Next-Generation Credit Scoring: Leveraging AI to Integrate Alternative Data for Financial Inclusion

References

[1] Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. arXiv preprint arXiv:1607.00133.

[2] Adegoke, A., Ofodile, C., Ochuba, P., & Akinrinol, O. (2024). On alternative credit scoring models. International Journal of Financial Risk Management Studies.

[3] Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.

[4] Bari, M. H., Juthi, S., Mistry, A. M., & Kamrujjaman, M. (2024). A systematic literature review of predictive models and analytics in AI-driven credit scoring. Journal of Machine Learning, Data Engineering & Decision Science, 1(1), 1–18.

[5] Björkegren, D., & Grissen, D. (2017). Behavior revealed in mobile phone usage predicts loan repayment. arXiv preprint arXiv:1701.08277.

[6] Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076.

[7] Bono, T., Kleinberg, J., Mullainathan, S., & Obermeyer, Z. (2021). Algorithmic fairness in credit scoring. Oxford Review of Economic Policy, 37(4), 867–885.

[8] Demajo, L. M., Vella, V., & Dingli, A. (2020). Explainable AI for interpretable credit scoring. arXiv preprint arXiv:2008.03407.

[9] Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference (pp. 265–284). Springer.

[10] Eagle Alpha. (2024). Alternative data for credit scoring: Enhancing risk assessment accuracy. Eagle Alpha.

[11] Extract Alpha. (2024). Alternative data for credit scoring: A comprehensive guide. Extract Alpha.

[12] Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018). Datasheets for datasets. arXiv preprint arXiv:1803.09010.

[13] Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 29, 3315–3323.

[14] Hlongwane, R., Ramaboa, K. K. K. M., & Mongwe, W. (2024). Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data. PLOS ONE, 19(5), e0303566.

[15] Home Credit. (2018). Home credit default risk [Data set]. Kaggle.

[16] International Finance Corporation & World Bank. (2022). The use of alternative data in credit risk assessment: Guidance notes.

[17] J-PAL. (2024). Using alternative data and artificial intelligence to expand financial inclusion. Poverty Action Lab Blog.

[18] Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. Advances in Neural Information Processing Systems, 30.

[19] Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update. European Journal of Operational Research, 247(1), 124–136.

[20] Ma, L. (2018). Phone usage behaviors and online credit default prediction. Journal of Banking & Finance, 86, 35–50.

[21] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of Machine Learning Research, 54, 1273–1282.

[22] MIT Sloan. (2024). The effect of AI-enabled credit scoring on financial inclusion. MIS Quarterly.

[23] OECD. (2023). Regulatory sandboxes in artificial intelligence. OECD Publishing.

[24] Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2020). The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 86, 105–922.

[25] Roa, L., Zhang, L., & Wang, Y. (2020). Super-app behavioral patterns in credit risk models. arXiv preprint arXiv:2011.09877.

[26] Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

[27] S&P Global. (2023). AI & alternative data: Redefining credit scoring. S&P Global Market Intelligence.

[28] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28.

[29] Sen, A. (1999). Development as freedom. Oxford University Press.

[30] Springer. (2024). Advancing financial inclusion and data ethics: The role of alternative credit scoring. SpringerLink.

[31] Wikipedia. (2024). Alternative data. In Wikipedia. https://en.wikipedia.org/wiki/Alternative_data

[32] Wikipedia. (2024). Criticism of credit scoring systems in the United States. In Wikipedia. https://en.wikipedia.org/wiki/Credit_score_in_the_United_States

[33] Wikipedia. (2024). Open finance. In Wikipedia. https://en.wikipedia.org/wiki/Open_finance

[34] World Bank. (2021). Global Findex Database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. World Bank Group.

[35] Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013). Learning fair representations. Proceedings of the 30th International Conference on Machine Learning, 235–243.

[36] Zhang, X. R. (2024). The application of alternative data in credit scoring. In Proceedings of the International Conference on Data Engineering and Business Applications (pp. 98–105). Atlantis Press.

@article{1710203,
      author = {Godwin David Akhamere},
      title = {Next-Generation Credit Scoring: Leveraging AI to Integrate Alternative Data for Financial Inclusion},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {2},
      pages = {1112-1124},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710203.pdf},
      abstract = {A new suite of AI-powered credit scoring systems that richly incorporate alternative data-including mobile payments, telecommunications data, behavioral digital traces, and psychometric indicators-can provide a new approach to broadening access to credit by members of traditionally underserved communities. This paper presents a conceptual motivation to exploit the predictive accuracy and fairness potential of these non-conventional inputs over the traditional scoring models of the past. Basing the evidence on quantitative research, case studies, and theory analysis we are developing a more specific research question and testable hypothesis. Finally, the paper will also attempt to lay out a road-map on how a robust ethical credit can be deployed, which in a globalized world will need to be inclusive of diversities of economies.},
      month = {August},
  }