Paper: arXiv 2401.09778
Abstract
This paper introduces a credit risk rating model for credit risk assessment in quantitative finance, aiming to categorize borrowers based on their behavioral data. The model is trained on data from Experian, a widely recognized credit bureau, to effectively identify instances of loan defaults among bank customers. Employing state-of-the-art statistical and machine learning techniques ensures the model’s predictive accuracy. Furthermore, we assess the model’s transferability by testing it on behavioral data from the Bank of Italy, demonstrating its potential applicability across diverse datasets during prediction. This study highlights the benefits of incorporating external behavioral data to improve credit risk assessment in financial institutions.
Complexity vs Empirical Score
- Math Complexity: 4.5/10
- Empirical Rigor: 8.0/10
- Quadrant: Street Traders — practical and empirical, lighter on theory
Why this score: The paper employs standard statistical and machine learning methods (Logistic Regression, AutoML, Genetic Algorithms, SHAP) without heavy theoretical derivations, keeping math complexity moderate. It demonstrates high empirical rigor through rigorous data filtering, feature engineering, out-of-sample/out-of-time validation, probability calibration, and backtesting on an external central credit register dataset.
Research Flowchart
flowchart TD A["Research Goal<br>Develop a credit risk model<br>for behavioral data"] --> B["Data Source: Experian<br>(Private Data)"] A --> C["Data Source: Bank of Italy<br>(Central Data)"] B --> D["Model Training & Validation<br>Using ML Techniques"] C --> E["Transferability Test<br>Unseen Data Evaluation"] D --> F["Key Finding: High Predictive<br>Accuracy on Experian"] E --> G["Key Finding: Successful<br>Transferability Across Datasets"] F --> H["Outcome:<br>Enhanced Credit Risk<br>Assessment Framework"] G --> H
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0 of 54 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.