Paper: arXiv 2506.19789

Authors: Adrian Iulian Cristescu, Matteo Giordano

Abstract

Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosting and AdaBoost-to the predominantly used logistic regression, over a benchmark dataset from Scheule et al. (Credit Risk Analytics: The R Companion). Our findings underscore the strengths and weaknesses of each method, providing valuable insights into the most effective ML algorithms for PD prediction in the context of loan portfolios.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a standard empirical comparison of common machine learning models for credit default prediction using a well-known benchmark dataset. While clear and methodology sound in its application of SMOTE and standard evaluation metrics, it lacks theoretical mathematical depth and offers limited novel methodological contributions.

Research Flowchart

  flowchart TD
    A["Research Goal: Compare ML algorithms vs. Logistic Regression for Probability of Default (PD) prediction"] --> B["Data Input: Benchmark loan dataset (Scheule et al.)"]
    B --> C["Methodology: Train & evaluate predictive models"]
    C --> D["Computational Models: Logistic Regression, Decision Trees, Random Forests, XGBoost, Gradient Boosting, AdaBoost"]
    D --> E["Performance Evaluation & Comparative Analysis"]
    E --> F["Key Outcomes: Identified strengths/weaknesses of each model; highlighted superior ML performance over traditional methods for credit risk"]

Cited References (10)

  1. Machine Learning for Credit Risk Prediction: A Systematic Literature Review (2023) — link
  2. Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature (2023) — link
  3. Machine learning-driven credit risk: a systemic review (2022) — link
  4. Credit Risk Analytics: The R Companion (2017)
  5. An Introduction to Statistical Learning with Applications. R. G. James, D. Witten, T. Hastie, and R. Tibshirani (2013). Berlin: Springer. 440 pages, ISBN: 978‐1‐4614‐7138‐7. (2016) — link
  6. Applied Logistic Regression (2002) — link
  7. Pattern recognition and machine learning , volume 4 (2006)
  8. SMOTE: Synthetic Minority Over-sampling Technique (2002) — link
and 2 more
  1. Comparative analysis of statistical models for computing probability of default: Application of machine learning techniques in python
  2. Factors influence loan default–a credit risk analysis

0 of 10 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.