Non-Performing Assets in the Banking Sector with Data Driven Analysis of Causes Impact and Recovery Measures

Abstract

Non-Performing Assets with the aim of identifying the reasons of NPAs, the impact of NPAs on bank performances and the effectiveness of the procedures that are applied to recover the assets. The customary method of the management of NPAs is intensive monitoring with conventional late approach procedures, which do not react well to the dynamic behavior of borrowers and initial warning signals. The innovativeness of the proposed research study lies in its empirical methods that are justified using multi-quantitative analysis methods. The suggested method is the analysis of the mean scores, the correlation analysis, and the linear regression analysis to perform an intensive analysis of NPAs and the recovery processes. The whole analysis procedure was aided by Python environment that made use of statistical calculations and analysis to provide clarity and validity. The findings have not only been found to be accurate on the analytical level but also have demonstrated consistency in standardized measures of analysis at the different variables. The outcomes of the analysis are of so much help to the banking professionals, regulators and all concerned bodies to improve on credit risk management and stabilization in the financial field.