IDRBT JOURNAL OF BANKING TECHNOLOGY



To read the Complete IDRBT Journal of Banking Technology Second Issue (Jan-Jun, 2018) please click here.

Editorial Board

Reflecting the thrust of the Journal, the Editorial Board of the Journal represents a fine balance of researchers and practitioners. The Editorial Board of the Journal is as under.

Editor-in-Chief
Name Affiliation
Dr. A. S. Ramasastri Director
Institute for Development and Research in Banking Technology (IDRBT)
Road No. 1, Castle Hills, Masab Tank
Hyderabad, India.
e-mail: asramasastri@idrbt.ac.in.
Editorial Board Members
Name Affiliation
Prof. Chin-Teng Lin University Chair Professor/Provost
Engineering and Information Technology
University of Technology, Sydney
Electrical and Computer Engineering
National Chiao-Tung University, Taiwan, China.
e-mail: Chin-Teng.Lin@uts.edu.au.
Prof. Constantin Zopounidis Director of the Financial Engineering Laboratory
School of Production Engineering and Management,
Technical University of Crete, Chania, Greece.
e-mail: kostas@dpem.tuc.gr.
Prof. Dirk Van den Poel Professor
Data Analytics/Big Data
Faculty of Economics and Business Administration
Ghent University, Belgium.
Tweekerkenstraat 2, 9000 Gent, Belgium.
e-mail: dirk.vandenpoel@UGent.be.
Prof. D. B. Phatak Professor
Department of Computer Science and Engineering
Indian Institute of Technology, Bombay, India.
e-mail: dbp@it.iitb.ac.in / dbp@cse.iitb.ac.in.
Prof. Kalyanmoy Deb Koenig Endowed Chair Professor
Department of Electrical & Computer Engineering
Professor of Computer Science & Engineering
Professor of Mechanical Engineering
Michigan State University, USA.
e-mail: kdeb @ egr.msu.edu.
Mr. Marc Hollanders Special Adviser on Financial Infrastructure
Monetary and Economic Department
Bank for International Settlements, Switzerland
e-mail: Marc.Hollanders@bis.org
Mr. Massimo Cirasino Advisor and Global Lead for Payments and Market Infrastructures
Finance and Markets Global Practice
The World Bank, Washington, USA.
e-mail: mcirasino@worldbank.org
Prof. Paolo Bellavista Associate Professor,
Department of Computer Science and Engineering
Università di Bologna, Italy.
e-mail: paolo.bellavista@unibo.it
Prof. Rajkumar Buyya Professor, Department of Computer Science and Software Engineering
Director, Cloud and Distributed Systems
University of Melbourne, Australia.
e-mail: rbuyya@unimelb.edu.au
Prof. R. K. Shyamasundar JC Bose National Fellow & Distinguished Visiting Professor
Department of Computer Science and Engineering
Indian Institute of Technology, Bombay, India.
e-mail: rkss@cse.iitb.ac.in
Prof. Sushil Jajodia University Professor
BDM International Professor of Information Technology
Director, Center for Secure Information Systems
Volgenau School of Engineering,
George Mason University, Fairfax, Virginia, USA.
e-mail: jajodia@gmu.edu
Mr. Thomas Lammer Senior Market Infrastructure Expert
European Central Bank
Frankfurt, Germany
e-mail: Thomas.lammer@ecb.int
Prof. Venu Govindaraju SUNY Distinguished Professor
Department of Computer Science & Engineering
Director, Center for Unified Biometrics and Sensors.
University at Buffalo, State University of New York, USA.
e-mail: venu@cubs.buffalo.edu

One of the primary objectives of banks is to deliver what customer needs. Technology has been assisting banks in achieving this objective by providing appropriate solutions. Quite often than not, the technology solutions do leave a few vulnerabilities. It is exactly here that the researchers and practitioners play an important role – to help banks in building secure, robust and convenient products and services.

Traditionally, banks have been using technology solutions that can do routine day-to-day activities more accurately and efficiently. Over the past few years, the scope of technology solutions has changed. There have been useful proven techniques to assist banks in prediction and decision-making due to recent developments in fuzzy systems and neural networks. In-store robots, software robotics, chatbots, smart cameras and self-learning risk models are being used by banks.

The keywords in today’s banking technology are artificial intelligence, cyber defence and digital payments. We are happy that the papers in the current (second) issue of our Journal on Banking Technology focusses on these important areas.

There are three stages in building good cyber defence systems. During the first stage, detection and prevention systems are put in place; such systems are mostly based on past knowledge. During the second stage, systems are built to predict and prevent cyber attacks; such systems are based on past knowledge and future predictive power. It is only in the final stage that systems are built to anticipate attacks and prevent them; they are in the realm of the unknown. The first paper in the journal by Michael Weiss titled “From Prediction to Anticipation of Cyber Attacks” is about the developments in this area.

More uncertain the environment, more difficult the decision-making of an individual or an institution. But, it is only the uncertainty that warrants effective decision-making. The second paper by Monika, Hao Quan and Dipti Srinivasan titled “Decision-Making under Uncertainty” discusses the issues of decision-making in detail.

