The increasing complexity of financial systems requires intelligent decision-support frameworks capable of improving analytical accuracy while protecting confidential information. Traditional financial intelligence approaches often depend on centralized data processing, which creates challenges related to privacy risks, regulatory compliance, and secure information sharing. This research proposes a Collaborative Financial Intelligence Framework (CFIF) that integrates artificial intelligence, privacy-aware analytics, and secure information protection mechanisms.

The proposed framework enables financial organizations to collaboratively generate insights while maintaining control over sensitive information. It combines intelligent decision models, regulatory-aware processing, and secure data management techniques to support applications such as risk assessment, compliance monitoring, and financial forecasting. The concept is aligned with emerging federated financial ecosystems that enable cross-institutional learning while preserving data sovereignty (Arifin Shawn et al., 2025).

The framework highlights the importance of balancing analytical performance with privacy preservation. AI-based financial regulation provides opportunities for improved monitoring and automated decision-making but also introduces challenges related to transparency and governance (Arner et al., 2020). This research presents a conceptual architecture for developing secure collaborative financial intelligence systems and discusses their practical implications, limitations, and future opportunities.

Collaborative intelligence, financial analytics, privacy preservation, artificial intelligence

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