Academic Journal of Business & Management, 2024, 6(10); doi: 10.25236/AJBM.2024.061039.
Haowei Yang1, Zhan Cheng2, Zhaoyang Zhang3, Yuanshuai Luo4, Shuaishuai Huang5, Ao Xiang6
1University of Houston, Cullen College of Engineering, Industrial Engineering, Houston, USA
2University of California, Irvine, Donald Bren School of Information & Computer Science, Master of Computer Science, Irvine, USA
3University of California San Diego, Computational Science, San Diego, USA
4Southwest Jiaotong University, School of Computing and Artificial Intelligence, Computer Science and Technology, Chengdu, China
5University of Science and Technology of China, Department of Software, Software System Design, Hefei, China
6Northern Arizona University, Information Security and Assurance, Arizona, USA
As the complexity and dynamism of financial markets continue to grow, traditional financial risk prediction methods increasingly struggle to handle large datasets and intricate behavior patterns. This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction. First, the application and advantages of deep learning and big data algorithms in the financial field are analyzed. Then, a deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets. The experimental results show that this method significantly improves the accuracy of financial risk behavior prediction and provides valuable support for risk management in financial institutions. Challenges in the application of deep learning are also discussed, along with potential directions for future research.
Deep learning, big data algorithms, financial risk, behavior prediction, risk management
Haowei Yang, Zhan Cheng, Zhaoyang Zhang, Yuanshuai Luo, Shuaishuai Huang, Ao Xiang. Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms. Academic Journal of Business & Management (2024) Vol. 6, Issue 10: 273-280. https://doi.org/10.25236/AJBM.2024.061039.
[1] Singh, Vinay, et al. "How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda." International Journal of Information Management Data Insights 2.2 (2022): 100094.
[2] Li, Zhenglin, et al. "Stock market analysis and prediction using LSTM: A case study on technology stocks." Innovations in Applied Engineering and Technology (2023): 1-6.
[3] Mo, Yuhong, et al. "Large Language Model (LLM) AI Text Generation Detection based on Transformer Deep Learning Algorithm." International Journal of Engineering and Management Research 14.2 (2024): 154-159.
[4] Liu, Tianrui, et al. "Spam detection and classification based on distilbert deep learning algorithm." Applied Science and Engineering Journal for Advanced Research 3.3 (2024): 6-10.
[5] Zhang J, Wang X, Ren W, et al. RATT: AThought Structure for Coherent and Correct LLMReasoning[J]. arXiv preprint arXiv:2406.02746, 2024.
[6] Li, Shaojie, Yuhong Mo, and Zhenglin Li. "Automated pneumonia detection in chest x-ray images using deep learning model." Innovations in Applied Engineering and Technology (2022): 1-6.
[7] Mo, Yuhong, et al. "Password complexity prediction based on roberta algorithm." Applied Science and Engineering Journal for Advanced Research 3.3 (2024): 1-5.
[8] Song, Jintong, et al. "A comprehensive evaluation and comparison of enhanced learning methods." Academic Journal of Science and Technology 10.3 (2024): 167-171.
[9] Qi Z, Ma D, Xu J, et al. Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks[J]. arXiv preprint arXiv:2403.08499, 2024.
[10] Dai, Shuying, et al. "The cloud-based design of unmanned constant temperature food delivery trolley in the context of artificial intelligence." Journal of Computer Technology and Applied Mathematics 1.1 (2024): 6-12.
[11] He, Shuyao, et al. "Lidar and Monocular Sensor Fusion Depth Estimation." Applied Science and Engineering Journal for Advanced Research 3.3 (2024): 20-26.
[12] Liu, Jihang, et al. "Unraveling large language models: From evolution to ethical implications-introduction to large language models." World Scientific Research Journal 10.5 (2024): 97-102.
[13] Zhang J, Wang X, Jin Y, et al. Prototypical Reward Network for Data-Efficient RLHF[J]. arXiv preprint arXiv:2406.06606, 2024.
[14] Mo, Yuhong & Zhang, Yuchen & Li, Hanzhe & Wang, Han & Yan, Xu. (2024). Prediction of heart failure patients based on multiple machine learning algorithms. Applied and Computational Engineering. 75. 1-7. 10.54254/2755-2721/75/20240498.
[15] Tang X, Wang Z, Cai X, et al. Research on heterogeneous computation resource allocation based on data-driven method[J]. arXiv preprint arXiv:2408.05671, 2024.
[16] Mo, Yuhong, et al. "Make Scale Invariant Feature Transform “Fly” with CUDA." International Journal of Engineering and Management Research 14.3 (2024): 38-45.
[17] Xiang A, Huang B, Guo X, et al. A neural matrix decomposition recommender system model based on the multimodal large language model[J]. arXiv preprint arXiv:2407.08942, 2024.
[18] Mo K, Chu L, Zhang X, et al. DRAL: Deep Reinforcement Adaptive Learning for Multi-UAVs Navigation in Unknown Indoor Environment[J]. arXiv preprint arXiv:2409.03930, 2024.
[19] Yan H, Wang Z, Bo S, et al. Research on image generation optimization based deep learning[J]. 2024.
