Genetic Algorithm Based Model For Optimizing Bank Lending Decisions
Expert Systems With Applications (2017)
27 Pages Posted: 1 Nov 2018
Date Written: March 15, 2017
To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in diﬀerent ﬁnancial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank proﬁt in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank proﬁt and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12% to 50%. Moreover, it greatly increases the bank proﬁt by a range of 3.9% to 8.1%.
Keywords: Lending Decision, Genetic Algorithm, Loan Portfolio, Bank Objectives
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