Connected Financial Services in the Automotive Industry: AI-Powered Risk Assessment and Fraud Prevention

Abstract

This paper presents a method for the application of AI in the design of risk assessment model, under a scheme for provision of financial services within the supply chain to the automotive industry. This scheme can be broadly divided into the procurement and distribution of automotive parts. The case of a dealer receiving a loan in order to pay for stock. The car dealer utilizes a DApp on a blockchain network to apply for a deferred insurance loan. The insurance company provides the loan after the car dealer is authenticated; however, the insurance company’s assets are tied up on an insurance contract. The insurance company additionally provides the car dealer with a loan level AI. This AI provides a recommendation as to the credit worthiness of the car dealer, and detects the court-mandated arrest registration of the car dealer’s CEO. Obligated to pay back the loan, the car dealer receives a loan from a stock financing service. The car dealer makes automatic returns of the loan based on financial vehicles. This last endowment is the one that will be modeled.
Risk assessment is defined as an identification of the risk tasks involved in a loan against a type of automotive working capital financing. After this, a causal loop diagram is constructed and a stock and flow model proposed. Lastly, a finite impulse response model is designed using a deep neural network. There exist no reports on the use of AI in the modeling of the aforementioned causal structure and, moreover, the type of automotive working capital financial service to be addressed is entirely novel. As such, the methodology is not merely empirical, but one of analytical modeling and modeling of the system structure on the basis of policy computer simulations of total policy commitments for selected rights. In terms of analysis, the credit AI is developed by a chain-based dealer networking platform that assists potential borrowers such as car dealers with credit evaluation.