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Claim this profileWorks148 from public data
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- Credit Card Fraud Detection Using Convolutional Neural Networks313
A CNN-based fraud detection framework, to capture the intrinsic patterns of fraud behaviors learned from labeled data is proposed, on which a convolutional neural network is applied to identify a set of latent patterns for each sample.
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- Graph neural networks for financial fraud detection: a review173
This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods.
- F-HMTC: Detecting Financial Events for Investment Decisions Based on Neural Hierarchical Multi-Label Text Classification160
A LSTM Relational Graph Convolutional Network model, which models the connection among stocks with their correlation matrix, which outperforms the baseline models and enables the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
- Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems156
This work is the first to identify and formally define the communication redundancy issue present in current LLM-based multi-agent pipelines, and efficiently performs one-shot pruning on the spatial-temporal message-passing graph, yielding a token-economic and high-performing communication topology.
- Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction151
This paper proposes a temporal and heterogeneous graph neural network-based (THGNN) approach to learn the dynamic relations among price movements in financial time series and deploys the proposed THGNN in a real-world quantitative algorithm trading system, where the accumulated portfolio return obtained by the method significantly outperforms other baselines.
- Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation147
The proposed semi-supervised graph neural network, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets and model the fraud patterns through risk propagation among transactions.
- Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection126
The result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves and demonstrates the effectiveness of the proposed method in both detecting suspicious transactions and mining fraud patterns.
- G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks125
G-Designer is introduced, an adaptive, efficient, and robust solution for multi-agent deployment, which dynamically designs task-aware, customized communication topologies.
- Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments106
A knowledge graph-based event embedding framework for quantitative investments that first extracts structured events from raw texts, and construct the knowledge graph with the mentioned entities and relations simultaneously, and leverages a joint model to merge theknowledge graph information into the objective function of anevent embedding learning model.
- TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting101
TimeFilter is proposed, a GNN-based framework for adaptive and fine-grained dependency modeling that refines the learned spatial-temporal dependencies by filtering out irrelevant correlations while preserving the most critical ones in a patch-specific manner.
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- Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation65
A high-order graph attention representation method (HGAR) to learn the embedding of guarantee networks and proves that the complexity is nearlinear with the number of edges, which could scale to large datasets.
- AgriKG: An Agricultural Knowledge Graph and Its Applications65
An agricultural knowledge graph, namely AgriKG, is proposed to automatically integrate the massive agricultural data from internet by applying the NLP and deep learning techniques, and can automatically recognize agricultural entities from unstructured text, and link them to form a knowledge graph.
- Image Cropping with Composition and Saliency Aware Aesthetic Score Map59
This paper proposes an interpretable image cropping model that can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by the model.
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- Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning47
An innovative framework that adapts Large Language Models for time-series representation learning with a distinctive element of the patch-wise decoding layer, which departs from previous methods reliant on sequence-level decoding.
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- Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs39
The proposed model can better estimate the default risks of enterprises compared to the state-of-the-art techniques and is integrated with the loan risk control system, which is deployed in the collaborated bank.
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- A Dynamic Default Prediction Framework for Networked-guarantee Loans35
A dynamic default prediction framework (DDPF) is proposed, which preserves temporal network structures and loan behavior sequences in an end-to-end model and design a gated recursive and attention mechanism to integrate both the loan behavior and network information.
- Contagious Chain Risk Rating for Networked-guarantee Loans34
A novel approach to rate the risk of contagion chains in the bank industry with the deep neural network is proposed and is significantly better than the state-of-the-art baselines on the dataset from a major financial institution in Asia.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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