Security is of utmost concern to banks. More secure the systems of banks, more trust the customers will have in banks. And banks function on the principle of trust. Security is at the core of trust. The third paper by Rao Vemuri titled “Machine Learning in Computer Security” dwells on the use of machine learning for security.

While Bipin Sahni presents from his experience “Innovating a Seamless Customer Experience” in the fourth paper, Gynedi Srinivas and Harish Natarajan, the researchers from World Bank give a detailed account of “National Payment System – Overview of Regulatory Mandates” in the fifth paper.

We trust the papers prove useful to both academia and industry.

Dr. A. S. Ramasastri
Editor-in-Chief

Please click here to read the pdf version of Editorial.

From prediction to anticipation of cyber attacks

Michael Weiss


Abstract: With the rising volume and variety of cyber attacks, it has become increasingly harder for businesses and organizations to defend against attacks. The paper makes the case that to respond to this challenge, we need to anticipate new threats, not merely react to known threats. It reviews reactive approaches to cyber attacks where current actions are based on past behavior, and proactive approaches guided by predictions about the future.

Keywords: Cybersecurity, Prediction, Anticipation, Threats, Cyber attacks

To read the Complete article please click here.

Decision-making under uncertainty

Monika; Hao Quan; Dipti Srinivasan


Abstract: Choosing actions on the basis of imperfect observations with unknown outcomes is called decision-making under uncertainty, and exists in many important problems. It unifies researchers across various disciplines to develop and model tools and methodologies to solve real-world decision-making problems under uncertainty. This paper proposes an efficient uncertainty modelling method using neural network-based prediction intervals (NN-based PIs) based on [1]. PIs are excellent tools to model uncertainties. Particle swarm optimisation (PSO)-based lower upper bound estimation (LUBE) method is used to construct NN-based PIs. Thereafter, a scenario generation method is used to generate scenarios from a list of PIs. These scenarios are further incorporated into the stochastic model for decision-making.

Keywords: Decision-making, Uncertainty, Neural networks, Particle swarm optimization, Lower Upper Bound Estimation, Prediction

To read the Complete article please click here.

Machine learning in computer security

V. Rao Vemuri


Abstract: The past few years have witnessed a rise in the use of AI and Machine Learning techniques to a variety of application areas, such as image understanding and autonomous vehicle driving. Wireless and cloud technologies have also made it possible for millions of people to access and use services available via the internet. During the same period, the world has also witnessed a rise in cyber-crime, with criminals continually expanding their methods of attack. Weapons like ransomware, botnets, and attack vectors became popular forms of malware attacks. This paper examines the state-of-the-art in computer security and the use of machine learning techniques therein. True, machine learning did make an impact on some narrow application areas such as spam filtering and fraud detection. However – in spite of extensive academic research – it did not seem to make a visible impact on the problem of intrusion detection in real operational settings. A possible reason for this apparent failure is that computer security is inherently a difficult problem. Difficult because it is not just one problem; it is a group of problems characterized by a diversity of operational settings and a multitude of attack scenarios. This is one reason why machine learning has not yet found its niche in the cyber warfare armory. This paper first summarizes the state-of-the-art in computer security and then examines the process of applying machine learning to solve a sample problem.

Keywords: Machine Learning, Computer Security, Intrusion Detection

To read the Complete article please click here.

Innovating a seamless customer experience

Bipin Sahni


Abstract: The pace of innovation presents multiple challenges for companies, but perhaps the most critical is how to meet and exceed customers’ ever-increasing user experience expectations. However, wherever there is a challenge, there is also an opportunity; innovating faster and better than your competitors can increase market share. In this article, we examine four innovations – Internet of Everything (IoE), Artificial Intelligence, Biometrics, and Mixed Reality – and discuss how they can deliver frictionless transactions to improve the user experience for our customers.

Keywords: Financial Services, Financial Technology, Innovations, Artificial Intelligence, Biometrics, Mixed Reality, User Experience

To read the Complete article please click here.

National payment system – overview of regulatory mandates

Gynedi Srinivas; Harish Natarajan


Abstract: This article discusses the concept of the national payment system (NPS) and the different regulatory models being applied in select countries for conducting oversight over different components of the NPS. We describe the core components of the NPS and their use in the World Bank in its payment system technical assistance projects. Further, we discuss the need to define the regulatory mandates and parameters when different regulators are involved for the oversight of the different components of the NPS, drawing upon international examples comprising the European Union (Germany and Luxembourg) and Turkey.

Keywords: National payment system, Regulatory models, Nine pillars methodology, Financial stability, Payment systems and operations

To read the Complete article please click here.

Publisher Contact

Queries, if any, may be sent to

S. Rashmi Dev

Assistant General Manager - Publications
Institute for Development and Research in Banking Technology (IDRBT)
Castle Hills, Road No.1, Masab Tank, Hyderabad - 500 057.
Telephone: +91 (40) 2329 4163
Fax: +91 (40) 2353 5157
Email: ijbtqueries@idrbt.ac.in