A Critical Review of Consumer Behaviour and Credit Supply: Evidence from an Australian FinTech Lender
DOI:
https://doi.org/10.63822/w8wder52Keywords:
alternative data , consumer behaviour , credit supply , financial inclusion , fintech lendingAbstract
This study critically reviews the relationship between consumer behaviour and credit supply decisions, focusing on evidence from an Australian FinTech lender. FinTech lenders increasingly use alternative data, such as bank transaction patterns, to assess creditworthiness beyond traditional metrics. The reviewed literature indicates that specific behaviours, notably gambling and intensive cash usage, negatively impact the offered loan amounts. While lenders generally favour mature demographics, these preferences are moderated by risky consumption signals. However, this critical review identifies several limitations in current research, including observational designs that prevent causal claims and potential data selection bias. Furthermore, the modest economic impact of these behavioural variables raises questions about their incremental predictive value. Crucially, penalizing cash usage and repeated borrowing may inadvertently foster algorithmic bias, disproportionately disadvantage vulnerable populations, and conflict with broader financial inclusion objectives. Future research must prioritize causal identification, default analysis, and algorithmic fairness. Ultimately, the integration of alternative data requires robust regulatory frameworks to balance technological innovation with consumer protection.
Downloads
References
Alamsyah, A., Hafidh, A. A., & Mulya, A. D. (2025). Innovative Credit Risk Assessment: Leveraging Social Media Data for Inclusive Credit Scoring in Indonesia’s Fintech Sector. Journal of Risk and Financial Management, 18(2). https://doi.org/10.3390/jrfm18020074
Anh, L. (2024). AI Chatbots in English Language Learning: A Critical Review. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (Online), 3, 185–195. https://doi.org/10.60087/jklst.vol3.n2.p195
Bea, M. D., & Bley, K. (2022). (Un)conditional consumer protections in high-cost lending regulation: Impacts on local lending geographies. Journal of Consumer Affairs, 56(4), 1561–1596. https://doi.org/10.1111/joca.12481
Bhattacharya, S., & Sachdev, B. K. (2024). Regulatory Challenges and Opportunities for Future Fintech and Green Finance. In V. Sharma, M. Gupta, N. Arora, & A. Rocha (Eds.), Blockchain’s Transformative Potential of Financial Technology for Sustainable Futures (pp. 103–122). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70219-8_7
Brown, M., Hentschel, N., Mettler, H., & Stix, H. (2022). The convenience of electronic payments and consumer cash demand. Journal of Monetary Economics, 130, 86–102. https://doi.org/10.1016/J.JMONECO.2022.06.001
Dede, E., Kara, Z. B., & Sezgin, T. M. (2026). A critical review of sketch collection methods: Remembering how humans really sketch. Computers & Graphics, 104603. https://doi.org/10.1016/J.CAG.2026.104603
Del Sarto, N., & Ozili, P. K. (2025). FinTech and financial inclusion in emerging markets: a bibliometric analysis and future research agenda. In International Journal of Emerging Markets (Vol. 20, Number 13, pp. 270–290). Emerald Publishing. https://doi.org/10.1108/IJOEM-08-2024-1428
Desai, P. S. (2024). The Role of AI and ML in Alternative Credit Scoring in Fintech Lending. In Journal of Information Systems Engineering and Management (Vol. 2025, Number 31s). https://www.jisem-journal.com/
Doerr, S., Gambacorta, L., Guiso, L., & Sanchez Del Villar, M. (2023). Privacy regulation and fintech lending (BIS Working Paper). www.bis.org
Duvalla, V. R. (2025). AI-Powered Credit Risk Assessment: Transforming Lending in FinTech. European Journal of Computer Science and Information Technology, 13(23), 115–131. https://doi.org/10.37745/ejcsit.2013/vol13n23115131
Franzke, S., Wu, J., Froese, F. J., & Chan, Z. X. (2022). Female entrepreneurship in Asia: a critical review and future directions. Asian Business and Management, 21(3), 343–372. https://doi.org/10.1057/s41291-022-00186-2
Gao, M., Leung, H., Liu, L., & Qiu, B. (2023). Consumer behaviour and credit supply: Evidence from an Australian FinTech lender. Finance Research Letters, 57. https://doi.org/10.1016/j.frl.2023.104205
Ha, D., Le, P., & Nguyen, D. K. (2025). Financial inclusion and fintech: a state-of-the-art systematic literature review. Financial Innovation, 11(1), 69. https://doi.org/10.1186/s40854-024-00741-0
He, H., Wang, Z., Yan, X., Ouyang, L., Liu, D., Zhao, M., Li, A., & Pan, J. (2026). From plastisphere to biorecycling: a critical review of plastic biodegradation scales, risks, and policy needs. Environmental Reviews, 34, 1–11. https://doi.org/10.1139/ER-2025-0266
Ioannidou, V., Pavanini, N., & Peng, Y. (2022). Collateral and asymmetric information in lending markets. Journal of Financial Economics, 144(1), 93–121. https://doi.org/10.1016/j.jfineco.2021.12.010
Karami, A., & Igbokwe, C. (2025). The impact of big data characteristics on credit risk assessment. International Journal of Data Science and Analytics, 20(5), 4239–4259. https://doi.org/10.1007/s41060-025-00753-8
Kemala, P. N., Koesrindartoto, D. P., & Hakam, D. F. (2025). Advancing inclusive finance through financial technology (FinTech): peer-to-peer (P2P) and digital lending as catalyst for financial inclusion. Digital Finance, 8(1), 1. https://doi.org/10.1007/s42521-025-00166-z
Kumar, A., Dhanka, S., Sharma, A., Sharma, A., Nain, M., Kumar, P., Gupta, A. R., Bansal, J., Saxena, N. K., & Pant, R. (2026). A Comprehensive Review of Bias in AI, ML, and DL Models: Methods, Impacts, and Future Directions. Archives of Computational Methods in Engineering, 33(4), 5743–5773. https://doi.org/10.1007/s11831-025-10483-6
Maggio, M. Di, & Ratnadiwakara, D. (2021). Invisible Primes: Fintech Lending with Alternative Data. Management Science.
