Big Data in Financial Markets: Advanced Risk Assessment and Fraud Detection Frameworks Using RNN

As the financial markets are constantly changing it is significant to emphasize the importance of the sophisticated risk evaluation and fraud identification systems. From these, the developed framework of this paper integrates big data analytics and machine learning algorithms to improve risk management and to provide real-time solutions for fraudulent activities. The research methodology uses Decision Trees, Random Forests, Support Vector Machines, Convolutional Neural Networks, and Recurrent Neural Networks in handling large volumes of financial data. The performance of these models in type of accurate, precise, recall and time complexity make it clear that deep learning models are a better option for detecting market risks and fraud compared to the traditional machine learning models though the former consumes more computational power than the latter. Moreover, the study discusses feature engineering as an essential step that greatly improves the model result due to merging extra economic factors and sentiment analysis. Web based data analysis takes advantage of the big data processing frameworks like Apache Hadoop and Apache Spark to perform structured and unstructured financial data analysis in real time. The findings reveal that although deep learning models give the better accurate outcomes the other models like Random Forests, Decision Trees yield a higher result in terms of speed used for real time applications. The findings of this work will be helpful in enhancing the fundamental evaluation of numerous confirmed and prospective frauds in the financial industry to help avoid further attacks, plus augment the existing systems for risk assessment to offer superior protection against such illicit activities.