Article Sidebar

Download
PDF
Statistic
Read Counter : 226 Download : 273

Main Article Content

Abstract

This research addresses the critical need for intelligent optimization mechanisms in financial process modules by developing a machine learning-enhanced collaborative system designed for digital finance platforms, aiming to bridge theoretical advances in human-machine collaboration with practical applications in financial process optimization. A sophisticated multi-layered architecture integrating machine learning capabilities with human decision-making processes was developed, incorporating advanced ensemble algorithms, multi-objective optimization techniques, and adaptive learning mechanisms. The system was validated across three real-world scenarios. These included credit risk assessment using 2.26 million Lending Club records, anti-money laundering with 6.3 million FinCEN transactions, and customer service optimization with 1.8 million banking interactions. The collaborative system achieved significant improvements. Cost reduced by 28.4% and accuracy increased by 15.3% in credit risk assessment. AML efficiency improved by 256%, and AUC-ROC increased from 0.847 to 0.923. Processing time was reduced from 4.2 days to 1.8 days while maintaining regulatory compliance, resulting in a 44.8% return on investment in the first operational year. The learning collaborative approach efficiently combines human knowledge and AI, outperforming regular computerized methods as well as purely human strategies and maintaining long-term system improvement through its adaptive learning capability. This study provides practical toolkits for financial institutions to further explore AI in process optimization, aiming to achieve sustainable competitive advantages and compliance, while also ensuring operational efficiencies.

Keywords

Machine learning Human-AI collaboration Financial process optimization Digital finance platforms Multi-objective optimization

Article Details

Author Biography

Ting Wang, College of Business Administration, University of the Cordilleras, Gov. Pack Road, 2600 Baguio City, the Philippines

TING-WANG is currently pursuing the PhD in Management degree in the University of the Cordilleras. Her research interests include financial management.

