Title: Forecasting financial markets and credit risk classification using genetic folding algorithm

Authors: Mohammad A. Mezher

Addresses: FBSU University, Saudi Arabia

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%.

Keywords: financial dataset; credit approval; German credit; genetic programming; genetic folding; evolutionary algorithms; classification; risk analysis; Python; toolbox.

DOI: 10.1504/IJEBANK.2022.128566

International Journal of Electronic Banking, 2022 Vol.3 No.4, pp.283 - 300

Received: 15 May 2021
Accepted: 07 Jun 2021
Published online: 26 Jan 2023 *

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