Magnuson, J. K., Ferraro, C. C., & Townsend, T. G. (2026). Optimization of pretreatments for municipal solid waste incineration ash reuse in cement manufacture: A critical review. Resources, Conservation and Recycling, 231, 108943. https://doi.org/10.1016/J.RESCONREC.2026.108943
Mamun, A. Y., & László, V. (2025). Advancing sustainability through financial inclusion and sustainable finance: a systematic literature review. Digital Finance, 7(4), 853–870. https://doi.org/10.1007/s42521-025-00142-7
Marks, M. J., Gyenes, P., & Pittkowski, R. K. (2026). High entropy materials in electrocatalysis: A critical review of structure–property understanding. In Electrochimica Acta (Vol. 557). Elsevier Ltd. https://doi.org/10.1016/j.electacta.2026.148495
Mêda, P., Calvetti, D., Corvacho, H., Hjelseth, E., & Sousa, H. (2026). European sustainable built environment vision framework: critical review and expansion. Smart and Sustainable Built Environment, 1–25. https://doi.org/10.1108/SASBE-09-2025-0585
Perdana, A., Arifin, S., & Quadrianto, N. (2025). Algorithmic trust and regulation: Governance, ethics, legal, and social implications blueprint for Indonesia’s central banking. Technology in Society, 81, 102838. https://doi.org/10.1016/J.TECHSOC.2025.102838
Rehman, K., & Mia, M. A. (2024). Determinants of financial literacy: a systematic review and future research directions. Future Business Journal, 10(1), 75. https://doi.org/10.1186/s43093-024-00365-x
Revolusi, P., & Febriandy, R. K. (2025). Developing AI Regulations in Indonesia: Policy Recommendations Based on Comparative Policy Analysis from the European Union, the United States, and Singapore. In Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) (Vol. 6, Number 2). https://journal.stmiki.ac.id
Roy, J. K., & Vasa, L. (2025). Transforming credit risk assessment: A systematic review of AI and machine learning applications. Journal of Infrastructure Policy and Development, 9(1), 9652. https://doi.org/10.24294/jipd9652
Sawant, J., Guru, R., Grewal, D., Talekar, S. C., & Kulkarni, S. P. (2024). SUSTAINABILITY IN TEXTILES: A CRITICAL REVIEW OF ECO – FRIENDLY PRACTICES AND MATERIALS. ShodhKosh: Journal of Visual and Performing Arts, 5(2). https://doi.org/10.29121/shodhkosh.v5.i2.2024.891
Selcuk, C. (2025). Asymmetric Information and Credit Rationing in a Model of Search. Games, 16(1). https://doi.org/10.3390/g16010001
Thomas, M., Tayade, R. J., Shamugavijayakumar, V., & Natarajan, T. S. (2026). A critical review on the versatility of polysaccharides biopolymer based nanocomposites: Classification, properties, and applications. Next Research, 9, 101659. https://doi.org/10.1016/J.NEXRES.2026.101659
Tigges, M., Mestwerdt, S., Tschirner, S., & Mauer, R. (2024). 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, 205, 123491. https://doi.org/10.1016/J.TECHFORE.2024.123491
Vijayagopal, P., Jain, B., & Ayinippully Viswanathan, S. (2024). Regulations and Fintech: A Comparative Study of the Developed and Developing Countries. Journal of Risk and Financial Management, 17(8). https://doi.org/10.3390/jrfm17080324
Warokka, A., Sartika, D., & Aqmar, A. Z. (2025). Digital Credit and Debt Traps: Behavioral and Socio-Cultural Drivers of FinTech Indebtedness in Indonesia. FinTech, 4(4). https://doi.org/10.3390/fintech4040062
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kristiana Sri Kristiana Sri Utami, Bhenu Artha (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
