Science and Technology Finance Key Laboratory of Hebei Province, Hebei Finance University, Baoding, Hebei, China.
Faculty of Management, Universiti Teknologi Malaysia, Johor Baru, Malaysia.
PLoS One. 2024 Apr 16;19(4):e0300195. doi: 10.1371/journal.pone.0300195. eCollection 2024.
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. 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. 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.
互联网金融已经渗透到千家万户，给人们的生活带来便利的同时也带来了潜在的风险。目前，互联网金融企业正在逐步采用机器学习和其他人工智能方法进行风险预警。不同机构对各种机器学习模型和算法的应用现状如何？是否有一种适用于大多数互联网金融平台和应用场景的最优机器学习算法？学者们已经对这些问题进行了一系列研究；然而，这些研究主要集中在比较特定平台和上下文中的不同算法，缺乏对该领域机器学习应用的全面论述和总结。因此，本文基于 Web of Science 和 Scopus 数据库的数据，对近年来互联网金融风险中机器学习的各个方面进行了系统的文献综述，包括出版物趋势、地理分布、文献重点、机器学习模型和算法以及评价。研究发现，机器学习作为一种新兴技术，无论是基本算法还是复杂的算法组合，在预测准确性、时间效率和互联网金融风险管理的稳健性方面，都比传统的信用评分方法有了显著的进步。然而，不同算法之间存在明显的差异，模型结构、样本数据和参数设置等因素也会影响预测准确性，尽管一般来说，更新的算法往往能达到更高的准确性。因此，没有一种适用于所有平台的一刀切的方法；每个平台都应根据其独特的特点、数据和人工智能技术的发展，增强其机器学习模型和算法，从关键评估指标入手，降低互联网金融风险。
