In recent years, the financial sector has faced growing pressure to adopt advanced machine learning models to derive valuable insights while preserving data privacy. However, the highly sensitive nature of financial data presents significant challenges to sharing and collaboration. This paper presents DPFedBank, an innovative framework enabling financial institutions to collaboratively develop machine learning models while ensuring robust data privacy through Local Differential Privacy (LDP) mechanisms. DPFedBank is designed to address the unique privacy and security challenges associated with financial data, allowing institutions to share insights without exposing sensitive information. By leveraging LDP, the framework ensures that data remains confidential even during collaborative processes, providing a crucial solution for privacy-aware machine learning in finance. We conducted an in-depth evaluation of the potential vulnerabilities within this framework and developed a comprehensive set of policies aimed at mitigating these risks. The proposed policies effectively address threats posed by malicious clients, compromised servers, inherent weaknesses in existing Differential Privacy-Federated Learning (DP-FL) frameworks, and sophisticated external adversaries. Unlike existing DP-FL approaches, DPFedBank introduces a novel combination of adaptive LDP mechanisms and advanced cryptographic techniques specifically tailored for financial data, which significantly enhances privacy while maintaining model utility. Key security enhancements include the implementation of advanced authentication protocols, encryption techniques for secure data exchange, and continuous monitoring systems to detect and respond to malicious activities in real-time.