The purpose of this study is to investigate how banking industry-specific variables like regulation, efficiency, and operations affected nonperforming loans (NPLs) in Ukraine and Russia from 1995 to 2019. This study has employed the robust unit root test and cross-sectional dependencies technique along with a new DCCE approach. The dynamic correlated method is employed as it provides the best results when data suffers from cross-sectional dependencies. The study concludes that loose credit policy and lower profitability help in rising NPLs. However, in the context of macroeconomic variables, volatile interest rates, and exchange rate fluctuations are the main reason for NPLs in Russia and Ukraine. The research work also highlights the issue of cross-sectional dependencies and provide substantial methods to resolve the problem of cross-sectional dependencies and provide robust results. Findings will help policymakers to recognize the relevance of industry-specific variables in managing NPLs along with other macroeconomic variables.