Resumen
This research primarily explores the application of Natural Language Processing (NLP) technology in precision financial fraud detection, with a particular focus on the implementation and optimization of the FinChain-BERT model. Firstly, the FinChain-BERT model has been successfully employed for financial fraud detection tasks, improving the capability of handling complex financial text information through deep learning techniques. Secondly, novel attempts have been made in the selection of loss functions, with a comparison conducted between negative log-likelihood function and Keywords Loss Function. The results indicated that the Keywords Loss Function outperforms the negative log-likelihood function when applied to the FinChain-BERT model. Experimental results validated the efficacy of the FinChain-BERT model and its optimization measures. Whether in the selection of loss functions or the application of lightweight technology, the FinChain-BERT model demonstrated superior performance. The utilization of Keywords Loss Function resulted in a model achieving 0.97 in terms of accuracy, recall, and precision. Simultaneously, the model size was successfully reduced to 43 MB through the application of integer distillation technology, which holds significant importance for environments with limited computational resources. In conclusion, this research makes a crucial contribution to the application of NLP in financial fraud detection and provides a useful reference for future studies.