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This study aims at testing the optimal mechanism of bank lending decisions using artificial intelligence techniques. It is based on a sectoral diversification strategy to minimise risk and maximise return of credits facilities portfolio and support bank managers in their decision making. In this context, we suggest a dynamically self-regulating method to optimise the bank lending decisions, by the application of the metaheuristic approach represented by genetic algorithms optimization. It has been used and improved in more recent empirical studies; the method has become a hot research topic. The reason for choosing GA is its convergence and flexibility in solving multi-objective optimization problems, such as credit assessment, portfolio optimization, and bank lending decision. Furthermore, we have also used Markowitz model to construct a mean-variance optimization problem, based on estimate expected return and risk. Finally, the optimal loans portfolio, among 11 economic activity sectors in the Kingdom of Saudi Arabia during the period 1998-2020, has been selected. We have also compared the results of the genetic algorithm with the classic Markowitz model in its static form.

Keywords

Credit Risks, Optimal Loans Portfolio, Return & Risk, Sectoral Diversification, Genetic Algorithms optimization.

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References

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