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.
Highlights
In order to build a more complete robo-advisory supervision system, reduce risks, and improve the digital level of inclusive finance, this paper studies the development and supervision of robo-advisors based on complex systems and clustering algorithms. e innovations of this research are as follows
In order to verify the effectiveness of the improved K-means clustering algorithm, three datasets (A, B, and C) in the UCI database are randomly used as experimental datasets. e traditional K-means clustering algorithm, Clara algorithm, and the improved algorithm of this research are used for comparative analysis. e details of the experimental dataset are as follows
Complex systems can be found in every corner of life. e development of digital inclusive finance has gone through the four stages of microfinance, microfinance, inclusive finance, and digital finance
Summary
In the operation of financial activities, there are many projects and tasks in parallel, which brings great impact to the traditional financial management concept. As an important application of financial technology in the field of wealth management, the mode of intelligent investment adviser is more complex. In the era of big data, there are many problems in the development and supervision of intelligent investment advisers [1]. Complexity science is an emerging research form that reveals the operation laws of complex systems [2]. E development and supervision of robo-advisors in the digital age is an extremely complex research object. Erefore, it is a unique and meaningful new idea to study the development and supervision of digital inclusive finance and robo-advisors from the perspective of complex systems Complexity science is an emerging research form that reveals the operation laws of complex systems [2]. e development and supervision of robo-advisors in the digital age is an extremely complex research object. erefore, it is a unique and meaningful new idea to study the development and supervision of digital inclusive finance and robo-advisors from the perspective of complex systems
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