Research on Financial Fraud Algorithm Based on Federal Learning and Big Data Technology

With the deepening of the digitization degree of financial business, financial fraud presents more complex and hidden characteristics, which poses a severe challenge to the risk prevention and control ability of financial institutions. At the same time, the vigorous development of big data technology provides massive potential information resources. And federated learning, as an emerging distributed machine learning paradigm, can realize multi-party data collaborative modeling under the premise of protecting data privacy. This paper firstly elaborates the basic principle, advantages and unique value of federated learning in solving data silos and protecting user privacy. Aiming at the needs of financial fraud detection, this paper discusses the design of federal learning architecture suitable for this scenario, including selecting suitable model type (such as neural network), setting reasonable data partitioning and updating rules. The central theme of the dissertation revolves around the exploration and execution of an algorithm for detecting financial fraud, which is grounded in federated learning methodologies. With a federated learning framework, each participant trains the model locally and exchanges only model parameters rather than raw data, enabling iterative optimization of the global model while protecting data privacy. To ascertain the efficacy and superiority of the suggested algorithm, a meticulous experimental investigation is both devised and executed. A real-world financial fraud dataset is selected to compare the fraud detection performance using traditional centralized learning and federated learning. Evaluation indicators include accuracy, recall rate, F1 score, etc. The findings from the experiments reveal that the federated learning-based financial fraud algorithm achieves a substantial reduction in the likelihood of data privacy breaches without compromising on high detection accuracies. Furthermore, it adeptly addresses challenges such as imbalanced data distribution and sparse sampling issues, thereby demonstrating its robust practical significance.