Real-Time Financial Fraud Prediction Using Big Data Streaming on Cloud Platforms

Abstract

Due to the rising level of financial fraud, there has been the need to adopt the use of sophisticated systems that have the capability of identifying and stopping fraud in real time. The current paper suggests the development of a real-time financial fraud prediction model based on the big data streaming on cloud computing systems. The suggested framework combines the use of big data technologies, machine learning algorithms, and cloud computing to provide a scalable and efficient solution to detecting financial frauds. The system can handle very high amounts of the transactional data in real time, and it can make use of the extraction and classification of features to detect potentially fraudulent transactions. The model uses machine learning classifiers, including the Random Forest, Support Vector Machines (SVM) and Neural Networks to predict the anomaly of transactional patterns, which are signs of fraudulent activities. The cloud platform expands the framework by offering flexibility, scalability and high-availability, which means that the system will be able to meet the variable data loads. The overall analysis of the system performance shows that it is highly accurate and has low false-positive rates, and hence it is appropriate to apply in dynamic financial settings. This paper provides the limitation and difficulties of using big data streaming and cloud technology in detecting fraud besides providing the recommendations on further research. The paper explains the necessity of implementing cloud-based big data analytics into the financial fraud prevention plan, where real-time information and better decision-making will be guaranteed.