Table Of ContentArtificial Intelligence and Big Data
for Financial Risk Management
This book presents a collection of high-quality contributions on the
state-of-the-art in artificial intelligence and big data analysis as it relates
to financial risk management applications. It brings together, in one place,
the latest thinking on an emerging topic and includes principles, reviews,
examples and research directions. The book presents numerous specific use-
cases throughout, showing practical applications of the concepts discussed.
It looks at technologies such as eye movement analysis, data mining or
mobile apps and examines how these technologies are applied by financial
institutions and how this affects both the institutions and the market.
This work introduces students and aspiring practitioners to the subject of
risk management in a structured manner. It is primarily aimed at research-
ers and students in finance and intelligent big data applications, such as
intelligent information systems, smart economics and finance applications
and the internet of things in a marketing environment.
Noura Metawa is Assistant Professor of Finance at the College of Business
Administration, University of Sharjah, Sharjah, UAE, and at the Faculty of
Commerce, Mansoura University, Egypt.
M. Kabir Hassan is Professor of Finance in the Department of Economics
and Finance at the University of New Orleans, Louisiana, USA.
Saad Metawa is Professor of Finance at the Faculty of Commerce, Mansoura
University, Dakahliya, Egypt.
Banking, Money and International Finance
25 Inflation Targeting and Central Banks
Institutional Set-ups and Monetary Policy Effectiveness
Joanna Niedźwiedzińska
26 Global Stock Market Development
Quantitative and Behavioural Analysis
Marcin Kalinowski
27 The Digitalization of Financial Markets
The Socioeconomic Impact of Financial Technologies
Edited by Adam Marszk and Ewa Lechman
28 The Digital Disruption of Financial Services
International Perspectives
Edited by Ewa Lechman and Adam Marszk
29 The Economics and Finance of Commodity Price Shocks
Mikidadu Mohammed
30 Digital Finance and the Future of the Global Financial System
Disruption and Innovation in Financial Services
Edited by Lech Gąsiorkiewicz and Jan Monkiewicz
31 Artificial Intelligence and Big Data for Financial Risk Management
Intelligent Applications
Edited by Noura Metawa, M. Kabir Hassan and Saad Metawa
For more information about this series, please visit: www.routledge.com/
series/BMIF
Artificial Intelligence and
Big Data for Financial Risk
Management
Intelligent Applications
Edited by
Noura Metawa, M. Kabir Hassan
and Saad Metawa
First published 2023
by Routledge
4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
605 Third Avenue, New York, NY 10158
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2023 selection and editorial matter, Noura Metawa, M. Kabir
Hassan and Saad Metawa; individual chapters, the contributors
The right of Noura Metawa, M. Kabir Hassan and Saad Metawa
to be identified as the authors of the editorial material, and of
the authors for their individual chapters, has been asserted in
accordance with sections 77 and 78 of the Copyright, Designs and
Patents Act 1988.
All rights reserved. No part of this book may be reprinted or
reproduced or utilised in any form or by any electronic, mechanical,
or other means, now known or hereafter invented, including
photocopying and recording, or in any information storage or
retrieval system, without permission in writing from the publishers.
Trademark notice: Product or corporate names may be trademarks
or registered trademarks, and are used only for identification and
explanation without intent to infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
A catalog record has been requested for this book
ISBN: 978-0-367-70056-0 (hbk)
ISBN: 978-0-367-70058-4 (pbk)
ISBN: 978-1-003-14441-0 (ebk)
DOI: 10.4324/9781003144410
Typeset in Times New Roman
by codeMantra
Contents
List of figures vii
List of tables ix
List of contributors xi
1 Grey Model as a Tool in Dynamic Portfolio Selection: Simple
Applications 1
TIHANA ŠKRINJARIĆ
2 Predicting Financial Statement Fraud Using Artificial Neural
Networks 17
AISHATH SHAIHA SHAREEF AND GEETHA A. RUBASUNDRAM
3 Bank Network Credit Model and Risk Management System
Based on Big Data Technology 36
TIELIN HE
4 Deep Learning in Detecting Financial Statement Fraud: An
Application of Deep Neural Network (DNN) 45
SANTHA MUNIANDY, NOWSHATH K. BATCHA AND
GEETHA A. RUBASUNDRAM
5 Predicting Stock Return Risk and Volatility Using Neural
Network: The Case of the Egyptian Stock
Exchange 67
MAHA METAWEA, SAAD METAWA AND NOURA METAWA
6 Operation Analysis of Financial Sharing Center Based on Big
Data Sharing Technology: Taking SF Express as an Example 83
CHENGWEI ZHANG, GE GUO, WEIQI RAO AND XINYAN LI
vi Contents
7 Optimization Algorithms for Multiple-Asset Portfolios with
Machine Learning Techniques: Theoretical Foundations of
Optimum and Coherent Economic Capital Structures 92
MAZIN A. M. AL JANABI
8 Random Forest and Grey Methodology in Dynamic Portfolio
Selection 123
TIHANA ŠKRINJARIĆ AND SILVIJA VLAH JERIĆ
9 The Role of Blockchain in Financial Applications:
