A Survey and Taxonomy of Datasets for Advanced Bankruptcy Prediction

核心概念

While numerous studies focus on improving bankruptcy prediction models, the effectiveness of these models heavily relies on the quality and informativeness of the datasets used. This paper introduces a taxonomy of datasets for bankruptcy research, analyzes their characteristics, and proposes metrics to evaluate their quality and informativeness.

摘要

This is a research paper that surveys and analyzes datasets used for advanced bankruptcy prediction.

Bibliographic Information: Wang, X., Brorsson, M., & Kr¨aussl, Z. (2024). Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy. Expert system with applications. Preprint submitted to Expert system with applications arXiv:2411.01928v1 [cs.CE] 4 Nov 2024

Research Objective: This paper aims to address the lack of focus on dataset quality in bankruptcy prediction research by providing a taxonomy of commonly used datasets, analyzing their characteristics, and proposing metrics to evaluate their quality and informativeness.

Methodology: The authors conducted a comprehensive literature review of bankruptcy prediction research using Google Scholar, focusing on papers published between 2013 and 2023 that utilized machine learning or deep learning methods. They identified 47 relevant papers and manually extracted information about the datasets used, leading to the development of a taxonomy categorized into five types: accounting-based, market-based, macroeconomic, relational, and non-financial. The authors then proposed metrics to evaluate the quality and informativeness of these datasets based on factors like data balance, volume, integrity, noise, distribution, and redundancy.

Key Findings: The study found that accounting-based data remains the most commonly used data source for bankruptcy prediction, but there is a growing trend of using mixed datasets. The authors also highlighted the challenges of data imbalance, limited sample sizes, and the lack of publicly available datasets in the field.

Main Conclusions: The authors argue that the quality and informativeness of datasets are crucial for building effective bankruptcy prediction models. They emphasize the need for researchers to carefully consider the characteristics of different datasets and utilize appropriate metrics for evaluation. The proposed taxonomy and evaluation metrics provide a framework for researchers to select and assess datasets, ultimately contributing to more reliable and robust bankruptcy prediction models.

Significance: This research contributes to the field of bankruptcy prediction by shifting the focus from model-centric approaches to a greater emphasis on data quality and informativeness. The proposed taxonomy and evaluation metrics offer valuable tools for researchers to navigate the landscape of bankruptcy prediction datasets and make informed decisions about data selection and utilization.

Limitations and Future Research: The study acknowledges the limitations of relying on publicly available datasets, which are often limited in scope and availability. Future research could explore the potential of alternative data sources, such as social media data or news articles, for bankruptcy prediction. Additionally, further investigation into the development of standardized data quality metrics and benchmarks for bankruptcy prediction datasets would be beneficial.

統計資料

From the total of 47 reviewed studies, 40 papers used accounting-based data.
19 out of 40 papers instrumentalized accounting-based data as the only resource.
Only two studies relied purely on market-based data for modeling.
6 papers used relational data for bankruptcy prediction.
18 papers used non-financial data for bankruptcy prediction.

引述

"It is a well-known fact in data science that the quality of the data determines the upper boundary of the model performance."
"Real-world bankruptcy datasets often exhibit a significant imbalance between the number of bankrupt and non-bankrupt enterprises, with bankrupt enterprises typically being a minority."

從以下內容提煉的關鍵洞見

Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy

by Xinl... 於 arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.01928.pdf

深入探究

How can the proposed taxonomy and evaluation metrics be integrated into data sharing platforms or repositories to facilitate better dataset selection and comparison in bankruptcy prediction research?

Integrating the proposed taxonomy and evaluation metrics into data sharing platforms like UCI Machine Learning Repository, Kaggle Datasets, or specialized platforms for financial data can significantly enhance bankruptcy prediction research. Here's how:
1. Enhanced Dataset Metadata:

Taxonomy-Based Categorization: Implement the taxonomy (accounting-based, market-based, macroeconomic, relational, non-financial) as searchable tags or categories. This allows researchers to quickly filter and find datasets relevant to their specific research focus.
Standardized Quality Metrics: Include a dedicated section for quality metrics (Bankruptcy Rate, Sample Size, Number of Features, Missing Values, Noise Level) in a standardized format. This allows for easy comparison across datasets.
Informativeness Scores: Calculate and display informativeness scores (Information Value, Feature Importance, Chi-squared test results) for each feature within a dataset. This helps researchers understand the predictive power of individual variables.
2. Advanced Search and Filtering:

Taxonomy-Based Search: Allow users to search for datasets based on the taxonomy categories, such as "accounting-based AND macroeconomic."
Metric-Based Filtering: Enable filtering of datasets based on specific metric thresholds, such as "Bankruptcy Rate > 10%" or "Sample Size > 10,000."
Combined Search: Facilitate combined searches using both taxonomy and metrics, e.g., "market-based datasets with high Information Value features."
3. Dataset Comparison Tools:

Side-by-Side Comparison: Develop tools that allow users to compare multiple datasets based on their taxonomy, metrics, and feature informativeness scores.
Visualizations: Provide interactive visualizations, such as scatter plots or heatmaps, to help users explore the relationships between different metrics and dataset characteristics.
Benefits:

Improved Dataset Discoverability: Researchers can easily find suitable datasets, saving time and effort.
Informed Dataset Selection: The taxonomy and metrics provide a framework for making informed decisions about which datasets are most appropriate for a given research question.
Enhanced Research Transparency and Reproducibility: Standardized reporting of dataset characteristics and metrics promotes transparency and facilitates the reproducibility of research findings.

