The study presents an AI-based trust and safety pipeline focusing on enterprise accounting systems, looking to consumer software platforms like Google Ads and YouTube for guidance. A hybrid model was developed and tested using unsupervised (Isolation Forest, Autoencoder) and supervised (XGBoost) machine learning to detect abnormalities and ensure real-time financial transaction compliance. Critical finance issues like having the same suppliers in multiple places, late journal entries, and not following policies can go unnoticed by traditional controls, but with the pipeline, they are easy to spot and address. Preprocessing included dealing with missing data, creating time-related features, and coding categories, before training the model on an 80/20 split. A 0.99 precision, recall, and AUROC were achieved by the XGBoost model, showing much better results than static rule-based methods and leading to some striking improvements. An AUROC of 0.87 from unsupervised models made anomaly detection more accurate. With help from explainable AI tools such as SHAP and LIME, it was clear that persnumber and various time-based features accounted for the most significant effects on predictions. The simulated enforcement layer highlighted high-risk entries, and graphics (confusion matrix, SHAP plots, comparison charts) highlighted the pipeline’s reliability. Challenges such as unequal classes and difficulty explaining the results do not prevent enterprises from using this pipeline to reduce the workforce’s burden and promote transparency. It may be helpful to study federated learning, blockchain-based records, and reinforcement learning that help thresholds change, while highlighting the need for AI in financial governance infrastructure.