Genetic algorithm based model for optimizing bank lending decisions.

The bank lending decisions in credit crunch environments are big challenge.This NP-hard optimization problem is solved using a proposed GA based model.The proposed model is tested using two scenarios with simulated and real data.The real data is collected from Southern Louisiana Credit Union.The proposed model increased the bank profit and improved the system performance. 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%50%. Moreover, it greatly increases the bank profit by a range of 3.9%8.1%.