Fin Tech Credit Scoring Systems and Financial Accessibility for Small Businesses
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P136Keywords:
FinTech, Credit Scoring, Financial Accessibility, Small Businesses, Artificial Intelligence, Machine Learning, Big Data Analytics, Blockchain, Financial Inclusion, Digital LendingAbstract
With the advent of rapid growth of FinTech technologies, traditional practices regarding the processes of credit scoring and loan making have been transformed, particularly focusing on providing financial inclusion for SMEs. Historically, banks and other financial organizations would use collateral, documentation procedures, and financial records for the purpose of loan granting, which made it impossible for some companies to get access to funds. To solve this problem, credit scoring mechanisms through FinTech started to apply new methods, including such approaches as AI, ML, Big Data, blockchain, and alternative data. This work is going to explore the issue of credit scoring via FinTech and will examine its development and evolution. In addition, the paper will review AI-based credit scoring algorithms that include Random Forest, Logistic Regression, Neural Networks, and XAI. Lastly, the paper will examine the incorporation of alternative data into credit scoring in the context of improving information transparency and financial inclusion among financially excluded borrowers. In addition, the paper examines some of the major obstacles faced in the FinTech lending ecosystem, including cybersecurity, privacy issues, algorithmic bias, lack of transparency, and regulatory challenges. The paper is based on the literature review and employs a critical analysis approach, coupled with theories of Information Asymmetry, Financial Inclusion, Technology Acceptance Model (TAM), and Diffusion of Innovation to explore the existing situation. It can be concluded from the findings that FinTech credit scoring surpasses traditional financing models with regard to performance, efficiency, financial inclusion, and predictive ability. However, for its sustainability, better governance frameworks, transparency, regulations, and cybersecurity measures must be put in place.
References
1. O. M. Bolanle and A. O. Ademola, “Financial Literacy and Credit Accessibility for Small Scale Business Sustainable Growth,” Int. J. Bus. Manag. Rev., vol. 12, no. 8, pp. 51–66, Jul. 2024, doi: 10.37745/ijbmr.2013/vol12n85166.
2. S. Kyeong and J. Shin, “Two-stage credit scoring using Bayesian approach,” J. Big Data, vol. 9, no. 1, p. 106, Nov. 2022, doi: 10.1186/s40537-022-00665-5.
3. J. R. Bhat, S. A. AlQahtani, and M. Nekovee, “FinTech enablers, use cases, and role of future internet of things,” J. King Saud Univ. - Comput. Inf. Sci., vol. 35, no. 1, pp. 87–101, Jan. 2023, doi: 10.1016/j.jksuci.2022.08.033.
4. A. K. Pillai, “The Impact of Fintech on Traditional Lending Practices,” Interantional J. Sci. Res. Eng. Manag., vol. 09, no. 04, pp. 1–9, Apr. 2025, doi: 10.55041/IJSREM44880.
5. L. M. Palladino, “The impacts of fintech on small business borrowing,” Journal of Small Business & Entrepreneurship, vol. 33, no. 6, pp. 639–661, Nov. 2021, doi: 10.1080/08276331.2020.1796110.
6. N. A. Khamitdkhanovich, C. Senthilkumar, V. O. Kavitha, and E. R. S. Ugli, “The evolution of open banking: Transforming financial services with data-driven technologies,” in AIP Conference Proceedings, 2025, p. 030026, doi: 10.1063/5.0277721.
7. M. Tigges, S. Mestwerdt, S. Tschirner, and R. Mauer, “Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data,” Technological Forecasting and Social Change, vol. 205, p. 123491, Aug. 2024, doi: 10.1016/j.techfore.2024.123491.
8. M. Bazarbash, “FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk,” IMF Working Papers, vol. 2019, no. 109, p. 1, May 2019, doi: 10.5089/9781498314428.001.
9. J. Hu, Y. Huang, and J. Liu, “The changing face of consumer credit: Evidence from a big tech platform in China,” Pacific-Basin Finance Journal, vol. 83, p. 102254, Feb. 2024, doi: 10.1016/j.pacfin.2024.102254.
10. S. A. Pushkala, “Hybrid Rule-Based and Deep Learning System using GNNs to Detect Credit Card Fraud,” in SoutheastCon 2026, IEEE, Feb. 2026, pp. 1–6, doi: 10.1109/SoutheastCon63549.2026.11476208.
11. D. Patel, “Explainable Risk Decision Systems Using Artificial Intelligence Models for Payment Fraud Identification with Mitigation,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), IEEE, Mar. 2026, pp. 1–6, doi: 10.1109/ISDFS69419.2026.11459006.
12. J. G. M., “Study on Usage of Artificial Intelligence in FinTech Industry,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 8, pp. 520–549, Aug. 2024, doi: 10.22214/ijraset.2024.63938.
13. S. Irfan, “AI-Driven Credit Scoring: A Study of Machine Learning Algorithms for Enhanced Risk Assessment,” in 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), IEEE, Mar. 2026, pp. 1–6, doi: 10.1109/AIEI69164.2026.11496824.
14. H. Kumar, “AI and Machine Learning Integration into Cloud-Based Fintech Platforms,” International Journal of Scientific Research in Engineering and Management, vol. 8, no. 10, pp. 1–8, Oct. 2024, doi: 10.55041/IJSREM37825.
