Micro Factor Analysis with the Development of Fintech Strategy to Reduce NPA Occurrences Using Artificial Intelligence Model for Government Sponsored Schemes

Abstract

Banking is a sector which is the core business beam which balances the national economical status by providing financial assistance to strengthen the business proposals. However, it is noted that banks facing timely repayment issues leading to the increase in NPA status. Apart from this, it is necessary to model business domain specific structure of sanctioning the loan. Many similar business owners’ demand loans from same geographical locations which may lead to hamper the businesses and that may lead to defaulter status with the repayment issues. Consequently, in Government sponsored schemes, NPA percentage may further increase with reason of business failures. To avoid such failures of Government sponsored schemes, it is necessary to setup automated strategically model considering the micro factors in banking. Hence, this paper presents the “Cumulative ultra age fintech Artificial Intelligence” strategy which suggests the geographical loan sanctioning process which curtails the target based lending, quick response (QR) code system for repayment and group loan and chained repayment system.