This paper starts from the application scenarios and pain points in the financial industry, focusing on the synthesis of financial technology (Fintech) product risk data. By integrating existing research in the field of data synthesis, we propose a novel Fintech Data Synthesis System (FinDS2). To implement this system, we have designed and developed a data synthesis product that leverages cloud computing and micro-services. The product includes a cloud-native data lake, an intelligent algorithm engine, and a SAAS tool engine. It collects data on regulatory rules and penalties in the finance industry and offers highly configurable and automated features. By tailoring the synthesis process to Fintech application scenarios, our proposed FinDS2 generates high-quality synthetic data efficiently. Through practical validation, we demonstrate that the synthesized data closely resembles the original data, effectively meeting the data synthesis needs for managing Fintech product risk.

Financial fraud detection plays a crucial role in maintaining financial security and risk control. Many types of financial data, such as transaction networks and entity relationship networks, can be represented as graph-structured data. Graph neural networks, exemplified by the Beta Wavelet Graph Neural Network (BWGNN), are instrumental in financial fraud detection. However, current research on adversarial attacks against graph neural networks primarily focuses on vanilla GNNs, with limited exploration into adversarial attacks targeting models like BWGNN used in financial fraud detection. This paper presents effective adversarial attack methods tailored to BWGNN. Leveraging node injection as an adversarial attack method, we construct surrogate models that closely resemble the structure of BWGNN, significantly enhancing the attack performance. Additionally, by incorporating dropout layers after the input layer of the surrogate model, we further enhance the attack effectiveness. This paper reveals the adversarial vulnerabilities of financial fraud detection models represented by BWGNN, which holds significant implications for enhancing the security of fraud detection models applied in critical financial security domains.

Artificial intelligence technology, such as fraud detection and biometrics, has recently been widely used in financial security. However, the related machine learning models may have algorithmic risks, and attackers can use loopholes in the models themselves to circumvent censorship or even steal private data. Our research team has designed and implemented a cloud service-based algorithmic risk detection platform for fintech products using advanced AI technologies to address this challenge. The platform can assess the algorithmic risk of machine learning models used for regulation in several common scenarios in the financial sector and provide early warnings of potential risk factors. Our platform, built upon cloud services, boasts high performance and embraces the principle of loose coupling. Our research aims to furnish the FinTech industry with a pragmatic tool for model algorithm risk detection.

Dynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.