Federated learning (FL) represents a promising approach that enables the collaborative training of machine learning models without compromising data privacy. This approach is particularly advantageous when handling heterogeneous data dispersed across numerous institutions or devices, as centralized data aggregation is often constrained by privacy concerns and data regulations. In order to address the challenges posed by heterogeneous data, we have devised an adaptive data integration mechanism. This mechanism maps the features of disparate data sources to a unified feature space through the use of feature alignment technology, thereby facilitating the effective fusion of data. This fusion is achieved through the application of statistical alignment and multi- perspective learning technology. Furthermore, in order to safeguard the confidentiality of data, we integrate differential privacy and homomorphic encryption techniques, thereby preventing the disclosure of information during model updates and data transfers. Furthermore, a multi-level privacy protection strategy is proposed, which employs de-identification, secure multi-party computation, and federated averaging technologies at the three stages of data preprocessing, model training, and result aggregation, respectively. This approach ensures data security and facilitates effective model updates. The experimental results demonstrate that the proposed framework exhibits enhanced model performance and robustness in comparison to traditional federated learning methods on a multitude of real-world heterogeneous datasets.

Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.

Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such acts not only disrupt the order of the financial market, but also harm social and economic development, and breed other illegal and criminal acts. With the proliferation of the internet and online payment methods, many fraudulent activities and money laundering have shifted from offline to online, posing a significant challenge for regulators. In this work, we proposed a novel detection model by utilizing the graph neural network. Specifically, the general model structure includes the combination of Graph Convolutional Network (GCN) and Graph Attention Network (GAT), which can effectively capture the complex relationships and features in the financial transaction network. By building a multilayer graph neural network, the model can perform deep learning on the implicit patterns between nodes, thereby improving the accuracy of fraud detection. Experimental analysis results show that the performance of the proposed method on multiple public datasets is better than that of traditional methods, showing its potential in practical application.