IDRBT JOURNAL OF BANKING TECHNOLOGY



To read the Complete IDRBT Journal of Banking Technology Third Issue (Jul-Dec, 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 Affilliation
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 Affilliation
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

In the past decade, disruptive technologies have shrunk the world leading to location-agnostic production and consumption of services. This has transformed the banking industry to a great extent, altering the way the business is conducted.

Banks have created digital infrastructure to offer various solutions like mobile banking, e-wallets and virtual cards, for successfully fulfilling the changing needs of the modern-day customer. Wholesale banking is also catching on this trend of designing and delivering the right mix of products and services to corporate customers, harnessing digital technologies.

Banks have adopted the SMAC technologies – Social, Mobile, Analytics and Cloud – very innovatively. While the SMAC technology developments will continue to be exploited by banks, there are several new technologies emerging in the realm of quantum computing, high-speed networks, biometrics, machine learning, robotics and smart wearables.

Among them, the four key technologies that we consider to be having a great impact on banking are FABS – Five G, Artificial Intelligence, Blockchain and Smart things. We feel that in the next decade, banking will witness a continued trend of reinventing itself, riding on FABS in addition to SMAC. Each of these technologies has potential to challenge the present banking sector and change it to the benefit of all stakeholders.

These trends are highlighted in the three research articles in the current journal. In his article titled "The Rise of Machine Learning and Robo-advisors in Banking", Chaman Lal Sabharwal highlights the use of robo-advisors, which relies heavily on the machine learning techniques. "Sustainable Co-training of Mixture-of-Experts for Credit Scoring of Borrowers in Social Lending" by Jae-Min Yu and Sung-Bae Cho is an illustration of the use of semi-supervised learning in an important area of banking like credit scoring. Alan Megargel, et al., in their article "SOA Maturity Influence on Digital Banking Transformation" discuss how Service-Oriented Architecture (SOA) is clearly an enabler for digital banking.

The two contributions from practitioners are on open APIs and Framework for Technology Controls. Hans Tesselaar, et al., present the perspective of "BIAN on Open APIs for Banking". Subramanian Annaswamy discusses in detail a "Robust Framework for Implementing Technology Controls Amidst Extreme Disruption."

I am sure the banking technology related research and practice across the world presented through the journal will keep enhancing, both in breadth and depth, as we move on.

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

Please click here to read the pdf version of Editorial.

SOA maturity influence on digital banking transformation

Alan Megargel; Venky Shankararaman; Terence Fan Ping-Ching


Abstract: Digital Banking is an evolution of online banking, where the banks attempt to further enhance customer experience by integrating digital technologies such as mobile technology, social media and analytics. Traditional banks have the highest barriers to entry into the digital banking market due to the presence of legacy core banking systems. These legacy systems while still high performing and reliable, are inflexible to change and are not easily integrated to the modern application systems needed for delivering digital banking services across multiple online banking channels. One solution that is widely adopted in the industry to overcome this obstacle is the implementation of a Service-Oriented Architecture (SOA). In this paper, we investigate the relationship between three factors, namely a bank's technology infrastructure, IT governance processes, SOA maturity, and their impact on time-to-market (T2M) of digital banking products and services. Our research study is achieved through surveys and case study interviews conducted with the chief technologists from eight banks operating in Asia. A key conclusion from our study is that SOA maturity plays a very important role in enhancing a bank's capability towards digital banking transformation. In order to move towards higher levels of SOA maturity, we make three recommendations – establishing an SOA centre of excellence, implementation of a well-architected Enterprise Service Bus (ESB), and adoption of an ESB framework and toolkit.

Keywords: Digital Banking, FinTech, Legacy Systems, Service-Oriented Architecture, SOA, SOA Maturity, SOA Centre of Excellence, IT Governance

To read the Complete article please click here.

