan default, we have submitted in detail the introduction of the problem, exploratory data analysis (EDA), data cleaning and pre-processing, model building, interpretation, model tuning, model validation, and final interpretation & recommendations. Under the current project of loan default forming part of predictive analytics of business analytics and intelligence, we have studied research-based review parameters in detail which have also been annexed for ready reference as Annexure I. Data dictionary has been annexed as Annexure-2. R. Code for the same is provided at the URL which can be downloaded from www.drvvlnsastry.com/businessanalytics/data The study finds out that logistic regression is the best model to classify those applicants with loan default.
Business Analytics
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Author : Dinabandhu Bag
language : en
Publisher: Taylor & Francis
Release Date : 2016-11-10
Business Analytics written by Dinabandhu Bag and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Business & Economics categories.
This book provides a first-hand account of business analytics and its implementation, and an account of the brief theoretical framework underpinning each component of business analytics. The themes of the book include (1) learning the contours and boundaries of business analytics which are in scope; (2) understanding the organization design aspects of an analytical organization; (3) providing knowledge on the domain focus of developing business activities for financial impact in functional analysis; and (4) deriving a whole gamut of business use cases in a variety of situations to apply the techniques. The book gives a complete, insightful understanding of developing and implementing analytical solution.
Data Analytics For Corporate Debt Markets
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Author : Robert S. Kricheff
language : en
Publisher: Pearson Education
Release Date : 2014-01-23
Data Analytics For Corporate Debt Markets written by Robert S. Kricheff and has been published by Pearson Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-23 with Business & Economics categories.
Use state-of-the-art data analytics to optimize your evaluation and selection of corporate debt investments. Data Analytics for Corporate Debt Markets introduces the most valuable data analytics tools, methods, and applications for today's corporate debt market. Robert Kricheff shows how data analytics can improve and accelerate the process of proper investment selection, and guides market participants in focusing their credit work. Kricheff demonstrates how to use analytics to position yourself for the future; to assess how your current portfolio or trading desk is currently positioned relative to the marketplace; and to pinpoint which part of your holdings impacted past performance. He outlines how analytics can be used to compare markets, develop investment themes, and select debt issues that fit (or do not fit) those themes. He also demonstrates how investors seek to analyze short term supply and demand, and covers some special parts of the market that utilize analytics. For all corporate debt portfolio managers, traders, analysts, marketers, investment bankers, and others who work with structured financial products.
Financial Analysis And Risk Management
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Author : Victoria Lemieux
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-20
Financial Analysis And Risk Management written by Victoria Lemieux and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-20 with Business & Economics categories.
The Global Financial Crisis and the Eurozone crisis that has followed have drawn attention to weaknesses in financial records, information and data. These weaknesses have led to operational risks in financial institutions, flawed bankruptcy and foreclosure proceedings following the Crisis, and inadequacies in financial supervisors’ access to records and information for the purposes of a prudential response. Research is needed to identify the practices that will provide the records, information and data needed to support more effective financial analysis and risk management. The unique contribution of this volume is in bringing together researchers in distinct domains that seldom interact to identify theoretical, technological, policy and practical issues related to the management of financial records, information and data. The book will, therefore, appeal to researchers or advanced practitioners in the field of finance and those with an interest in risk management, computer science, cognitive science, sociology, management information systems, information science, and archival science as applied to the financial domain.