Financial Fraud Detection Based on Computer Big Data Analysis and Artificial Intelligence

In view of the complex large-scale transaction data in the financial field, traditional fraud detection methods are difficult to effectively identify potential fraudulent behaviors. The financial fraud detection method based on computer big data (BD) analysis and machine learning technology is introduced to improve the detection accuracy and reduce the false alarm rate. First, by collecting large-scale financial transaction data, preprocessing and constructing feature vectors to ensure the quality and relevance of the data. Then, the decision tree (DT) algorithm is used to train the data, and a classification system of transaction patterns is established through the DT model. Then, the Random Forest (RF) technology in the ensemble learning method is used to combine the results of multiple DTs to improve the robustness and accuracy of the model. Finally, through cross-validation and performance evaluation, the model parameters are optimized to detect fraudulent behaviors. In terms of precision, it increased from 0.88 to 0.92, indicating that the model can better identify positive samples and reduce false alarms; the increase in recall (from 0.86 to 0.94) further shows that the optimized model is more efficient in capturing potential fraudulent behaviors and significantly reduces missed reports. By adopting DT and RF technologies in machine learning and combining them with big data analysis, the effectiveness of financial fraud detection can be significantly improved, providing financial institutions with more effective risk management tools.