AI-Driven Credit Risk Assessment in Agriculture: A Case Study of Indian Commercial Banks

Ranjit Singh; Meenakshi Mritunjay

doi:10.69968/ijisem.2024v3si2118-125

Authors

Ranjit Singh Associate Professor, Department of Applied Economics, Faculty of Commerce, University of Lucknow
Meenakshi Mritunjay Research Scholar, Department of Applied Economics, University of Lucknow.

DOI:

https://doi.org/10.69968/ijisem.2024v3si2118-125

Keywords:

Credit Risk Assessment, Agriculture, Indian Commercial Banks

Abstract

India's economy is based mostly on agriculture, which employs about half of the labour force and accounts for 17–18% of GDP (gross domestic product). The agricultural industry is full with uncertainties, despite its crucial importance. These uncertainties include unpredictable weather patterns, volatile market prices, and the inherent risk of crop failure. These difficulties make it more difficult to obtain sufficient funding in a timely manner, which is necessary for farmers to make investments in infrastructure, technology, and inputs that raise production. Commercial banks have always depended on traditional techniques for assessing credit risk, which frequently fall short in effectively assessing the intricacies of agricultural concerns. Banks have taken a cautious stance as a result, restricting the amount of credit that is available to the rural sector.

References

[1] Basso, B., Cammarano, D., & Carfagna, E. (2013). Review of crop yield forecasting methods and early warning systems. European Commission, JRC Scientific and Technical Reports.

[2] Ghosh, S., Sinha, R., & Mahajan, A. (2021). AI in financial inclusion: Opportunities and challenges. Journal of Financial Services Research, 48(2), 159-178.

[3] Iyer, S., Khanna, T., & Varma, A. (2019). Ethical implications of AI in financial services: A review. Journal of Banking and Financial Technology, 3(1), 45-56.

[4] 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. https://doi.org/10.1016/j.jbankfin.2010.06.001

[5] Kumar, R., Aggarwal, V., & Gaur, A. (2018). AI in agricultural credit: Challenges and opportunities in the Indian banking sector. Journal of Rural and Agricultural Research, 18(1), 23-31.

[6] Patel, R., Raj, A., & Desai, V. (2022). AI in agricultural credit assessment: A case study of sugarcane farmers in Maharashtra. International Journal of Agricultural Economics, 7(3), 165-176.

[7] Sharma, P., & Singh, M. (2020). AI-driven credit risk assessment: A case study of Indian commercial banks. Journal of Banking and Finance, 9(4), 112-125.

[8] Wang, Y., Luo, X., & Zhang, J. (2020). Machine learning and AI in credit risk assessment: A review. IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2409-2423.

[9] Government of India. (2020). Economic Survey 2019-20: Vol I. Ministry of Finance, Government of India.

[10]Reserve Bank of India. (2020). Handbook of Statistics on the Indian Economy 2019-20. Reserve Bank of India.

[11] National Bank for Agriculture and Rural Development. (2022). Annual report (Various issues). Mumbai, India.

[12]Agarwal, P., & Sinha, S. (2020). The role of commercial banks in agricultural credit in India. Journal of Rural Development and Management, 15(2), 112-127.

[13] Bhardwaj, R., & Singh, T. (2019). Artificial Intelligence in banking: Applications, challenges, and opportunities. International Journal of Financial Studies, 7(3), 45-61.

[14] Narayanan, S., & Joshi, R. (2018). Agricultural finance in India: Challenges and prospects. Indian Journal of Agricultural Economics, 73(4), 425-438.

[15]Sharma, V., & Patel, A. (2021). AI-driven credit risk assessment in Indian agriculture. Asia-Pacific Financial Review, 12(1), 39-54.