[Retracted] Development and Supervision of Robo‐Advisors under Digital Financial Inclusion in Complex Systems

Abstract

With the rapid development of the market economy, there are more and more projects in the financial industry, and their complexity and technical requirements are getting higher and higher. The development of computer technology has promoted the birth of robot consultants, and it is of great significance to use robot consultants to manage and supervise financial industry projects. In order to further analyze the development and supervision of robo-advisors under the digital inclusive financial system, this paper uses complex systems and clustering algorithms as technical support to carry out research. First, the traditional K-means algorithm is used to select the initial clustering center, to improve the noise and outlier processing capabilities, and to build a data mining system based on the improved algorithm. Then, a product design model for robo-advisors is built and the risks of robo-advisors are analyzed from three aspects: technology, market, and law. Analyzing the performance of the improved K-means algorithm, in the operation of the experimental dataset B, the accuracy of the clustering result after 6 iterations reached 97.08%, which shows that the algorithm has good performance. During the trial operation of the data mining system, the four types of customers of financial institutions were accurately clustered, and it was concluded that the main type of customers who brought benefits to financial institutions was high-income customers accounting for 10.75%. Robo-advisory product models are used to build five risk-level investment portfolios and conduct risk backtests. Except for the growth and income portfolio, other portfolios have consistently outperformed the performance benchmark during the analyzed time period. Running the research system of this paper in a financial institution, comparing the capital budget before and after the operation, found that the system can improve the accuracy of the budget and reduce the risk of the robo-advisor for the financial institution.

Legal Supervision Mechanism of Recommendation Algorithm Based on Intelligent Data Recognition

“…Therefore, from the perspective of what should be and the development trend of intelligent investment advisers in the future, China's intelligent investment advisers can be defined as providing customers with personalized investment advice services matching their portraits based on modern financial theories and models and relying on emerging technologies dominated by artificial intelligence asset management services. After combing the risk sources of the legal supervision mechanism of the recommendation algorithm through the big data analysis method, literature [22] found that the risks in traditional financial activities still continue to the intelligent investment adviser, and the legal supervision mechanism of the recommendation algorithm will even amplify the traditional risks, resulting in more serious consequences. Moreover, as the service basis of the legal supervision mechanism of recommendation algorithm, algorithm technology also brings challenges to intelligent investment advisory services.…”

Improvement of the financial management supervision system of universities in the context of big data

“…For the steady development of colleges and universities, establishing a perfect financial management system and effective financial management is crucial [1][2]. At present, the traditional financial management mode of colleges and universities can no longer meet the financial management requirements of colleges and universities in the context of information technology, and there are many shortcomings [3].…”

Digital Financial Inclusion

“…In the literature, many studies on digital financial inclusion have emerged. In the year 2021 alone, many studies examined digital financial inclusion in relation to economic growth (Ahmad et al, 2021), entrepreneurship (Baker, 2021), urban-rural income gap (Ji et al, 2021), poverty reduction (Wang and Fu, 2021), investment diversification (Lu et al, 2021), complex systems (Dai, 2021), research and development (Sun et al, 2021), etc. Despite the increasing number of studies on digital financial inclusion, digital financial inclusionin terms of its meaning, goal, components, instruments and regulatory issues is not generally understood.…”