DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data

ArXiv ID: 2308.03704 “View on arXiv”

Authors: Unknown

Abstract

Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods. Different from images or languages, real-world financial data are high-dimensional, sparse, noisy and extremely imbalanced, which makes deep neural network models particularly challenging to train and fragile in practice. In this work, we propose DeRisk, an effective deep learning risk prediction framework for credit risk prediction on real-world financial data. DeRisk is the first deep risk prediction model that outperforms statistical learning approaches deployed in our company’s production system. We also perform extensive ablation studies on our method to present the most critical factors for the empirical success of DeRisk.

Keywords: Deep Learning, Credit Risk Prediction, Imbalanced Data Learning, Gradient Boosting, Ablation Study, Fixed Income (Credit)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper is heavily implementation-focused, describing a multi-stage pipeline with data preprocessing, separate training, and fine-tuning on real-world financial datasets, with extensive ablation studies and production comparison, showing high empirical rigor. While it references deep learning architectures like Transformers, the mathematical exposition is relatively light, focusing more on the framework design and practical techniques rather than dense theoretical derivations.

  flowchart TD
    A["Research Goal<br/>Develop Deep Learning Framework<br/>to Outperform Statistical Models<br/>for Credit Risk Prediction"] --> B["Input Data<br/>Real-World Financial Data<br/>(High-dimensional, Sparse, Noisy, Imbalanced)"]
    B --> C["Key Methodology<br/>DeRisk Framework<br/>Deep Neural Network Model"]
    C --> D["Computational Process<br/>Training on Financial Data<br/>Handling Imbalance & Sparsity"]
    D --> E["Key Outcomes<br/>DeRisk Outperforms<br/>GB/RF Models in Production<br/>Critical Factors Identified via Ablation Study"]