Saxo Fintech Business School, University of Sanya, Sanya 572000, Hainan, China.
Comput Intell Neurosci. 2022 May 10;2022:5842039. doi: 10.1155/2022/5842039. eCollection 2022.
In recent years, with the continuous increase of financial business, the risk of business is on the rise. Among them, major risk cases are frequent, the cases are increasingly complex, and the means of committing crimes are concealed. The main research contents of this paper include the preprocessing of internal and external financial data and the structure design of recurrent NNs. Its purpose is to design a financial risk control model based on a deep learning NNs, thereby reducing financial risk. The Borderline-SMOTE algorithm is used first to preprocess the sample data, and the oversampling method is used to eliminate the imbalance of the data, and then, the long short-term memory deep NNs algorithm is introduced to process the sample data with time series characteristics. The final experiment shows that LSTM has a better accuracy, reaching 0.9715, compared with traditional methods; the sample preprocessing method and risk control model proposed in this paper have better ability to identify fraudulent customers, and the model itself has faster iteration efficiency.
近年来，随着金融业务的不断增加，业务风险也在上升。其中，重大风险案件频发，案件日益复杂，犯罪手段隐蔽。本文的主要研究内容包括内部和外部金融数据的预处理以及递归神经网络的结构设计。其目的是设计一个基于深度学习神经网络的金融风险控制模型，从而降低金融风险。首先使用 Borderline-SMOTE 算法对样本数据进行预处理，使用过采样方法消除数据的不平衡性，然后引入长短期记忆深度神经网络算法处理具有时间序列特征的样本数据。最终的实验表明，LSTM 的准确率达到 0.9715，优于传统方法；本文提出的样本预处理方法和风险控制模型具有更好的识别欺诈客户的能力，模型本身具有更快的迭代效率。