Architecture, Benefit, and Challenges 140
AHMED A. ELNGAR, MOHAMMED KAYED AND
HOSNY H. ABO EMIRA
10 Using Computer Blockchain Technology to Analyze the
Development Trend of China’s Modern Financial Industry 160
ZHENXING BIAN
11 Financial Efficiency Differentiation Based on Data
Quantitative Analysis under Big Data Technology 169
YICHEN SONG
12 Optimization Algorithms for Multiple-Asset Portfolios with
Machine Learning Techniques: Practical Applications with
Forecasting of Optimum and Coherent Economic Capital Structures 179
MAZIN A. M. AL JANABI
13 An Overview of Neural Network in Financial
Risk Management 214
MAHA METAWEA, SAAD METAWA AND NOURA METAWA
Index 227
Figures
1.1 CROBEX value over time (black line, left axis), return on
CROBEX (grey line, right axis) 6
1.2 Out-of-sample forecasts of three approaches from Table 1.2 7
1.3 Estimation error for out-of-sample forecasts of three
approaches in Figure 1.2 8
1.4 Simulated portfolio values for all strategies, no transaction costs 9
1.5 Simulated portfolio values for all strategies, included
transaction costs 10
2.1 Architecture of a multi-layer feed-forward neural network 20
2.2 Process for establishment of the ANN model 23
2.3 Partitioning of training and testing dataset 27
2.4 Architecture of the multilayer feed forward neural network 28
2.5 Network information 29
2.6 Classification of ANN 29
2.7 Overall percent correct 30
3.1 Changes in total deposits and loans of Bank A
in the past four years 42
3.2 Non-performing loan ratio in the past four years 43
4.1 Research framework 49
4.2 DNN model data flow 50
4.3 MySQL data tables list 52
4.4 Main variables (Financial Ratios and FRIs) 52
4.5 DNN architecture for FSF and NFSF detection and prediction 57
5.1 The study model 71
5.2 The mean square error (MSE) for the neural network model 77
5.3 The fitted line produced by the neural network model 78
6.1 Basic financial work of SF express financial shared service center 87
6.2 Shared platform of SF express financial shared service center 88
7.1 Stages of computational processing 115
8.1 Indices observed in the study 129
8.2 Normalized indices observed in the study 130
8.3 Simulated portfolio values 132
8.4 Simulated portfolio risks 133
viii Figures
8.5 Normalized return series (via risk) 133
8.6 Portfolio structure, 1 best index 135
8.7 Portfolio structure, min var portfolio 135
8.8 Portfolio risk (left panel), standardized return (right panel) 136
9.1 Blockchain structure 143
9.2 Block structure 145
9.3 Types of blockchain 146
10.1 The level of financial development in our country’s
provinces from 2011 to 2015 165
10.2 Views on computer blockchain technology 166
11.1 Regional Malmquist index analysis 176
11.2 Comparative analysis of Theil index in different regions 177
12.1 Optimal economic capital portfolios with 1-day L-VaR
horizon (case of long and short trading positions) 199
12.2 Optimal economic capital portfolios with 10-days L-VaR
horizon (case of long and short trading positions) 199
12.3 Optimal and coherent economic capital portfolios with
different L-VaR horizons (case of long and short trading
positions) 200
12.4 Optimal economic capital portfolios with 1-day L-VaR
horizon (case of long trading positions) 203
12.5 Optimal economic capital portfolios with 10-day L-VaR
horizon (case of long trading positions) 204
12.6 Optimal and coherent economic capital portfolios with
different L-VaR horizons (case of long trading positions) 204
13.1 Analogy of ANN with biological NN 219
13.2 How does ANN works? 219
13.3 Different layers in typical ANN 220
13.4 Popular NN architectures 222
13.5 NN for pattern recognition 223
Tables
1.1 Descriptive statistics of CROBEX and return on CROBEX 6
1.2 Summarization of selected estimation approaches 7
1.3 Portfolio performance of simulated strategies 11
3.1 Changes in total deposits and loans of Bank A
in the past four years 41
3.2 Non-performing loan ratios in the past four years 42
4.1 Key financial elements from the annual report 51
4.2 Financial risk indicators (FRI) list 53
4.3 Data validation checkpoints (CP) 55
4.4 Confusion matrix results for the testing dataset 58
4.5 Confusion matrix table (final results) 59
4.6 Confusion matrix (companies with FSF history) 59
4.7 Confusion matrix (companies without FSF history) 60
4.8 Industry based result analysis 60
4.9 Confusion matrix evaluation 61
5.1 Study variables 68
5.2 Regression analysis 74
5.3 GARCH model 76
5.4 Matrix for neural network 78
5.5 Matrix for logit model 79
5.6 Comparative analysis of the three used models 79
8.1 Descriptive statistics for indices and return series 128
8.2 Descriptive statistics for indices and return series 130
8.3 Descriptive statistics for indices and return series 134
9.1 Blockchain benefits analysis framework for finance services 154
10.1 2011–2015 our country’s financial development level
center of gravity 166
11.1 Regional Malmquist index analysis 176
11.2 Comparative analysis of Theil index in different regions 177
12.1 Risk analysis dataset: daily and annual volatility and
sensitivity factor 188
12.2 Risk analysis dataset: descriptive statistics of daily returns,
skewness, kurtosis, and Jarque–Bera test of normality 189
