A Compliance Service Mode Selection Model Based on Machine Learning Algorithms

This study is based on multidimensional compliance feature data of enterprises, and deeply explores and constructs an intelligent selection model for compliance service modes based on machine learning algorithms. By systematically comparing the performance of decision trees, random forests, support vector machines (SVM), BP neural networks, and the non-linear weight particle swarm optimization based support vector machine (NWPSO-SVM) classification model proposed in this study on the same dataset, the empirical results clearly show that the NWPSO-SVM model exhibits excellent and comprehensive classification ability in compliance service mode selection tasks. The model achieved the best results in all key evaluation metrics, with an accuracy of 0.894, a recall of 0.894, a precision of 0.886, and a stable F1 Score of 0.886. This series of significantly leading indicator values not only confirms the high accuracy of the model's prediction results. By utilizing this model for precise analysis and pattern prediction of enterprise compliance characteristics, potential compliance risk points can be identified proactively at the beginning of data service launch or product design, and appropriate service patterns can be matched. This enables compliance requirements to be more intelligently and efficiently embedded at the source of business processes, achieving a transition from passive response to active prevention, effectively improving the accuracy and effectiveness of pre regulation, reducing compliance costs and violation risks in the later stage, and laying a solid technical foundation for building a secure, reliable, and trustworthy data service ecosystem. In summary, the NWPSO-SVM model has been proven to be an ideal tool for intelligent and precise selection of enterprise compliance service models due to its significantly superior comprehensive performance.