★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★Open research. Global perspectives.
Engineering and Technology OPEN ACCESS

TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING

Md Habibur Rahman
Department of Business Administration, International American University, Los Angeles, California, USA
Ashim Chandra Das
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Shujan Shak
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Kafil Uddin
Dahlkemper School of Business, Gannon University, USA
Md Imdadul Alam
Master of Science in Financial Analysis, Fox School of Business, Temple University, USA
Nafis Anjum
College of Technology and Engineering, Westcliff University, Irvine, USA
Md Nad Vi Al Bony
Department of Business Administration, International American University, Los Angeles, USA
Murshida Alam
Department of Business Administration, Westcliff University, Irvine, California, USA
Md Mehedi Hassan
Master of Science in Information Technology, Washington University of Science and Technology, USA
tajet 2024
VOL. 6 / NO. 10 OCTOBER
VOLUME 6
ISSUE 10
YEAR 2024
PAGES 150-163

Abstract

In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.

ZENODO DOI:- https://doi.org/10.5281/zenodo.14008362

Keywords

Customer Retention, Predictive Analytics Machine Learning

Downloads

Download data is not yet available.

References

  1. Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186.
  2. Shahid, R., Mozumder, M. A. S., Sweet, M. M. R., Hasan, M., Alam, M., Rahman, M. A., ... & Islam, M. R. (2024). Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience. International Journal on Computational Engineering, 1(2), 50-54.
  3. Chowdhury, M. S., Shak, M. S., Devi, S., Miah, M. R., Al Mamun, A., Ahmed, E., ... & Mozumder, M. S. A. (2024). Optimizing E-Commerce Pricing Strategies: A Comparative Analysis of Machine Learning Models for Predicting Customer Satisfaction. The American Journal of Engineering and Technology, 6(09), 6-17.
  4. Md Abu Sayed, Badruddowza, Md Shohail Uddin Sarker, Abdullah Al Mamun, Norun Nabi, Fuad Mahmud, Md Khorshed Alam, Md Tarek Hasan, Md Rashed Buiya, & Mashaeikh Zaman Md. Eftakhar Choudhury. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CYBERSECURITY ATTACK SUCCESS: A PERFORMANCE EVALUATION. The American Journal of Engineering and Technology, 6(09), 81–91. https://doi.org/10.37547/tajet/Volume06Issue09-10
  5. Md Al-Imran, Salma Akter, Md Abu Sufian Mozumder, Rowsan Jahan Bhuiyan, Tauhedur Rahman, Md Jamil Ahmmed, Md Nazmul Hossain Mir, Md Amit Hasan, Ashim Chandra Das, & Md. Emran Hossen. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22–33. https://doi.org/10.37547/tajet/Volume06Issue09-04
  6. Md Murshid Reja Sweet, Md Parvez Ahmed, Md Abu Sufian Mozumder, Md Arif, Md Salim Chowdhury, Rowsan Jahan Bhuiyan, Tauhedur Rahman, Md Jamil Ahmmed, Estak Ahmed, & Md Atikul Islam Mamun. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR ACCURATE LUNG CANCER PREDICTION. The American Journal of Engineering and Technology, 6(09), 92–103. https://doi.org/10.37547/tajet/Volume06Issue09-11
  7. Bahl, S., Kumar, P., & Agarwal, A. (2021). Sentiment analysis in banking services: A review of techniques and challenges. International Journal of Information Management, 57, 102317.
  8. Ashim Chandra Das, Md Shahin Alam Mozumder, Md Amit Hasan, Maniruzzaman Bhuiyan, Md Rasibul Islam, Md Nur Hossain, Salma Akter, & Md Imdadul Alam. (2024). MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY. The American Journal of Engineering and Technology, 6(10), 42–53. https://doi.org/10.37547/tajet/Volume06Issue10-06
  9. Rowsan Jahan Bhuiyan, Salma Akter, Aftab Uddin, Md Shujan Shak, Md Rasibul Islam, S M Shadul Islam Rishad, Farzana Sultana, & Md. Hasan-Or-Rashid. (2024). SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(10), 54–66. https://doi.org/10.37547/tajet/Volume06Issue10-07
  10. Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: A comparative analysis. Journal of Applied Security Research, 12(4), 1–14. https://doi.org/10.1080/19361610.2017.1315696
  11. Bhowmik, D. (2019). Detecting financial fraud using machine learning techniques. International Journal of Data Science, 6(2), 102-121. https://doi.org/10.1080/25775327.2019.1123126
  12. Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2015). Credit card fraud detection: A realistic modeling and a novel
  13. Bashir, A., Yousaf, M., & Awan, M. U. (2021). Customer churn prediction in mobile banking: A machine learning approach. International Journal of Information Management, 58, 102303. https://doi.org/10.1016/j.ijinfomgt.2021.102303
  14. Chen, Y., Chen, H., & Liu, Y. (2021). The impact of customer satisfaction on customer retention in the fintech industry: A case study. Journal of Financial Services Marketing, 26(2), 85-97. https://doi.org/10.1057/s41264-021-00110-7
  15. Dewan, S., Wu, D. J., & Trivedi, R. (2020). Customer engagement in fintech: A study of factors affecting retention. Journal of Banking and Finance, 118, 105888. https://doi.org/10.1016/j.jbankfin.2020.105888
  16. Li, J., Liu, X., & Zhang, Y. (2021). Predicting customer churn in mobile banking: A case study of a Chinese fintech firm. Journal of Retailing and Consumer Services, 59, 102393. https://doi.org/10.1016/j.jretconser.2021.102393
  17. Ransom, C. (2021). The cost of customer acquisition versus retention in fintech. Harvard Business Review. Retrieved from https://hbr.org/2021/03/the-cost-of-customer-acquisition-versus-retention-in-fintech
  18. Wang, Q., Zhang, H., & Liu, S. (2022). A gradient boosting approach for customer churn prediction in digital financial services. Expert Systems with Applications, 208, 118267. https://doi.org/10.1016/j.eswa.2022.118267
CONTINUE EXPLORING

Most read articles by the same author(s)

Explore other published research by the authors of this article.

1 2 > >>