Credit approval classification through a WASD neuronet

Because the banking business is growing, more people are seeking for bank loans, although banks can only lend to a certain number of applicants because they have a limited amount of assets to lend to. Hence, in order to save a lot of bank resources, the industry of banking is particularly concerned in developing ways to lower the risk element involved in selecting the safe applicant. These days, selecting the safe applicant requires a lot less work thanks to machine intelligence. In light of this, a new weights and structure determination (WASD) neuronet has been developed to address the two issues of credit approval mentioned above, as well as to manage its particular features. We improve the learning process of the WASD algorithm with a novel activation function for optimal adaptation to the credit approval model, motivated by the finding that WASD neuronets perform better than traditional back-propagation neuronets in terms of slow training speed and trapping in a local minima. An experimental study with an insurance company dataset demonstrates superior performance and adaptability to issues.