OGAN-POW and Ensemble-Based Learning for Preserving Privacy in Financial Institutional

AI and Blockchain applications are essential for modernizing outdated multimedia business models, technological processes, and industrial processes. It combines smart contracts and frames resilient technology to simplify service prices. The association of significant characteristics like instantly and securely self-verification with blockchain applications eliminated accessing the use of trustworthy third parties. The industrial 4th revolution employs AI Technology (AI) to analyze and retrieve vital information from real-world systems and hence produces the result of evolving scientific and industrial advancement. In order to increase system efficiency, it also uses digital analytics to connect the data with cloud and blockchain repositories. Investigating A.I. technologies and methodologies is still difficult due to security and privacy concerns. To protect privacy in financial institutions, the work suggests an Optimal Generative Adversarial Network-Based Proof of Work (OGAN-POW) consensus and 90%CI-Ensemble learning for malicious activity detection. The proposed framework is capable of performing a smooth node selection, system operations, service warnings, potential threats, and bogus allegations. Finally, Ensemble-based learning is used to examine data exchange and sharing. Ensemble learning evaluates the transactional services to reduce system strain and determines whether the transmission rate varies due to network connectivity issues. The results of the experiments show that the proposed methodology tends to achieve high throughput with low average latency and remains to be highly secure as compared to the methods currently considered to be state-of-the-art.