15. D. M. Parthiban, “Big Data Analytics in Fintech: A Review of Credit Risk Assessment and Fraud Detection,” Educational Administration: Theory and Practice, pp. 3676–3684, May 2024, doi: 10.53555/kuey.v30i5.3514.
16. T. Ali, M. Al-Khalidi, and R. Al-Zaidi, “Information Security Risk Assessment Methods in Cloud Computing: Comprehensive Review,” Journal of Computer Information Systems, vol. 66, no. 1, pp. 123–150, Jan. 2026, doi: 10.1080/08874417.2024.2329985.
17. P. Parida and N. Senguttuvan, “Responsible Utilization of Cloud in Retail Banking Ecosystem,” International Journal of Computer Applications, vol. 187, no. 49, pp. 34–39, Oct. 2025, doi: 10.5120/ijca2025925835.
18. M. Attaran and J. Woods, “Cloud Computing Technology: Improving Small Business Performance Using the Internet,” Journal of Small Business & Entrepreneurship, vol. 31, no. 6, pp. 495–519, Nov. 2019, doi: 10.1080/08276331.2018.1466850.
19. J. Nwoke, “Digital Transformation in Financial Services and FinTech: Trends, Innovations and Emerging Technologies,” International Journal of Finance, vol. 9, no. 6, pp. 1–24, Sep. 2024, doi: 10.47941/ijf.2224.
20. S. AlBenJasim, T. Dargahi, H. Takruri, and R. Al-Zaidi, “FinTech Cybersecurity Challenges and Regulations: Bahrain Case Study,” Journal of Computer Information Systems, vol. 64, no. 6, pp. 835–851, Nov. 2024, doi: 10.1080/08874417.2023.2251455.
21. S. K. Abbas, M. Hussain, and Y. N. Rimal, “Machine Learning-Based Analysis of Technology Acceptance in FinTech: A Behavioral Study Using Digital Wallet Data,” SN Computer Science, vol. 6, no. 6, p. 674, Jul. 2025, doi: 10.1007/s42979-025-04214-8.
22. A. K. Kashyap, “Rethinking FinTech Regulation Under the Indian Data Protection Framework,” Juridical Tribune – Review of Comparative and International Law, vol. 14, no. 3, pp. 363–383, Oct. 2024, doi: 10.62768/TBJ/2024/14/3/02.
23. N. Singh, “Data Security and Consumer Trust in Fintech Innovations Using Technology Adoption Method,” International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, pp. 1–5, May 2024, doi: 10.55041/IJSREM33015.
24. J. Majid, “Financial Technology: Meningkatkan Inclusif Financial EMKM di Era Pandemi Covid-19,” AKUA: Jurnal Akuntansi dan Keuangan, vol. 1, no. 1, pp. 111–121, Jan. 2022, doi: 10.54259/akua.v1i1.155.
25. K. S. Skandalis, “From Credit Fear to Competitive Edge: The Interplay of Entrepreneurship, FinTech and SME Performance,” Journal of Economic and Administrative Sciences, pp. 1–16, Sep. 2025, doi: 10.1108/JEAS-06-2025-0386.
26. F. Ben Bouheni, M. Tewari, A. Salamon, P. Johnston, and K. Hopkins, “Credit Sales and Risk Scoring: A FinTech Innovation,” FinTech, vol. 4, no. 3, p. 31, Jul. 2025, doi: 10.3390/fintech4030031.
27. M. Tang, Y. Hu, Y. (Greg) Hou, L. Oxley, and J. W. Goodell, “Fintech Development, Corporate Tax Avoidance and Firm Value,” International Review of Financial Analysis, 2025, doi: 10.1016/j.irfa.2024.103765.
28. Pooja V., “The Role of AI and ML in Alternative Credit Scoring in Fintech Lending,” Journal of Information Systems Engineering and Management, vol. 10, no. 31s, pp. 16–22, Apr. 2025, doi: 10.52783/jisem.v10i31s.4956.
29. A. Bitetto, P. Cerchiello, S. Filomeni, A. Tanda, and B. Tarantino, “Can We Trust Machine Learning to Predict the Credit Risk of Small Businesses?,” Review of Quantitative Finance and Accounting, vol. 63, no. 3, pp. 925–954, Oct. 2024, doi: 10.1007/s11156-024-01278-0.
30. N. Hardik, “Digitalisation Promotes Adoption of Soft Information in SME Credit Evaluation: The Case of Indian Banks,” Digital Finance, vol. 6, no. 1, pp. 23–54, Mar. 2024, doi: 10.1007/s42521-023-00078-w.
31. N. Karimova, “Application of AI in Credit Risk Scoring for Small Business Loans: A Case Study on How AI-Based Random Forest Model Improves a Delphi Model Outcome in the Case of Azerbaijani SMEs,” arXiv Preprint, pp. 1–22, 2024.
32. C. Duan, “Discovering Current Trends and Forecasting Future Research Directions in FinTech by Way of Co-Word-Burst Analyses,” Industrial Management & Data Systems, vol. 125, no. 2, pp. 458–482, Jan. 2025, doi: 10.1108/IMDS-03-2023-0190.
