Abstract
Making choices is a basic managerial skill. Some financial markets employ credit monitoring and risk analysis to control risk, forcing financial services and consultancy businesses to develop quantitative decision-making models. Incorporating several methods into the classification system for credit risk leads to more substantial research. Several quantitative credit scoring methods are now available for credit risk assessment. Some disadvantages of metaheuristic techniques include local maxima and early convergence. As a consequence, GFLibPy, a free and open source genetic folding (GF) Python toolkit, is provided. This paper aims to provide: 1) a technical explanation of the 'state-of-the-art' GF algorithm in the credit approval and German credit databases; 2) a road map to help management adopt GFLibPy technologies in banking; 3) GFLibPy increases classification accuracy for new loan applications. In testing phase, the GFLibPy performance varied from 81% to 93%.
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