Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

FinLangNet: A Novel Deep Learning Framework for Credit Risk Prediction Using Linguistic Analogy in Financial Data (2404.13004v2)

Published 19 Apr 2024 in cs.CE and cs.AI

Abstract: Recent industrial applications in risk prediction still heavily rely on extensively manually-tuned, statistical learning methods. Real-world financial data, characterized by its high dimensionality, sparsity, high noise levels, and significant imbalance, poses unique challenges for the effective application of deep neural network models. In this work, we introduce a novel deep learning risk prediction framework, FinLangNet, which conceptualizes credit loan trajectories in a structure that mirrors linguistic constructs. This framework is tailored for credit risk prediction using real-world financial data, drawing on structural similarities to language by adapting natural language processing techniques. It particularly emphasizes analyzing the development and forecastability of mid-term credit histories through multi-head and sequences of detailed financial events. Our research demonstrates that FinLangNet surpasses traditional statistical methods in predicting credit risk and that its integration with these methods enhances credit overdue prediction models, achieving a significant improvement of over 4.24\% in the Kolmogorov-Smirnov metric.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. SFE: A simple, fast and efficient feature selection algorithm for high-dimensional data. IEEE Transactions on Evolutionary Computation (2023).
  2. Wide and deep learning for peer-to-peer lending. Expert Systems with Applications 134 (2019), 209–224.
  3. Credit risk evaluation: a comprehensive study. Multimedia Tools and Applications 82, 12 (2023), 18217–18267.
  4. Deep neural networks and tabular data: A survey. IEEE Transactions on Neural Networks and Learning Systems (2022).
  5. Contagious chain risk rating for networked-guarantee loans. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2715–2723.
  6. On supervised class-imbalanced learning: An updated perspective and some key challenges. IEEE Transactions on Artificial Intelligence 3, 6 (2022), 973–993.
  7. Investigating the role of Fintech in the banking industry: what do we know? European Journal of Innovation Management 26, 5 (2023), 1365–1393.
  8. Fang Fang and Yuanyuan Chen. 2019. A new approach for credit scoring by directly maximizing the Kolmogorov–Smirnov statistic. Computational Statistics & Data Analysis 133 (2019), 180–194.
  9. SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research 61 (2018), 863–905.
  10. Standardizing fairness-evaluation procedures: interdisciplinary insights on machine learning algorithms in creditworthiness assessments for small personal loans. AI and Ethics (2023), 1–17.
  11. Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems 34 (2021), 18932–18943.
  12. Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023).
  13. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247 (2017).
  14. Improving credit risk prediction in online peer-to-peer (P2P) lending using feature selection with deep learning. In 2019 21st International Conference on Advanced Communication Technology (ICACT). IEEE, 511–515.
  15. A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications 98 (2018), 105–117.
  16. A filter feature selection for high-dimensional data. Journal of Algorithms & Computational Technology 17 (2023), 17483026231184171.
  17. Wonkeun Jo and Dongil Kim. 2022. OBGAN: Minority oversampling near borderline with generative adversarial networks. Expert Systems with Applications 197 (2022), 116694.
  18. Aleum Kim and Sung-Bae Cho. 2019. An ensemble semi-supervised learning method for predicting defaults in social lending. Engineering applications of Artificial intelligence 81 (2019), 193–199.
  19. Predicting mortgage default using convolutional neural networks. Expert Systems with Applications 102 (2018), 207–217.
  20. Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China. World Wide Web 23 (2020), 23–45.
  21. Credit risk prediction based on loan profit: Evidence from Chinese SMEs. Research in International Business and Finance 67 (2024), 102155.
  22. DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data. arXiv preprint arXiv:2308.03704 (2023).
  23. itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625 (2023).
  24. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electronic Commerce Research and Applications 31 (2018), 24–39.
  25. Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks. Annals of Operations Research 330, 1 (2023), 609–637.
  26. M Senthil Murugan et al. 2023. Large-scale data-driven financial risk management & analysis using machine learning strategies. Measurement: Sensors 27 (2023), 100756.
  27. Forecasting groundwater quality using automatic exponential smoothing model (AESM) in Xianyang City, China. Human and Ecological Risk Assessment: An International Journal 29, 2 (2023), 347–368.
  28. Sequential multi-dimensional self-supervised learning for clinical time series. In International Conference on Machine Learning. PMLR, 28531–28548.
  29. The role of fintech on bank risk-taking: Mediating role of bank’s operating efficiency. Human Behavior and Emerging Technologies 2023 (2023).
  30. Using sequences of life-events to predict human lives. Nature Computational Science (2023), 1–14.
  31. Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme. Information Sciences 629 (2023), 599–617.
  32. A deep learning approach to competing risks representation in peer-to-peer lending. IEEE transactions on neural networks and learning systems 30, 5 (2018), 1565–1574.
  33. Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH). IEEE, 1–5.
  34. Attention is all you need. Advances in neural information processing systems 30 (2017).
  35. Temporal-aware graph neural network for credit risk prediction. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). SIAM, 702–710.
  36. Fintech inputs, non-performing loans risk reduction and bank performance improvement. International Review of Financial Analysis 90 (2023), 102849.
  37. Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques. Annals of Operations Research 331, 1 (2023), 1–33.
  38. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert systems with applications 78 (2017), 225–241.
  39. Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports 11, 1 (2021), 18759.
  40. Predict and Optimize Financial Services Risk Using AI-driven Technology. Academic Journal of Science and Technology 10, 1 (2024), 299–304.
  41. A novel method for credit scoring based on cost-sensitive neural network ensemble. IEEE Access 9 (2021), 78521–78537.
  42. A review of recurrent neural networks: LSTM cells and network architectures. Neural computation 31, 7 (2019), 1235–1270.
  43. An attention-based Logistic-CNN-BiLSTM hybrid neural network for credit risk prediction of listed real estate enterprises. Expert Systems 41, 2 (2024), e13299.
  44. Fintech, macroprudential policies and bank risk: Evidence from China. International Review of Financial Analysis 87 (2023), 102648.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Yu Lei (57 papers)
  2. Zixuan Wang (83 papers)
  3. Chu Liu (3 papers)
  4. Tongyao Wang (4 papers)
  5. Dongyang Lee (1 paper)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - leiyu0210/FinLangNet (62 stars)
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets