Identifying potential millennial customers for financial institutions using SVM

Swati Anand () and Kushendra Mishra ()
Additional contact information
Swati Anand: Babasaheb Bhimrao Ambedkar University (A Central University)
Kushendra Mishra: Babasaheb Bhimrao Ambedkar University (A Central University)

Journal of Financial Services Marketing, 2022, vol. 27, issue 4, No 6, 335-345

Abstract: Abstract In order to survive in this complex economic business environment with fierce competition among various players of the finance sector, the need is to understand the even more complex financial behaviour of the customers. We apply the support vector machine classifier, a machine learning algorithm to construct a nonlinear model which classifies the customers into good and bad class based on their respective positive and negative saving behaviour. With the help of web-based survey, a sample of urban banking millennial was collected and preprocessed to apply the support vector machine classifier technique. Pattern recognition from data and its prediction for the financial behaviour are based on the machine learning forecasts. Moreover, the comparative analysis of the weightage of the three attributes, namely income level, financial literacy and behavioural characteristic, is carried out and it is analysed for the savings/wealth accumulation of the millennial generation to understand the financial distress among the said generation in context.

Keywords: Support vector machine classifier; Machine learning; Nonlinear pattern recognition; Financial literacy (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1057/s41264-021-00128-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jofsma:v:27:y:2022:i:4:d:10.1057_s41264-021-00128-7

Ordering information: This journal article can be ordered from
https://www.palgrave.com/gp/journal/41264

DOI: 10.1057/s41264-021-00128-7

Access Statistics for this article

Journal of Financial Services Marketing is currently edited by Tina Harrison

More articles in Journal of Financial Services Marketing from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().