Version 1
: Received: 4 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (17:39:31 CEST)
How to cite:
Sotiropoulos, D.; Koronakos, G.; Solanakis, S. V. Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming. Preprints2024, 2024100527. https://doi.org/10.20944/preprints202410.0527.v1
Sotiropoulos, D.; Koronakos, G.; Solanakis, S. V. Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming. Preprints 2024, 2024100527. https://doi.org/10.20944/preprints202410.0527.v1
Sotiropoulos, D.; Koronakos, G.; Solanakis, S. V. Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming. Preprints2024, 2024100527. https://doi.org/10.20944/preprints202410.0527.v1
APA Style
Sotiropoulos, D., Koronakos, G., & Solanakis, S. V. (2024). Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming. Preprints. https://doi.org/10.20944/preprints202410.0527.v1
Chicago/Turabian Style
Sotiropoulos, D., Gregory Koronakos and Spyridon V. Solanakis. 2024 "Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming" Preprints. https://doi.org/10.20944/preprints202410.0527.v1
Abstract
Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited insights available on both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score's calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in Genetic Programming (GP). Here, Occam's razor principle guides evolutionary bias towards simpler, more interpretable models. To ascertain our method's efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multi-Layer Perceptrons (MLPs), Regression Trees and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers comparable accuracy with these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