[20] Zhao Y, Hu B, Wang S. Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition[J]. arXiv preprint arXiv:2409.12376, 2024.
[21] Zhang W, Huang J, Wang R, et al. Integration of Mamba and Transformer--MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics[J]. arXiv preprint arXiv:2409.08530, 2024.
[22] Tan C, Wang C, Lin Z, et al. Editable Neural Radiance Fields Convert 2D to 3D Furniture Texture[J]. International Journal of Engineering and Management Research, 2024, 14(3): 62-65.
[23] Zhao Q, Hao Y, Li X. Stock Price Prediction Based on Hybrid CNN-LSTM Model[J]. 2024.
[24] Wang, Zixuan, et al. "Improved Unet model for brain tumor image segmentation based on ASPP-coordinate attention mechanism." arXiv preprint arXiv:2409.08588 (2024).
[25] Yukun, Song. "Deep Learning Applications in the Medical Image Recognition." American Journal of Computer Science and Technology 9.1 (2019): 22-26.
[26] Xiang A, Qi Z, Wang H, et al. A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product[J]. arXiv preprint arXiv:2403.08511, 2024.
[27] Song, Yukun, et al. "Going Blank Comfortably: Positioning Monocular Head-Worn Displays When They are Inactive." Proceedings of the 2023 ACM International Symposium on Wearable Computers. 2023.
[28] Zhang X, Zhang J, Mo F, et al. TIFG: Text-Informed Feature Generation with Large Language Models[J]. arXiv preprint arXiv:2406.11177, 2024.
[29] Yu H, Yu C, Wang Z, et al. Enhancing Healthcare through Large Language Models: A Study on Medical Question Answering[J]. arXiv preprint arXiv:2408.04138, 2024.
[30] Zheng H, Zhang Q, Gong Y, et al. Identification of prognostic biomarkers for stage iii non-small cell lung carcinoma in female nonsmokers using machine learning[J]. arXiv preprint arXiv:2408.16068, 2024.
[31] Song, Yukun, et al. "Looking From a Different Angle: Placing Head-Worn Displays Near the Nose." Proceedings of the Augmented Humans International Conference 2024. 2024.
[32] Shen X, Zhang Q, Zheng H, et al. Harnessing xgboost for robust biomarker selection of obsessive-compulsive disorder (ocd) from adolescent brain cognitive development (abcd) data[J]. ResearchGate, May, 2024.
[33] Wang L, Zhang S, Mammadov M, et al. Semi-supervised weighting for averaged one-dependence estimators[J]. Applied Intelligence, 2022: 1-17.
[34] Gong Y, Zhang Q, Zhen H, et al. Graphical Structural Learning of rs-fMRI data in Heavy Smokers[J]. arXiv preprint arXiv:2409.08395, 2024.
[35] Yang H, Zi Y, Qin H, et al. Advancing Emotional Analysis with Large Language Models[J]. Journal of Computer Science and Software Applications, 2024, 4(3): 8-15.
[36] Zhang Q, Qi W, Zheng H, et al. CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset[J]. arXiv preprint arXiv:2406.13113, 2024.
[37] Ma D, Wang M, Xiang A, et al. Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment[J]. arXiv preprint arXiv:2404.12634, 2024.
[38] Zhang X, Zhang J, Rekabdar B, et al. Dynamic and Adaptive Feature Generation with LLM[J]. arXiv preprint arXiv:2406.03505, 2024.
[39] Zheng H, Wang B, Xiao M, et al. Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function[J]. arXiv preprint arXiv:2408.11839, 2024.
[40] Zhu W, Hu T. Twitter Sentiment analysis of covid vaccines[C]//2021 5th International Conference on Artificial Intelligence and Virtual Reality (AIVR). 2021: 118-122.
[41] Hu T, Zhu W, Yan Y. Artificial intelligence aspect of transportation analysis using large scale systems[C]//Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference. 2023: 54-59.
[42] Zhu W. Optimizing distributed networking with big data scheduling and cloud computing[C]//International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022). SPIE, 2022, 12303: 23-28.
[43] Yan Y. Influencing Factors of Housing Price in New York-analysis: Based on Excel Multi-regression Model[J]. 2022.
[44] Wang L M, Zhang X H, Li K, et al. Semi-supervised learning for k-dependence Bayesian classifiers[J]. Applied Intelligence, 2022: 1-19.
[45] Zhang X, Wang Z, Jiang L, et al. TFWT: Tabular Feature Weighting with Transformer[J]. arXiv preprint arXiv:2405.08403, 2024.
[46] Hu Y, Yang Z, Cao H, et al. Multi-modal steganography based on semantic relevancy[C]//International Workshop on Digital Watermarking. Cham: Springer International Publishing, 2020: 3-14.
[47] Hu Y, Cao H, Yang Z, et al. Improving text-image matching with adversarial learning and circle loss for multi-modal steganography[C]//International Workshop on Digital Watermarking. Cham: Springer International Publishing, 2020: 41-52.
[48] Feng J, Li Y, Yang Z, et al. User identity linkage via co-attentive neural network from heterogeneous mobility data[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(2): 954-968.