Policy-Constrained Neural Networks for Compliance-Aware Decision Support Systems

Abstract

The enterprise decision provides a support system that enhances reliance on the neutral network, automating decision-making in sectors such as finance, healthcare, and even human resources. Although the models achieve strong predictive performance, they do so without explicit regard for regulations, legality, or internal policies. When a prediction is statistically accurate, a lack of consistency may pose compliance risks for model output, which may contradict business standards, regulatory requirements, and even ethical guidelines. A regulatory examination of the AI-driven decision may enhance an enterprise's obligations to ensure compliance, which is directly within a model for deception procedures.

This paper presents a policy-constrained neural network framework that ensures compliance and enables the system to make decisions. The framework provides the implementation of formalized policies derived from regulations, organizational norms, or even governance standards. This can directly interfere with the training process of network neutrals. In reality, by combining a constraint-aware loss function with rule-based penalties, an approach can be proposed that yields model outputs that adhere to the predicted compliance boundaries while preserving predictive efficacy.

The simulation-based experiments will be carried out using an enterprise-inspired decision dataset to make the policy limitations clear. The results show that the policy-constraint model dramatically reduces compliance violations compared to an untrained neural network, with minimal impact on overall accuracy. Illustrating how policy integration shapes model behaviour will lead to the discovery of a policy-contained neural network that provides a viable pathway. The approach will deliver AI-driven decisions aligned with the company and that demonstrate compliance standards, while also facilitating accountable, trustworthy decision automation.