Predictive Data Governance AI Orchestrated Compliance for Mission Critical Financial Systems
DOI:
https://doi.org/0.15662/IJRAI.2026.0901010Keywords:
Finance, Compliance, Predictive Analysis, Data GovernanceAbstract
This paper will analyse how predictive information governance can decrease compliance risks and enhance the stability of data systems. The quantitative models that are applied to the research include XGBoost, Random Forest, LSTM, and transformer-based time-series models. The findings indicate that predictive indicators such as transaction entropy, schema volatility and lineage complexity have a high capability to predict violations. Following the introduction of predictive controls, the percentage of compliance violations was decreased, reporting accuracy increased, and the time used to review manual reports decreased. Usefulness of the data, completeness of the lineages and lineage stability enhanced. On the whole, the paper indicates that predictive governance has the potential of providing early warning, minimizing false warnings, and streamlining compliance procedures
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