THE DEVELOPMENT OF MODERN METHODS IN ASSESSING CREDITWORTHINESS

Authors

  • Abduvaliyev Adxam Abdiraximovich Banking and finance academy of the Republiс of Uzbekistan

Keywords:

Creditworthiness, credit risk assessment, credit scoring models, alternative data, artificial intelligence, machine learning, psychometric models, blockchain, financial technology, algorithmic bias, financial inclusion, decentralized credit networks.

Abstract

The assessment of creditworthiness has undergone significant transformation, evolving from traditional models focused on financial ratios and credit history to more sophisticated, data-driven methods. This article explores the development of modern approaches in evaluating credit risk, highlighting key advancements such as credit scoring models, the integration of alternative data, behavioral and psychometric assessments, and the use of artificial intelligence (AI) and machine learning (ML) algorithms. The emergence of blockchain technology and decentralized credit networks also promises to reshape credit evaluation. While these innovations offer improved accuracy and inclusivity, they also raise concerns related to privacy, algorithmic bias, and regulatory challenges. This article examines the balance between leveraging new technologies and addressing ethical considerations in the modern creditworthiness assessment landscape.

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References

Arráiz, I., Bruhn, M., & Stucchi, R. (2020). Psychometric credit scoring in emerging markets: The impact on loan repayment behavior. Journal of Development Economics, 146, 102512.

Buehler, K., Freeman, A., & Hulme, R. (2009). The risk revolution. McKinsey Quarterly.

Citron, D. K., & Pasquale, F. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89(1), 1-33.

Finlay, S. (2008). Credit scoring, response modeling, and insurance rating: A practical guide to forecasting consumer behavior. Palgrave Macmillan.

Iansiti, M., & Lakhani, K. R. (2017). The truth about blockchain. Harvard Business Review, 95(1), 118-127.

Jagtiani, J., & Lemieux, C. (2017). Fintech lending: Financial inclusion, risk pricing, and alternative information. Federal Reserve Bank of Philadelphia Working Papers, 17-17.

Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787.

Ong, S. W., Tee, E. S., & Lai, L. F. (2015). Enhancing credit risk models with machine learning. Journal of Risk Model Validation, 9(1), 45-62.

Thomas, L. C. (2000). A survey of credit and behavioral scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149-172.

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Published

2024-10-15

How to Cite

Abduvaliyev Adxam Abdiraximovich. (2024). THE DEVELOPMENT OF MODERN METHODS IN ASSESSING CREDITWORTHINESS. Journal of Applied Science and Social Science, 14(10), 74–77. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/314

Issue

Vol. 14 No. 10 (2024): Journal of Applied Science and Social Science

Section

Articles

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