Could the over-reliance on accounting-based data for bankruptcy prediction be inadvertently perpetuating biases against certain types of companies or industries?

Yes, the heavy reliance on accounting-based data for bankruptcy prediction can inadvertently perpetuate biases, leading to unfair disadvantages for certain companies or industries. Here's why:

Industry-Specific Factors: Accounting practices and reporting standards can vary significantly across industries. Metrics that might signal risk in one industry might be standard practice in another. For example, high debt-to-equity ratios might be common in capital-intensive industries but raise red flags in others.
Company Size and Maturity: Startups and young companies often operate at a loss in their early years as they invest heavily in growth. Relying solely on traditional financial ratios might misinterpret this as a sign of financial distress, even if the company has strong growth potential.
Business Model Innovation: Companies with innovative business models, such as those in the sharing economy or subscription-based services, might have non-traditional revenue streams or cost structures that are not adequately captured by standard accounting metrics.
Geographic Location: Accounting standards and economic conditions differ across countries. A model trained on data from one country might not accurately predict bankruptcy risk for companies operating in different economic environments.
Consequences of Bias:

Limited Access to Capital: Biased models can lead to higher borrowing costs or even denial of loans for companies unfairly deemed "high-risk," hindering their growth and innovation.
Reinforcement of Existing Inequalities: If models are primarily trained on data from larger, established companies, they might systematically disadvantage smaller businesses or those from underrepresented industries.
Mitigating Bias:

Diverse Data Sources: Incorporate alternative data sources like market data, macroeconomic indicators, relational data, and non-financial data (management quality, industry trends) to provide a more holistic view of a company's financial health.
Contextualized Models: Develop industry-specific or size-specific models that account for the unique characteristics and risk factors of different business types.
Fairness-Aware Machine Learning: Employ fairness-aware machine learning techniques during model development to identify and mitigate potential biases in the data or algorithms.
Regular Model Review and Auditing: Continuously monitor and audit models for bias, particularly as new data becomes available or business environments change.

What are the ethical implications of using increasingly sophisticated data analysis techniques for bankruptcy prediction, and how can we ensure responsible and fair use of these methods?

The use of advanced data analysis for bankruptcy prediction, while offering potential benefits, raises significant ethical concerns that demand careful consideration and mitigation:
1. Privacy Violation:

Data Sensitivity: Bankruptcy prediction models often utilize highly sensitive financial and non-financial data, potentially including personal information about company employees, customers, or suppliers.
Data Security and Misuse: Unauthorized access, data breaches, or misuse of this information could have severe consequences for individuals and businesses.
2. Bias and Discrimination:

Algorithmic Bias: As discussed earlier, models trained on biased data can perpetuate and even amplify existing societal biases, leading to unfair or discriminatory outcomes.
Lack of Transparency: Sophisticated models, especially "black box" models like deep neural networks, can be difficult to interpret, making it challenging to identify and rectify bias.
3. Lack of Accountability:

Responsibility for Decisions: When AI systems are involved in credit decisions or bankruptcy predictions, it can be unclear who is ultimately responsible for potentially harmful outcomes.
Recourse and Appeal: Individuals or businesses denied loans or flagged as high-risk based on opaque models might lack clear avenues for recourse or appeal.
Ensuring Responsible and Fair Use:
1. Data Governance and Privacy:

Data Minimization: Collect and use only the data strictly necessary for the specific purpose of bankruptcy prediction.
Data Anonymization and De-identification: Implement robust techniques to protect individual privacy by removing or masking personally identifiable information.
Secure Data Storage and Access Control: Establish strict security protocols to prevent unauthorized access, data breaches, or misuse.
2. Fairness and Transparency:

Bias Detection and Mitigation: Employ fairness-aware machine learning techniques to identify and mitigate bias in data and algorithms.
Model Explainability: Utilize explainable AI (XAI) methods to make model predictions more transparent and understandable.
Human Oversight and Review: Maintain human oversight in the decision-making process, especially for high-stakes decisions like loan approvals.
3. Accountability and Recourse:

Clear Lines of Responsibility: Establish clear accountability frameworks to determine who is responsible for the development, deployment, and outcomes of AI systems.
Mechanisms for Appeal: Provide accessible and transparent mechanisms for individuals or businesses to challenge decisions made by AI systems.
4. Regulation and Ethical Guidelines:

Industry Standards and Best Practices: Develop and adhere to industry-specific standards and best practices for responsible AI in finance.
Government Regulation: Explore and implement appropriate regulations to govern the use of AI in financial decision-making, ensuring fairness, transparency, and accountability.
By proactively addressing these ethical implications, we can harness the power of advanced data analysis for bankruptcy prediction while fostering a more responsible, fair, and equitable financial system.