ABSTRACT
Credit scoring is a method of defining a person’s chances to gain or lose credit from financial institutions. In this work, a bio-inspired algorithm is implemented to optimize the neural network weights. In six credit scoring using Birds Swarm Optimization (BSO), in addition to the types of credit scoring, there are various other types like Experian, Equifax, TransUnion, and so on. The judgmental techniques and credit scoring models support decision-making which involves accepting or rejecting the client's credit. Different credit scoring models have been employed by financial institutions to increase cash flows and profits from customers. Bio-inspired algorithms can be broadly classified into following sections: The present work includes a newly proposed bio-inspired algorithm called the Birds Swarm Optimization inspired from swarm intelligence. BSO uses simple birds swarm behavior to optimize weights as shown in the algorithm in proposed method. From the comparison of experimental results, it is evident that bio-inspired algorithms play a vital role in credit scoring models.
