Faulty diagnostics model in e-commerce using AI

Abstract

Risk administration has increased due to increased online orders to avoid sales invoices. Failure to pay a bill within 90 days of receipt constitutes overdue payment. The credit rating is used to determine the probability that consumers will fail. The CR has been thoroughly studied and several learning algorithms have been proposed. The main goal is to create a CR model for the role of Risk Solution Services (RSS), an industry standard for predicting the parameters of customer default in e-commerce risk assessment. The risk assessment included a general concept, exclusion criteria, and details of the ordering process. The most recent design should operate both independently and in conjunction with the NRC Primary Risk Audit as it is intended to replace the overall screening risk assessment. This article is about a CR implementation of Genetic Programming (GP) with Artificial Intelligence (AI). The dataset includes RSS-enabled purchase requisitions. The results show that the GP pre-risk control model goes beyond the generic CR model in terms of classification accuracy. A system with more discriminatory capacity is produced by combining GP models with the NRC's major risk assessment.

Keywords

• e-commerce
• Credit rating
• GP model
• Accuracy
• Artificial intelligence