A Low-Latency Edge Computation Offloading Scheme for Trust Evaluation in Finance-Level Artificial Intelligence of Things

The finance-level Artificial Intelligence of Things (AIoT) is going to become a novel media in the 6G-driven digital society. Inside the financial AIoT environment, large-scale crowd credit assessment with the guarantee of low latency has been a general demand. Facing limited computational resources, there is still a lack of effective computation offloading methods for this purpose to ensure low latency. In order to deal with such an issue, this article introduces edge computing mode and proposes a low-latency edge computation offloading scheme for trust evaluation in financial AIoT. With different elements involved in the assessment process being denoted via mathematical description, a multiobjective optimization problem with constraints is formulated. Then, the aforementioned optimization problem is solved by a specific search algorithm, so that optimal task offloading schemes can be found. To assess the performance of the proposal, some simulation experiments are conducted to verify the proposed task offloading method. And it can be reflected from numerical results that latency can be well reduced compared with baseline methods.

I. Introduction

With the development of the economy, people’s consumption psychology and consumption concept have changed dramatically. Personal consumer loans, personal housing loans, credit cards, and other personal credit business has gradually become an important profit growth point for financial enterprises [1]. Therefore, to avoid great financial loss caused by malicious credit fraud of some customers, financial enterprises need to comprehensively consider risk control, which relies on existing data of a customer to assess the customer’s credit [2]. Personal credit evaluation refers to the collection of basic information, personal credit history, and the family environment of customers by credit-granting institutions [3]. Then, these qualitative factors affecting individual credit are reasonably quantified using reasonable evaluation techniques, and the behavior of individual credit rating is evaluated comprehensively [4]. The traditional personal credit evaluation method is mainly based on personal experiences, such as credit scorecards [5]. However, with the advent of the big data era, data volume in the financial industry has gradually increased, and data types also have exploded [6]. It is challenging to deal with such a huge amount of data simply by relying on personal experience, so artificial intelligence technology is introduced to effectively evaluate credit [7].