IDEAS home Printed from https://ideas.repec.org/p/zbw/itsb18/190348.html
   My bibliography  Save this paper

Use of big data in financial sector of Bangladesh – A review

Author

Listed:
  • Abu Taher, Sheikh
  • Uddin, Md. Kama

Abstract

The objective of the paper is to review the use of big data analytics (BDA) in banks and non-banks financial institutions (BNBFI) in Bangladesh. Since the advent of information technology (IT), data collection for BNBFI becomes easy through various channels. As BNBFI conduct business through information, data plays essential role to take an accurate decision. Besides, literature suggests use of big data helps BNBFI to reduce customer churn rate, enhance loyalty, manage risk and increase revenue. BNBFI are leveraging big data to transform their processes, their organizations and soon, the entire industry. For this, the study attempts to explore the effect of BDA on the efficiency of BNBFI in Bangladesh using an explorative study. Since no study has been conducted until now, collection of data becomes difficult. However, data has been collected from extensive literature review, company websites, annual reports and formal conversation with the bank employees. The primary observations suggests, some BNBFIs use data analytics regarding customer through ATM transactions, debit and credit card use, online banking and generate the data from Internet and computer that has better performance than the banks which do not use it. But the performance, however, is not identified in which specific functions BNBFI can emphasize the most to promote efficiency and growth. Besides, the observation is preliminary in nature and needs further study to provide recommendation.

Suggested Citation

  • Abu Taher, Sheikh & Uddin, Md. Kama, 2018. "Use of big data in financial sector of Bangladesh – A review," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190348, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itsb18:190348
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/190348/1/A6_3_Taher-and-Uddin.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    2. Sheikh Taher Abu & Masatsugu Tsuji, 2011. "The Development of ICT for Envisioning Cloud Computing and Innovation in South Asia," International Journal of Innovation in the Digital Economy (IJIDE), IGI Global, vol. 2(1), pages 61-72, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcel Fafchamps & Julien Labonne, 2017. "Do Politicians’ Relatives Get Better Jobs? Evidence from Municipal Elections," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 33(2), pages 268-300.
    2. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    3. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    4. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    5. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    6. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
    7. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    8. Marieke Bos & Emily Breza & Andres Liberman, 2018. "The Labor Market Effects of Credit Market Information," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2005-2037.
    9. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    10. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    11. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    12. Jin-Hyuk Kim & Tin Cheuk Leung, 2013. "Quantifying the Impacts of Digital Rights Management and E-Book Pricing on the E-Book Reader Market," Working Papers 13-03, NET Institute.
    13. Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.
    14. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    15. Vincze, János, 2017. "Információ és tudás. A big data egyes hatásai a közgazdaságtanra [Information and knowledge: some effects of big data on economics]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(11), pages 1148-1159.
    16. Gabriel Suarez & Juan Raful & Maria A. Luque & Carlos F. Valencia & Alejandro Correa-Bahnsen, 2021. "Enhancing User' s Income Estimation with Super-App Alternative Data," Papers 2104.05831, arXiv.org, revised Aug 2021.
    17. Justin Longo & Alan Rodney Dobell, 2018. "The Limits of Policy Analytics: Early Examples and the Emerging Boundary of Possibilities," Politics and Governance, Cogitatio Press, vol. 6(4), pages 5-17.
    18. Max Nathan & Anna Rosso, 2017. "Innovative events," Development Working Papers 429, Centro Studi Luca d'Agliano, University of Milano, revised 08 Apr 2019.
    19. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
    20. Haskamp, Ulrich, 2017. "Improving the forecasts of European regional banks' profitability with machine learning algorithms," Ruhr Economic Papers 705, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    More about this item

    Keywords

    big data analytics; finance; efficiency; innovation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:itsb18:190348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: http://www.itsworld.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.