The Optimizing Financial Data Transfers in the Cloud: A Comparative Analysis of Encryption and Machine Learning Algorithms

Financial Data Transfers in the Cloud Data

Abstract

The development of big data analytics, cloud computing, and machine learning is transforming the financial sector. The application of these technologies in decision-making, risk management, and fraud detection is growing. But obstacles like complicated integration, data security, and regulatory compliance have hindered their wider use. For the purpose to improve financial forecasts, risk management, and customer service, this study looks into how cloud computing and machine learning might be used. Additionally, it discusses the opportunities and challenges associated with integrating cutting-edge encryption technology, such as quantum cryptography and SS-BLAKE-512, in order to secure financial data transmissions. Methods: Data from financial organizations had been acquired for this investigation using cloud computing platforms like Microsoft Azure and Amazon. LSTM, SVM, and autoencoders are examples of machine learning models used in predictive analytics. In order to protect data security during cloud migrations, sophisticated encryption techniques were used. Large-scale financial data handling was accomplished by processing data using frameworks such as Apache Hadoop and Spark. Results: According to the investigation, SVM models were the most accurate at 92.3% as it came to forecasting credit risk, but LSTM networks had 95.3% accuracy in stock price prediction. With 94.7% accuracy in fraud detection, autoencoders turned in the best results. Enhancing data security during cloud migration, SS-BLAKE-512 and quantum cryptography implementation dramatically decreased the danger of data breaches from 17% to 0.5%. Conclusion: Efficiency and security gains in the banking sector are being driven by the combination of cloud computing and machine intelligence. Machine learning algorithms improve forecasting and risk analysis, while cloud platforms offer the scalability required to handle massive datasets. Sophisticated encryption techniques, including SS-BLAKE-512, guarantee safe data migration, strengthening and preparing financial systems for the future.