Bankruptcy analysis using images and convolutional neural networks (CNN)

ArXiv ID: 2502.15726 “View on arXiv”

Authors: Unknown

Abstract

The marketing departments of financial institutions strive to craft products and services that cater to the diverse needs of businesses of all sizes. However, it is evident upon analysis that larger corporations often receive a more substantial portion of available funds. This disparity arises from the relative ease of assessing the risk of default and bankruptcy in these more prominent companies. Historically, risk analysis studies have focused on data from publicly traded or stock exchange-listed companies, leaving a gap in knowledge about small and medium-sized enterprises (SMEs). Addressing this gap, this study introduces a method for evaluating SMEs by generating images for processing via a convolutional neural network (CNN). To this end, more than 10,000 images, one for each company in the sample, were created to identify scenarios in which the CNN can operate with higher assertiveness and reduced training error probability. The findings demonstrate a significant predictive capacity, achieving 97.8% accuracy, when a substantial number of images are utilized. Moreover, the image creation method paves the way for potential applications of this technique in various sectors and for different analytical purposes.

Keywords: Convolutional Neural Network (CNN), Small and Medium Enterprises (SMEs), Credit Risk Assessment, Bankruptcy Prediction, Image Generation, Corporate Credit

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper applies a standard CNN architecture (low-to-moderate math complexity) but uses a large dataset (>10,000 images) and reports high accuracy (97.8%), indicating significant data processing and implementation focus. However, it lacks detailed backtesting or live trading results, placing it in the empirical rigor range.

  flowchart TD
    A["Research Goal:<br>Assess SME Bankruptcy<br>Risk via Images"] --> B["Data Input:<br>10,000+ Images<br>Generated from SME Data"]
    B --> C["Methodology:<br>CNN Model Architecture"]
    C --> D["Computational Process:<br>Training & Learning"]
    D --> E["Key Outcome:<br>97.8% Accuracy<br>High Predictive Capacity"]
    E --> F["Impact:<br>Improved Credit Risk<br>Assessment for SMEs"]