Adaptive AI-Driven Anomaly Detection Framework for Identity & Access Management in Financial Technology Systems

Identity and Access Management (IAM) is critical for securing digital assets, particularly in financial technology (FinTech) systems, where unauthorized access can lead to significant financial losses. Three formal research questions guide this work: (RQ1) Do AI-driven models statistically significantly outperform traditional rule-based IAM systems in anomaly detection accuracy? (RQ2) Which AI model best balances precision and recall for real-time insider-threat detection under class-imbalanced IAM log conditions? (RQ3) Are the observed performance gains robust and stable across cross-validated experimental folds? This study evaluates the performance of AI-driven anomaly detection models, including autoencoders, random forests, and support vector machines, in detecting unusual user activities and potential insider threats. The Autoencoder model achieved the highest overall accuracy of 94.2% (+/- 0.8% across five-fold cross-validation) with a precision of 92.8% and recall of 91.5%. The Random Forest attained a slightly lower accuracy (92.5%) but excelled in recall (93.2%), highlighting its strength in identifying actual malicious activities. Compared to traditional rule-based IAM methods, which achieved only 78.4% accuracy, AI models significantly improved anomaly detection, particularly for subtle or previously unseen threats. McNemar's tests confirm that all accuracy improvements over the baseline are statistically significant (p < 0.001). The Autoencoder also demonstrated the lowest latency (120 ms), making it suitable for real-time deployment. These results confirm that AI-enhanced IAM systems can effectively strengthen security and operational efficiency in FinTech environments, within the scope of the simulated and publicly available datasets employed in this study.