Contrastive Unsupervised Graph Neural Network in Financial Industry

Authors

  • Imran Babayaro Financial System Research Centre

DOI:

https://doi.org/10.70393/6a6574626d.323437

ARK:

https://n2t.net/ark:/40704/JETBM.v1n6a04

Disciplines:

Finance

Subjects:

Risk Management

References:

51

Keywords:

Graph Neural Networks, Heterophily in Graphs, Unsupervised Learning, Financial Networks, Risk Propagation, Fraud Detection

Abstract

This paper explores the application of the Contrastive Unsupervised Graph Neural Network (CuGNN) framework in financial domains, leveraging its heterophily-based adaptive convolution to address critical tasks like fraud detection, risk propagation, and portfolio optimization. CuGNN's ability to identify and utilize heterophilic patterns in financial transaction graphs enables robust representation learning even in unsupervised settings, where labeled data is scarce. Specifically, we adapt CuGNN to model risk spread across trading and investment networks by dynamically capturing high-frequency and low-frequency signals through its adaptive convolution mechanism. This approach allows us to differentiate between correlated and inverse-correlated asset behaviors, providing deeper insights into systemic risks and diversification strategies. Furthermore, CuGNN’s feature-distribution embedding and latent-space contrastive learning strategies are utilized to detect anomalous interactions in high-frequency trading networks and to identify heterophilic relationships in credit scoring and supply chain finance. By applying the CuGNN framework across diverse financial datasets, this study demonstrates its potential to uncover hidden structures and optimize decision-making in critical financial applications, addressing the growing need for explainable and adaptive graph-based methods in the sector.

Author Biography

Imran Babayaro, Financial System Research Centre

Financial System Research Centre, Canada.

References

T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," arXiv preprint arXiv:1609.02907, 2016.

P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio and Y. Bengio, "GRAPH ATTENTION NETWORKS," stat, vol. 1050, p. 4, 2018.

M. Chen, Z. Wei, Z. Huang, B. Ding and Y. Li, "Simple and deep graph convolutional networks," in International conference on machine learning, 2020.

Z. Li, B. Wang and Y. Chen, "Incorporating economic indicators and market sentiment effect into US Treasury bond yield prediction with machine learning," Journal of Infrastructure, Policy and Development, vol. 8, p. 7671, 2024.

S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg and A. Galstyan, "Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing," in international conference on machine learning, 2019.

J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu and D. Koutra, "Beyond homophily in graph neural networks: Current limitations and effective designs," Advances in neural information processing systems, vol. 33, p. 7793–7804, 2020.

D. Bo, X. Wang, C. Shi and H. Shen, "Beyond low-frequency information in graph convolutional networks," in Proceedings of the AAAI conference on artificial intelligence, 2021.

E. Chien, J. Peng, P. Li and O. Milenkovic, "Adaptive universal generalized pagerank graph neural network," arXiv preprint arXiv:2006.07988, 2020.

P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio and R. D. Hjelm, "Deep graph infomax," arXiv preprint arXiv:1809.10341, 2018.

B. Wang, Y. Chen and Z. Li, "A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping," Decision Analytics Journal, p. 100522, 2024.

D. Cheng, Y. Zou, S. Xiang and C. Jiang, "Graph Neural Networks for Financial Fraud Detection: A Review," arXiv preprint arXiv:2411.05815, 2024.

K. Li, J. Chen, D. Yu, T. Dajun, X. Qiu, L. Jieting, S. Baiwei, Z. Shengyuan, Z. Wan, R. Ji and others, "Deep reinforcement learning-based obstacle avoidance for robot movement in warehouse environments," arXiv preprint arXiv:2409.14972, 2024.

K. Li, L. Liu, J. Chen, D. Yu, X. Zhou, M. Li, C. Wang and Z. Li, "Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout," arXiv preprint arXiv:2411.06128, 2024.

K. Li, J. Wang, X. Wu, X. Peng, R. Chang, X. Deng, Y. Kang, Y. Yang, F. Ni and B. Hong, "Optimizing automated picking systems in warehouse robots using machine learning," arXiv preprint arXiv:2408.16633, 2024.

S. Feng, R. Song, S. Yang and D. Shi, "U-net Remote Sensing Image Segmentation Algorithm Based on Attention Mechanism Optimization," in 2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT), 2024.

S. Feng, J. Wang, Z. Li, S. Wang, Z. Cheng, H. Yu and J. Zhong, "Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications," Biomimetics, vol. 9, 2024.

D. Liu and M. Jiang, "Distance Recomputator and Topology Reconstructor for Graph Neural Networks," arXiv preprint arXiv:2406.17281, 2024.

D. Liu, "Contemporary Model Compression on Large Language Models Inference," arXiv preprint arXiv:2409.01990, 2024.

D. Liu, "MT2ST: Adaptive Multi-Task to Single-Task Learning," arXiv preprint arXiv:2406.18038, 2024.

D. Liu, M. Jiang and K. Pister, "LLMEasyQuant – An Easy to Use Toolkit for LLM Quantization," arXiv preprint arXiv:2406.19657, 2024.

D. Luo, "Enhancing Smart Grid Efficiency through Multi-Agent Systems: A Machine Learning Approach for Optimal Decision Making," Preprints preprints:202411.0687.v1, 2024.

D. Luo, "Decentralized Energy Markets: Designing Incentive Mechanisms for Small-Scale Renewable Energy Producers," Preprints preprints::202411.0696.v1, 2024.

