Dr. Hualin Li was invited to attend the 6th International Conference on Computer Science and Artificial Intelligence and give a keynote speech

(Interpreter:Jiaqi Huang)

Dr. Hualin Li was invited to attend the 6th International Conference on Computer Science and Artificial Intelligence and give a keynote speech

Net news(article/ picture Jie Li , School of Software and Artificial Intelligence )The sixth International Conference on Computer Science and Artificial Intelligence, organized by Beijing Computer Association, was held on December 9th -11th . This conference gathered experts and scholars in the field of big data and artificial intelligence from 16 countries. At the conference, experts and scholars made special reports on the cutting-edge achievements in machine learning model and algorithm, intelligent recommendation system and data analysis, image detection and recognition, image analysis and methods and other directions.

Dr. Hualin Li, director of the Institute of Artificial Intelligence of the School of Software and Artificial Intelligence of our University and general manager of the Risk Control Center of Chongqing Suyixin Credit Management Co., LTD., was invited to attend the conference. On December 11th, he gave an academic report on his research results entitled "Research on the Credit Risk of Small and Medium-sized Enterprises -- Comparison between expert Model and Artificial Intelligence Model", which was highly recognized and recognized by the experts attending the conference.

Data from the National Bureau of Statistics shows that by the end of April 2022, micro, small and medium-sized enterprises in China accounted for more than 95% of the total employment, accounting for about 80% of the urban employment population, and contributing more than 60% of GDP. Micro and small enterprises are the engine of economic development in our country and also the main force of modern economic construction.

Since the outbreak of COVID-19 in December 2019, the domestic economic situation has been grim, and small and medium-sized enterprises have been strongly affected. The production and operation of most enterprises have been greatly affected, and the credit risk of small and medium-sized enterprises is difficult to predict, which brings great challenges to the stability and healthy development of financial activities.

Dr. Hualin Li's team used advanced artificial intelligence algorithm, combined with big data and cloud computing technology to conduct credit risk analysis for representative small and medium-sized enterprises in China, especially those in Chongqing area. Through in-depth mining of multi-dimensional data of enterprises from 2018 to 2020, such as industrial and commercial, judicial, public opinion and fiscal and tax invoices, in-depth exploration and research was conducted on the changes of credit risk characteristics of small and medium-sized enterprises during the epidemic.

Dr. Hualin Li's research shows that there is a close relationship between the derivative characteristics of corporate tax receipt data and the future default of enterprises, which can accurately predict the future credit risk of enterprises, so as to prevent industrial and regional economic risks. At the same time, the financing risks of enterprises and credit risks of financial institutions were reduced, which promoted economic recovery and development during the epidemic. Digging the credit risk forecasting method of small and medium-sized enterprises will help to rescue enterprises, relieve people's worries, consolidate the good trend of economic stability, and promote the stable and long-term economic development.

Dr. Hualing Li mentioned that the follow-up team will continue to conduct in-depth research on the credit risk of small and medium-sized enterprises, and strive to provide financial institutions with an integrated solution of "data + model + system + customer acquisition + post-loan supervision", so as to effectively help micro, small and medium-sized enterprises and new agricultural subjects to obtain credit faster and more convenient, and form a unique "industry-learning-research" mode.