Hebei Agricultural University Reports Findings in Risk Management (Machine learning in internet financial risk management: A systematic literature review): Risk Management

2024 APR 30 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Risk Management is the subject of a report. According to news reporting originating from Hebei, People’s Republic of China, by NewsRx correspondents, research stated, “Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness.”

Financial supporters for this research include Open Fund Project of Science and Technology Finance Key Laboratory of Hebei Province, Open Fund Project of Science and Technology Finance Key Laboratory of Hebei Province, S&T Program of Hebei, Hebei Social Science Fund, Baoding City Science and Technology and Intellectual Property Bureau.

Our news editors obtained a quote from the research from Hebei Agricultural University, “What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy.”

According to the news editors, the research concluded: “Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.”

For more information on this research see: Machine learning in internet financial risk management: A systematic literature review. PLOS ONE, 2024;19(4). PLOS ONE can be contacted at: Public Library Science, 1160 Battery Street, Ste 100, San Francisco, CA 94111, USA. (Public Library of Science - www.plos.org; PLOS ONE - www.plosone.org)

The news editors report that additional information may be obtained by contacting ZongYi Tian, BoHai College, Hebei Agricultural University, Cangzhou, Hebei, People’s Republic of China. Additional authors for this research include Xu Tian, Saleh F. A. Khatib and Yan Wang.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1371/journal.pone.0300195. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal PLOS ONE is: Public Library Science, 1160 Battery Street, Ste 100, San Francisco, CA 94111, USA.

(Our reports deliver fact-based news of research and discoveries from around the world.)