With the rapid development of information technology, artificial intelligence technology and the financial industry began to deeply integrate up. Algorithmic trading, credit card fraud detection and a series of other new technologies being applied to the financial industry all require a large amount of data support. However, due to the increasing amount of online financial data, it is difficult for the majority of investors and financial industry practitioners to obtain the required information in a timely manner. Entity recognition technology, as the basis of natural language processing, can quickly extract effective information from the massive financial texts and can provide effective help for investors and financial industry practitioners. In this paper, we propose a neural network model based on Bert-BiLSTM-CRF, which is applied to recognize financial entities. Through experimental analysis, the model achieves more than 95% of all indicators. Compared with the conventional model, the model has superior performance.
KEYWORDS: Deep learning, Data modeling, Education and training, Random forests, Performance modeling, Machine learning, Industry, Matrices, Mathematical modeling, Detection and tracking algorithms
With the changes of people’s consuming attitudes and the popularization of mobile payment, credit card seems increasingly indispensable in life. However, as the number of issued credit cards and credit lines is increasing, there emerges more and more fraud cases involving credit cards. Due to the rapid development of the Internet industry, the channels for capital flow have become unprecedentedly smooth, making it very difficult to prevent credit card fraud cases. If that continues, the development of banks and other financial institutions in the credit card field would be restricted, which might affect people's daily consumption and even the normal running of the society. The Bayesian Deep Learning method is used to quantify the uncertainty of credit card fraud prediction in this essay. Through experimental analysis, the accuracy of the model is over 99%. Compared with conventional classification models, this model has superior performance.
With the development of the Internet industry and the rise of mobile payment, the risk prevention of credit card fraud has become more difficult. With the continuous expansion of the number of cards issued, the total amount of credit, and the transaction volume, the problem of credit card fraud has become increasingly prominent. This seriously affects the normal operation of financial institutions such as banks, threatens the property safety of users, and even directly threatens the normal operation of society. In this paper, after standardizing the data set, the SMOTE oversampling method is used for sample expansion and synthesis, so that the black and white samples are balanced, and the integrated algorithm is used to train a high-performance credit card fraud detection model. Through experimental analysis, the accuracy of the model is over 99.4%, and compared with the conventional classification model, the model has superior performance.
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