Big Data Analytics for Financial Services and Banking

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

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BIG DATA ANALYTICS IN BANKING AND FINANCIAL SERVICES SECTOR

EPRA International Journal of Research & Development (IJRD) ◽

10.36713/epra6558 ◽

2020 ◽

pp. 92-96

Author(s):

Dr.P.V. Mohini ◽

Mr. Rohit Kumar Srivastav

Keyword(s):

Big Data ◽

Financial Services ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Analysis ◽

Valuable Data ◽

Services Sector ◽

Financial Services Sector ◽

The People ◽

Potential Benefits

‘He who holds the wealth of information, holds the solution to the toughest of the situations’ this quote is very much apt for the recent ongoing scenario, where there is an invisible struggle going on among the organizations as well as nations in the search for more and more information. Now, when the Banking and Financial services sectors are put under the scanner, it becomes evident that they are sitting on top of a humungous heap of valuable data. This data can be used for the betterment and advancement of the industry as well as the people. While it is good to have large amount of data available, it will be termed a big pile of trash if it is not analyzed properly and the results obtained from it are not put to use. With the adoption of Big Data analytics into the banking and financial services, many obvious as well as concealed problems can be addressed to and even solved quickly. The main objective of this paper is to highlight the meaning of Big Data analysis, study the types of data analytics with respect to Banking and Financial services sector and to showcase the potential benefits of embracing Big Data analytics into the Banking & Financial services sector. KEYWORDS: Information, Banking and Financial services, Advancement, Big Data, Data Analytics

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AI Robo-Advisor with Big Data Analytics for Financial Services

2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) ◽

10.1109/asonam.2018.8508854 ◽

2018 ◽

Cited By ~ 2

Author(s):

Min-Yuh Day ◽

Tun-Kung Cheng ◽

Jheng-Gang Li

Keyword(s):

Big Data ◽

Financial Services ◽

Data Analytics ◽

Big Data Analytics

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Pushing the limits of accountability: big data analytics containing and controlling COVID-19 in South Korea

Accounting Auditing & Accountability Journal ◽

10.1108/aaaj-08-2020-4829 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Paul D. Ahn ◽

Danture Wickramasinghe

Keyword(s):

Big Data ◽

South Korea ◽

Financial Services ◽

Media Coverage ◽

Data Analytics ◽

Central Government ◽

Big Data Analytics ◽

Content Type ◽

The Government ◽

State Form

PurposeThe purpose of this paper is to illustrate how big data analytics pushed the limits of individuals' accountability as South Korea tried to control and contain coronavirus disease 2019 (COVID-19).Design/methodology/approachThe authors draw upon Deleuzo-Guattarian framework elaborating how a surveillant assemblage was rhizomatically created and operated to monitor a segment of the population holding them accountable. Publicly available secondary data, such as press release from the government and media coverage, were used.FindingsA COVID-19 Smart Management System and a Self-Quarantine Safety Protection App constituted a surveillance assemblage operating in a “state-form”. This comprises the central government departments, local councils, policing systems, providers of telecommunication and financial services, and independent groups of people. This assemblage pushed the limits of accountability as individuals who tested positive or might bear possible future risks of the infection and transmission were held accountable for their locations and health conditions.Practical implicationsPolicymakers may consider constructing this type of state-form for containing and controlling pandemics, such as COVID-19, while dealing with the issue of undermined privacy.Social implicationsThe mass may consider to what extent individuals' personal information should be protected and how to hold the governments accountable for the legitimate use of such information.Originality/valueWhile accountability studies have largely focussed on formal organisations, the authors illustrated how a broader context of a state-form, harnessing big data analytics, pushes the limits of accountability.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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