DAMBF-Book2022: Data Analytics for Management, Banking and Finance |
| Submission link | https://easychair.org/conferences/?conf=dambfbook2022 |
| Poster | download |
| Abstract registration deadline | September 15, 2022 |
| Submission deadline | November 15, 2022 |
CALL FOR BOOK CHAPTERS
Title: Data Analytics for Management, Banking and Finance
Over the past few decades, the world has experienced many social, economic and evenhealth upheavals. On the financial level, we have experienced the two worst global financialcrises since the Great Depression in the 1930s. At the heart of this turbulence, contagions andrepercussions also began to cross the boundaries and gradually creep into the local bankingand financial systems. After each crisis, we noticed that the majority of decision supportmodels inherited from classical econometers were in fact powerless in the face of marketdynamics, which each time showed new forms and geometries of fluctuations. The alternativeto these models has been intelligent algorithmic techniques. The birth of discrete andcombinatorial mathematics and their reinforcement with a considerable numerical potentialhas generated much more powerful machine learning approaches. Nowadays, these methodsare increasingly introduced into different disciplines, especially in pattern recognition, datamining and expert systems. They are also used in several banking and financial applications,such as in banking marketing, fraud detection, governance, stock market forecasting, etc.Thanks to its flexibility and ease of implementation, this new generation of models has provento be a powerful tool driving a variety of business analyses.
This book is expected to be a concept and practical guidance on the use of various dataanalytics and visualization techniques and tools in the managerial, banking and financialsectors. It will especially focus on how combining expertise from interdisciplinary areas, suchas machine learning and business analytics, can bring forward a shared vision on the benefitsof data science from the research point of view to the evaluation of policies. It highlights howdata science is reshaping the business sector. It includes examples of novel big data sourcesand some successful applications on the use of advanced machine learning, natural languageprocessing, networks analysis, and time series analysis and forecasting, among others, in thebanking and finance. The book is also expected to include several case studies whereinnovative data science models could have been used to analyse, test or model some crucialphenomena 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 theycan reach their full potential and support policy-makers and the related stakeholders in thetransformational recovery of our societies.The book should provide a practical and relevant material not only for stakeholdersinvolved in research and innovation in the managerial, banking and financial sectors, but alsothose in the fields of computing, IT and managerial information systems, helping through thisnew theory to better specify the new opportunities and challenges. The many real casesaddressed in this book are also supposed to provide a detailed guide allowing the reader torealize the latest methodological discoveries and the use of the different Machine Learningapproaches (supervised, unsupervised, reinforcement, deep, etc.) and to learn how to use andevaluate performance of new data science tools and frameworks.
This book solicits contributions from researchers as well as practitioners with interests in the area of Data Analytics for Management, Banking and Finance. Submissions are invited on topics from the following non-comprehensive list:
- Big Data in the Banking Industry
- IoT in Banking and Financial Services
- Expert Systems in Banking and Insurance
- AI-Based Fraud Detection in Banking
- Behavioural Analytics on Banking Industry
- Big Data Analytics in the Fintech Industry
- Machine Learning in Banking/Finance
- Data Mining in Banking/Finance
- Metaheuristics for Portfolio Optimization
- Credit Risk Analysis with Machine Learning
- Forecasting Financial Time Series
- Chaotic/Multifractal Financial Time Series
- Nonlinear Causality in Financial data
- Electricity Prices and Power Derivatives
- Economic/Financial Data Fusion
- NLP in Financial Services
- Smart Data Analytics for Banking
- Multiresolution Analysis of Financial Data
- Supply Chain Analytics
Submission Guidelines
• All manuscripts should be submitted with good English and clear phrasing.
• Chapters are expected to be self-contained and may be one of the following:
- Original Works: Describe original work in an area of interest within the scope of the book.
- Experiments: Experiments addressing analytics scenarios within the book’s scope.
- Review/Survey: Articles that offer a review of recent work in an emerging direction of interest.
- Expanded versions of work published in premier data analytics avenues are also welcome, but should have at least 50% new content that is clearly identified.
• It is especially important that you use the following Springer book template:
- LATEX: https://resource-cms.springernature.com/springer-cms/rest/v1/content/20568/data/v10
- Word: https://resource-cms.springernature.com/springer-cms/rest/v1/content/3318/data/v5
Important Dates
Submission of abstracts: September 15, 2022
Notification of initial editorial decisions: September 30, 2022
Submission of full-length chapters: November 15, 2022
Notification of final editorial decisions: December 01, 2022
Submission of revised chapters: January 15, 2023
Editorial Committees
The main point of contact for any queries are the editors of this volume:
- Prof. Foued SAADAOUI, King Abdulaziz University, KSA. fsadawi@kau.edu.sa
- Dr Hana RABBOUCH, University of Tunis, TUNISIA, hana_rabbouch@alkawthar.edu.sa
- Prof. Yichuan ZHAO, Georgia State University, Atlanta, USA, yichuan@gsu.edu
Contact
Please write to us for any queries or if you are unsure whether your work is likely to be relevant to this book.