Jonnalagadda Anil Kumar
- PhD Scholar, KL University, Andhra Pradesh, India
- Associate Professor, KL University, Andhra Pradesh, India
A vast majority of the population, especially the rural and low-income strata of the society, lacks access to credit from financial institutions. Lack of documented income, issues in ascertaining the customer identity and creditworthiness, relatively high distribution, and service cost are barriers to financial inclusion. In the post-COVID era, these customers may further be isolated and deprived of formal credit. Artificial intelligence (AI) offers novel ways of assessing creditworthiness, repayment capability while offering customized financial solutions to those lying at the bottom of the pyramid. AI can help develop a cost-efficient and paper-less channel for acquiring, serving, and engaging the customers. Although the initial investment in setting up the digital interface is high, the platform also enables these institutions to process large volumes of low-value transactions in the long run, thereby turning the otherwise cost-ineffective relationships into profitable ones. The application of data analytics can also assist in designing algorithm-based credit scoring models for financial inclusion. The government initiatives like Jan-Dhan Yojana, direct benefit transfer (DBT), Etc. have improved access to basic banking facilities, timely benefits, and access to money, especially during the pandemic. Such initiatives should be scaled up to extend formal credit to the poor, farmers, and self- employed to scale up agricultural production and businesses, thereby fostering economic growth and employment opportunities.
Keywords: COVID, Artificial Intelligence, creditworthiness, data analytics, algorithm-based
[This article belongs to NOLEGEIN Journal of Information Technology & Management(njitm)]
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|Received||October 21, 2021|
|Accepted||October 27, 2021|
|Published||November 28, 2021|