D. Luo, "Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes," arXiv preprint arXiv:2410.17696, 2024.

D. Luo, "Quantitative Risk Measurement in Power System Risk Management Methods and Applications," Preprints preprints:202411.1636.v1, 2024.

Y. Weng and J. Wu, "Big data and machine learning in defence," International Journal of Computer Science and Information Technology, vol. 16, 2024.

Y. Weng and J. Wu, "Fortifying the global data fortress: a multidimensional examination of cyber security indexes and data protection measures across 193 nations," International Journal of Frontiers in Engineering Technology, vol. 6, 2024.

Y. Weng and J. Wu, "Leveraging Artificial Intelligence to Enhance Data Security and Combat Cyber Attacks," Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, vol. 5, p. 392–399, 2024.

Y. Weng, J. Wu, T. Kelly and W. Johnson, "Comprehensive Overview of Artificial Intelligence Applications in Modern Industries," arXiv preprint arXiv:2409.13059, 2024.

X. Li, Y. Ma, Y. Huang, X. Wang, Y. Lin and C. Zhang, "Integrated Optimization of Large Language Models: Synergizing Data Utilization and Compression Techniques," Preprints preprints:202409.0662, 2024.

L. He, X. Wang, Y. Lin, X. Li, Y. Ma and Z. Li, "BOANN: Bayesian-Optimized Attentive Neural Network for Classification," Preprints preprints:202409.2367, 2024.

M. Jia, A. Liu and T. Narahara, "The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning," Sustainability, vol. 16, p. 3928, 2024.

X. Zeng, Y. Gao, F. Song and A. Liu, "Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insights," arXiv preprint arXiv:2404.04281, 2024.

H.-C. Dan, Z. Huang, B. Lu and M. Li, "Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework," Construction and Building Materials, vol. 453, p. 139056, 2024.

H.-C. Dan, P. Yan, J. Tan, Y. Zhou and B. Lu, "Multiple distresses detection for Asphalt Pavement using improved you Only Look Once Algorithm based on convolutional neural network," International Journal of Pavement Engineering, vol. 25, p. 2308169, 2024.

Z. Wang, Y. Chen, F. Wang and Q. Bao, "Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism," arXiv preprint arXiv:2409.08588, 2024.

Z. Wu, "Mpgaan: Effective and efficient heterogeneous information network classification," Journal of Computer Science and Technology Studies, vol. 6, p. 08–16, 2024.

X. Li, H. Cao, Z. Zhang, J. Hu, Y. Jin and Z. Zhao, "Artistic Neural Style Transfer Algorithms with Activation Smoothing," arXiv preprint arXiv:2411.08014, 2024.

X. Li, X. Wang, Z. Qi, H. Cao, Z. Zhang and A. Xiang, "DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network," Academic Journal of Computing & Information Science, vol. 7, p. 31–40, 2024.

Y. Wang, J. Zhao and Y. Lawryshyn, "GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Process," in Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for Scenario Planning, Jeju, 2024.

J. Zhao, Y. Ding, C. Jia, Y. Wang and Z. Qian, "Gender Bias in Large Language Models across Multiple Languages," arXiv preprint arXiv:2403.00277, 2024.

J. Zhao, Z. Qian, L. Cao, Y. Wang and Y. Ding, "Bias and Toxicity in Role-Play Reasoning," arXiv preprint arXiv:2409.13979, 2024.

Z. Ke and Y. Yin, "Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms," arXiv preprint arXiv:2412.06193, 2024.

Z. Ke, J. Xu, Z. Zhang, Y. Cheng and W. Wu, "A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm," arXiv preprint arXiv:2412.07223, 2024.

Q. Yu, Z. Xu and Z. Ke, "Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems," arXiv preprint arXiv:2412.07027, 2024.

Y. Hong, "Study on the Maximum Level of Disposable Plastic Product Waste," Sustainability, vol. 15, p. 9360, 2023.

Y. Jin, G. Fu, L. Qian, H. Liu and H. Wang, "Representation and Extraction of Diesel Engine Maintenance Knowledge Graph with Bidirectional Relations Based on BERT and the Bi-LSTM-CRF Model," in 2021 IEEE International Conference on e-Business Engineering (ICEBE), 2021.

T. Xie, S. Tao, Q. Li, H. Wang and Y. Jin, "A lattice LSTM-based framework for knowledge graph construction from power plants maintenance reports," Service Oriented Computing and Applications, vol. 16, p. 167–177, 2022.

F. Shen and J. Tang, "IMAGPose: A Unified Conditional Framework for Pose-Guided Person Generation," in The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.

F. Shen, X. Jiang, X. He, H. Ye, C. Wang, X. Du, Z. Li and J. Tang, "Imagdressing-v1: Customizable virtual dressing," arXiv preprint arXiv:2407.12705, 2024.

F. Shen, Y. Xie, J. Zhu, X. Zhu and H. Zeng, "Git: Graph interactive transformer for vehicle re-identification," IEEE Transactions on Image Processing, vol. 32, p. 1039–1051, 2023.

F. Shen, H. Ye, S. Liu, J. Zhang, C. Wang, X. Han and W. Yang, "Boosting consistency in story visualization with rich-contextual conditional diffusion models," arXiv preprint arXiv:2407.02482, 2024.

Downloads

Published

2024-12-16

How to Cite

Babayaro, I. (2024). Contrastive Unsupervised Graph Neural Network in Financial Industry. Journal of Economic Theory and Business Management, 1(6), 25–32. https://doi.org/10.70393/6a6574626d.323437

Issue

Section

Articles

ARK