Using Harmony Search Algorithm in Neural Networks to Improve Fraud Detection in Banking System Financial fraud is among the main problems undermining the confidence of customers in addition to incurring economic losses to banks and financial institutions. In recent years, along with the proliferation of fraud, financial institutions began looking for ways to find a suitable solution in the fight against fraud. Given the advanced and varied changes in methods of fraud, extensive research has been conducted to detect fraud. In this paper, the Artificial Neural Network technique and Harmony Search Algorithm are used to detect fraud. In the proposed method, hidden patterns between normal and fraudulent customersxe2x80x99 information are searched. Given that fraudulent behavior could be detected and stopped before they take place, the results of the proposed system show that it has an acceptable capability in fraud detection.

A SemiSupervised Graph Attentive Network for Financial Fraud Detection With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rulebased methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection. To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semisupervised attentive graph neural network, named SemiGNN to utilize the multiview labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest thirdparty online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the stateoftheart methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.