AI-Based Credit Risk Evaluation in FinTech Applications

  • Authors

    • Arvind Menon Senior Project Manager, Infosys Ltd, India. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2019PII4T1Y

    Published 11-05-2019

  • Artificial Intelligence, Credit Risk Assessment, Fintech, Machine Learning, Credit Scoring, Digital Lending, Predictive Analytics, Financial Technology, Risk Management, Deep Learning

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    Menon, A. (2019). AI-Based Credit Risk Evaluation in FinTech Applications. International Journal of Commerce, Finance and Digital Economy, 2(2), 01-16. https://doi.org/10.67228/3071642X/IJCFDE-2019PII4T1Y

  • Abstract

    Financial Technology (FinTech) has transformed banking and lending by enabling fast, accessible, and scalable digital credit services. As digital lending expands, traditional credit assessment methods are becoming less effective in analyzing complex borrower behaviors and alternative data sources. Artificial Intelligence (AI) has emerged as a powerful solution for credit risk assessment, utilizing machine learning, deep learning, predictive analytics, and natural language processing to evaluate borrower risk more accurately. This study examines AI-based credit risk assessment techniques in FinTech, focusing on credit scoring models, automated underwriting, big data analytics, and real-time risk monitoring. The proposed framework includes data preprocessing, feature engineering, model training, and risk classification. Findings indicate that AI-driven models significantly improve prediction accuracy, fraud detection, risk segmentation, and loan approval decisions compared to traditional methods. Ensemble learning and deep neural networks demonstrate strong performance in large-scale credit assessment tasks. AI also promotes financial inclusion by leveraging alternative data for individuals with limited credit histories. However, challenges related to model transparency, algorithmic bias, data privacy, and regulatory compliance remain. Overall, AI-powered credit risk assessment is a key driver of intelligent, customer-centric, and sustainable FinTech lending systems.

  • References

    [1] D. B. Hand and W. E. Henley, “Statistical classification methods in consumer credit scoring: A review,” Journal of the Royal Statistical Society: Series A, vol. 160, no. 3, pp. 523–541, 1997.

    [2] E. I. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,” The Journal of Finance, vol. 23, no. 4, pp. 589–609, 1968.

    [3] L. C. Thomas, J. N. Crook, and D. B. Edelman, Credit Scoring and Its Applications. Philadelphia, PA, USA: SIAM, 2017.

    [4] S. Lessmann, B. Baesens, H. V. Seow, and L. C. Thomas, “Benchmarking state-of-the-art classification algorithms for credit scoring,” European Journal of Operational Research, vol. 247, no. 1, pp. 124–136, 2015.

    [5] B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen, “Benchmarking state-of-the-art classification algorithms for credit scoring,” Journal of the Operational Research Society, vol. 54, no. 6, pp. 627–635, 2003.

    [6] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer, 2009.

    [7] [J. Brownlee, Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-to-End. Melbourne, Australia: Machine Learning Mastery, 2016.

    [8] J. Y. Yeh and C. H. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients,” Expert Systems with Applications, vol. 36, no. 2, pp. 2473–2480, 2009.

    [9] H. He, W. Zhang, and S. Zhang, “A novel ensemble method for credit scoring: Adaption of different imbalance ratios,” Expert Systems with Applications, vol. 98, pp. 105–117, 2018.

    [10] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

    [11] J. Frost, L. Gambacorta, Y. Huang, H. S. Shin, and P. Zbinden, “BigTech and the changing structure of financial intermediation,” Economic Policy, vol. 34, no. 100, pp. 761–799, 2019.

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