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Identifying potential millennial customers for financial institutions using SVM

Author

Listed:
  • Swati Anand

    (Babasaheb Bhimrao Ambedkar University (A Central University))

  • Kushendra Mishra

    (Babasaheb Bhimrao Ambedkar University (A Central University))

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.

Suggested Citation

  • Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
    • Handle: RePEc:pal:jofsma:v:27:y:2022:i:4:d:10.1057_s41264-021-00128-7
    DOI: 10.1057/s41264-021-00128-7
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    References listed on IDEAS

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    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Gestel, Tony Van & Baesens, Bart & Suykens, Johan A.K. & Van den Poel, Dirk & Baestaens, Dirk-Emma & Willekens, Marleen, 2006. "Bayesian kernel based classification for financial distress detection," European Journal of Operational Research, Elsevier, vol. 172(3), pages 979-1003, August.
    3. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    4. K B Schebesch & R Stecking, 2005. "Support vector machines for classifying and describing credit applicants: detecting typical and critical regions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1082-1088, September.
    5. Gathergood, John, 2012. "Self-control, financial literacy and consumer over-indebtedness," Journal of Economic Psychology, Elsevier, vol. 33(3), pages 590-602.
    6. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
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