Enterprise AI Analysis

To achieve a good learning effect, machine learning algorithms require data preprocessing, feature selection, model training, evaluation, and adjustment [11, 12]. Decision trees are fundamental algorithms for classification and regression, mimicking human decision-making processes. Random forest improves diversity and generalization by constructing multiple decision trees through playback sampling and random feature selection [24, 25]. XGBoost, an optimized version of GBDT, enhances training speed and prediction performance by iteratively correcting errors of weak learners [26]. For natural language processing, CBOW (Continuous Bag of Words) and Skip-gram models predict words based on context to capture semantic relationships [Eq. (1)]. The attention mechanism calculates similarity between Query, Key, and Value to form a comprehensive prediction model [Eqs. (2) & (3)]. Focal Loss addresses sample imbalance by assigning different weights to samples [Eqs. (4) & (5)].

The system integrates basic financial management functions (vouchers, fixed assets, salary processing) with a core function of risk control analysis using machine learning algorithms [38, 39]. Principal Component Analysis (PCA) is used for feature extraction and dimensionality reduction by analyzing the covariance structure of multidimensional data, transforming it into a set of new feature vectors [29, 30]. PCA identifies the main direction of data by selecting feature vectors with maximum variance [Eqs. (6), (7), (8), (9)], retaining most information while simplifying complexity. The overall computing architecture involves multiple multi-layer perceptron (MLP) for feature extraction, node and edge feature embedding, and a GNN layer for processing. The system also uses a 'timeline cutting method' for data segmentation: past data for training and future data for testing, ensuring predictive performance evaluation.

Model performance is evaluated using accuracy, recall rate, and F1 score [Eq. (10)]. In binary classification, samples are categorized into true examples (TP), false positive examples (FP), true negative examples (TN), and false negative examples (FN). True Positive Rate (TPR) and False Positive Rate (FPR) are calculated [Eqs. (11) & (12)]. The machine learning model showed an accuracy of 0.9459, recall of 0.8418, F1 score of 0.4822, and AUC of 0.9641, significantly outperforming traditional methods (Table 2). Resampling methods, particularly machine learning algorithms, notably improved prediction effect with accuracy increasing by 4.3% and AUC by 2.3% (Fig. 3). The fusion model, especially with XGBoost, showed significant improvements in accuracy and F1 value, surpassing traditional models. XGBoost identified 'equity pledge ratio' as the core risk indicator (Fig. 9). The model training loss curves indicate efficient parameter optimization with SSA (Fig. 5). The model converges after ten iterations, with AUC exceeding 99% (Fig. 11).