AI-Driven Predictive Compliance: Automating Regulatory Monitoring in Investment Management - Venkata Sai Nageen Kanikanti (International Journal of Intelligent Systems and Applications in Engineering, 2025)
With the increasing complexity and dynamics in the regulatory environment in investment management, the regulatory compliance cost and burden experienced has increased at a high rate. Conventional compliance mechanisms, those that depend to a significant extent on manual reviews, and those rule-based systems, are now inadequate to deal with the extent, the velocity and the variety regulatory information. As discussed in this paper, transformative power of Artificial Intelligence (AI) in predictive compliance is a paradigm that foresees breaches of the regulation and the risk of non-comply taking action prior to occurrence. The topics addressed in the research include the AI-powered solutions applicable to real-time regulatory surveillance, fraud detection, as well as asset and portfolio risk score in machine learning (ML), natural language processing (NLP), and graph analytics. The empirical study was carried out on an aggregate of 38 mid-large investment firms in two years (24 months). The findings indicate that the use of AI has resulted in a 45 percent drop in the expense of compliance, a 38 percent upsurge in the precision of reporting to regulators, and the notably lower number of fines and legal charges. The Monte Carlo simulations revealed the same levels of return on investment (ROI) volumes 9-15 percent after integrating AI, whereas the splines and radar plots indicated the operational and strategic upsurps, such as the improvement of customer retention and improved audit readiness. The research paper provides a constructive comparison of different types of AI model like XGBoost, LSTM, and Random Forest in the context of anomaly detection because, out of these, the precision accuracy was the highest (90.2 percent) and false positive rate was the lowest (2.9 percent) by XGBoost. Other challenges treated in the paper highlighted include model explainability, data governance, and alignment to ethical AI principles that continue to be an obstacle to adoption. Conducting a synthesis of technical results and strategic consequences, the paper presents a governance-aligned framework of AI to fit the investment management industry. Based on this study, it can be concluded that AI is not an option of technology upgrading but a strategic goal of companies that want to be resistant and adaptable to a regulated, competitive marketplace. The change of the reactive and predictive compliance enables organizations to predict and pre-empt risks, automated monitoring, and accumulates sustainable regulatory trust.