Enhancing Financial Data Security And Business Resiliency In Housing Finance: Implementing AI-Powered Data Analytics, Deep Learning, And Cloud-Based Neural Networks For Cybersecurity And Risk Management

Abstract

Diverse aspects of enhancing financial data security and business resiliency amid a challenging context consisting of housing finance are examined. AI-powered data analytics, deep learning, and cloud-based neural networks are applied to concrete aspects of financial data management. These technologies are empowering the diligent examination of granular outlier detection. Novel data processing and data quality issues are unravelled. The broader objectives, context, and impactful outcomes of pertinent use cases, as well as future research, are discussed. There is rapid growth and expanding ventures known as FinTech in the housing sector. The developments introduce significant challenges as well as modernisation opportunities for technologically lagging small and medium-sized players. It is discussed how newly empowered AI-powered data analytics, previously focused mainly on a detailed risk assessment, robust and efficient cybersecurity, and an improved handling of large heterogeneous data can assist in adaptation efforts, as well as in becoming more competitive FinTech operators in housing. The fintech evolution primarily focussed on the institutional and regulatory context, relevant vulnerabilities, and possible opportunities for financial data housing managers. These provided anticipated findings and marked AI-based toolboxes capable of addressing striking compliance requirements with EU regulations aimed to enhance the financial data security of small financial firms, including housing financiers. A detailed method of granular applications of the tools was also presented, highlighting experimental comparative studies with well-chosen small housing financiers. Finally, the presented essay emphasises the broader promising aspects of the proposed approach from the housing finance community's viewpoint. With FinTech developments, there is a significant drive to enhance the robustness, efficiency, or scope of the machine-learning-based handling of financial instruments as well as Real-Estate related to the data.