How to Cite
Wang, T., & R. Tobias, G. (2025). Research on intelligent optimization mechanisms of financial process modules through Machine Learning-enhanced collaborative systems in digital finance platforms. Future Technology, 4(4), 240–254. Retrieved from https://fupubco.com/futech/article/view/503
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. Aldasoro, I., Gambacorta, L., Korinek, A., et al., Intelligent financial system: how AI is transforming finance. BIS Working Paper No. 1194, Bank for International Settlements, 2024. Available at: https://www.bis.org/publ/work1194.pdf
  2. Pattnaik, D., Ray, S., and Raman, R., Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon, 2024. 10(1): e23492. DOI: 10.1016/j.heliyon.2023.e23492
  3. Roy, P., Ghose, B., Singh, P.K., Tyagi, P.K., and Vasudevan, A., Artificial intelligence and finance: A bibliometric review on the trends, influences, and research directions. F1000Research, 2025. 14: 122. DOI: 10.12688/f1000research.160959.1
  4. Vuković, D.B., Dekpo-Adza, S., and Matović, S., AI integration in financial services: a systematic review of trends and regulatory challenges. Humanities and Social Sciences Communications, 2025. 12(1): 1-29. DOI: 10.1038/s41599-025-04850-8
  5. Van Rooy, D., Human–machine collaboration for enhanced decision-making in governance. Data & Policy, 2024. 6: e60. DOI: 10.1017/dap.2024.60.
  6. Li, J.-M., Wu, T.-J., Wu, Y.J., and Goh, M., Systematic literature review of human–machine collaboration in organizations using bibliometric analysis. Management Decision, 2023. 61(10): 2920-2944. DOI: 10.1108/MD-09-2022-1183
  7. Ameen, N., et al., The rise of human–machine collaboration: managers’ perceptions of leveraging artificial intelligence for enhanced B2B service recovery. British Journal of Management, 2025. 36(1): p. 91-109.DOI: 10.1111/1467-8551.12829
  8. Fragiadakis, G., et al., Evaluating human-ai collaboration: A review and methodological framework. arXiv preprint arXiv:2407.19098, 2024.
  9. Cantrell, S., Davenport, T.H., Hatfield, S., et al., Strengthening the bonds of human and machine collaboration. Deloitte Insights, 2022. Available at: https://www2.deloitte.com/us/en/insights/focus/technology-and-the-future-of-work/human-and-machine-collaboration.html
  10. Roy, M.-A. and G. Abdul-Nour, Integrating Modular Design Concepts for Enhanced Efficiency in Digital and Sustainable Manufacturing: A Literature Review. Applied Sciences, 2024. 14(11): p. 4539. DOI: 10.3390/app14114539
  11. Usenko, L.N., Guzey, V.A., and Bijieva, A.S., Modern opportunities for optimizing business processes to achieve sustainable development. In International Scientific and Practical Conference Operations and Project Management: Strategies and Trends. Cham: Springer International Publishing, 2021: 138-150. DOI: 10.1007/978-3-030-94245-8_19.
  12. Butt, J., A conceptual framework to support digital transformation in manufacturing using an integrated business process management approach. Designs, 2020. 4(3): p. 17. DOI: 10.3390/designs4030017
  13. Fischer, M., et al., Strategy archetypes for digital transformation: Defining meta objectives using business process management. Information & management, 2020. 57(5): p. 103262. DOI: 10.1016/j.infman.2020.103262
  14. Zellner, G., A structured evaluation of business process improvement approaches. Business process management journal, 2011. 17(2): p. 203-237. DOI: 10.1108/14637151111122329
  15. Tsakalidis, G. and K. Vergidis, Business process redesign: a systematic review of evaluation approaches. Decision Making: Applications in Management and Engineering, 2024. 7(1): p. 79-98. DOI: 10.31181/dmame712024889
  16. Wang, S., et al., An integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm. Symmetry, 2021. 14(1): p. 48. DOI: 10.3390/sym14010048
  17. Brusoni, S., et al., The power of modularity today: 20 years of “Design Rules”. 2023, Oxford University Press UK. p. 1-10. DOI: 10.1093/icc/dtac054
  18. Monetti, F.M. and A. Maffei, Towards the definition of assembly-oriented modular product architectures: a systematic review. Research in Engineering Design, 2024. 35(2): p. 137-169. DOI: 10.1007/s00163-023-00427-1
  19. Aguilera, R.V., et al., Organizational goals, outcomes, and the assessment of performance: Reconceptualizing success in management studies. Journal of Management Studies, 2024. 61(1): p. 1-36. DOI: 10.1111/joms.12994
  20. Nayeli, I. and B. GEORGE, Advancing Performance Management In Digital Enterprises: Exploring Challenges, Opportunities, And Recommendations For The Digital Age. Ecoforum Journal, 2023. 12(3). Available at: https://www.ceeol.com/search/article-detail?id=1194070
  21. Korsen, E.B.H., M.D.-Q. Holmemo, and J.A. Ingvaldsen, Digital technologies and the balance between control and empowerment in performance management. Measuring business excellence, 2022. 26(4): p. 583-596. DOI: 10.1108/MBE-04-2021-0055
  22. Nasiri, M., et al., Digital-related capabilities and financial performance: the mediating effect of performance measurement systems. Technology analysis & strategic management, 2020. 32(12): p. 1393-1406. DOI: 10.1080/09537325.2020.1772966
  23. Camarinha-Matos, L.M., et al., Collaborative networks: A pillar of digital transformation. Applied Sciences, 2019. 9(24): p. 5431. DOI: 10.3390/app9245431
  24. Lahajnar, S. and A. Rožanec, The evaluation framework for business process management methodologies. Management: journal of contemporary management issues, 2016. 21(1): p. 47-69. Available at: https://hrcak.srce.hr/clanak/237864
  25. Sachan, S., et al., Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm. International Review of Financial Analysis, 2024. 93: p. 103149. DOI: 10.1016/j.irfa.2024.103149