The rise of machine learning and robo-advisors in banking

Chaman Lal Sabharwal


Abstract: Machine Learning (ML) is a branch of artificial intelligence. A learning algorithm is an algorithm that supports the technology to simulate the human learning process. Computers execute algorithms to facilitate temporal and spatial efficiency for knowledge extraction from large volume of data. Computers are used in all aspects of life, one aspect is financial industry of which banking is a major part. Financial institutions are now increasingly leaning on machine learning to devise new business opportunities, deliver customer services and even detect banking fraud as it is taking place. Deep learning is a branch of machine learning that produces efficient algorithms to model high-level data. These new technologies utilize complex techniques inspired by genetics. The future software applications include NLP processing and analysis of text-based business reports, and intelligent algorithms with intuitive graphical user interface. The financial industry stores vast amounts of data from transaction data to customer data. This volume is likely to increase in the future, and the financial sector is increasingly looking to make the most of such data. In 2016, there was a lot of talk in the industry about the potential for deploying machine learning and over the last two years, this is aptly taking off in the mainstream in finance and banking. But the noise and behavioral elements inherent in raw financial data often require non-standard machine learning solutions, possibly yet to be developed. The full potential of machine learning in finance is still to be explored. We present an intuitive, practical and non-mathematical view of the impact of machine learning. This paper shows how machine learning is going to be used in financial sector in the future.

Keywords: Machine Learning, Robo-advisors, Finance, Banking, Cyber Security, Fraud Prevention

To read the Complete article please click here.

Sustainable co-training of mixture-of-experts for credit scoring of borrowers in social lending

Jae-Min Yu; Sung-Bae Cho


Abstract: Of late, social lending has been so popular that several services for it are provided based on credit scoring with a variety of personal aspects that affect an individual's credit. The factors that are not considered in traditional banks might be more influential than the conventional scoring. Loan requisition can be registered continuously, anytime, by whoever wants loan through social lending. At the same time, large-scale unlabeled data are increasing. Labeling data is expensive. In this paper, with focus on these characteristics, we present a global-local co-training algorithm for mixture-of-experts to exploit the unlabeled data for accurate credit scoring. We conducted experiments with dataset from the Lending Club to evaluate the accuracy based on the reliability of unlabeled dataset. To show the usefulness of the proposed method, we compared the performance with other machine learning methods such as Naive Bayes, logistic regression, decision tree and SVM, and analyzed the confusion matrix. A series of repetitive experiments revealed the quantitative superiority in various characteristics.

Keywords: Social Lending, Credit Scoring, Co-training, Mixture-of-experts

To read the Complete article please click here.

BIAN – PNC open APIs for banking

Mark Grobaker; Arashdeep Kaur1; Chaitanya Kommuru1; Wenting Tao1; Pallavi Thakur1; Hans Tesselaar


Abstract: This paper demonstrates a working proof-of-concept for open APIs in banking, in compliance with the European Commission's PSD2 (Payment Service Directive) financial regulation document. This proof-of-concept was implemented within PNC Bank, a US-based financial services institution. PNC Bank is headquartered in Pittsburgh and operates in 19 states and the District of Columbia with 2,459 branches and 9,051 ATMs. The aim of this paper is to help and encourage other groups within banking, and the broader financial services space, to build on these efforts, and ensure PSD2 compliance at their respective institutions.

Keywords: BIAN, Payment Service Directive, APIs (Application Programming Interface), Enterprise Architecture

To read the Complete article please click here.

A robust framework for implementing technology controls amidst extreme disruption

Subramanian Annaswamy


Abstract: This paper presents a practitioner's approach to addressing technology control issues, especially in the financial services industry. In an era of extremely disruptive technologies, organisations are increasingly vulnerable to complex control issues, which requires creative problem solving. This paper provides a model for control professionals to address emerging risks in a reliable manner. The article presents the industry best practices and experiences of experts in IT audit of some of the leading financial services firms.

Keywords: Big Data, Compliance, Controls, Continuous Auditing, Continuous Monitoring, Cyber Risks, Big Data, GDPR, Sheltered Harbor

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