Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction
Problem
Financial institutions need to accurately predict whether a borrower will default on a loan in order to reduce losses and maintain profitability.
Why it matters
Better default prediction directly reduces financial losses and supports lending stability for institutions.
What the AI did
Multiple machine learning models (random forest, decision tree, SVM, XGBoost, ADABoost, MLP) were compared on LendingClub loan data, with an XGBoost framework used to evaluate techniques and handle class imbalance.
Results
not stated in source
Impact
The study compared ML techniques for default prediction, but the abstract states no measured outcome.
Record
| Venue | Mathematics |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Abisola Akinjole, Olamilekan Shobayo, Jumoke Popoola, Obinna Okoyeigbo, Bayode Ogunleye |
| Citations | 31 (OpenAlex, at import time) |
| References | 75 |
| Model or method | XGBoost, random forest, decision tree, SVM, ADABoost, multi-layer perceptron |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/math12213423 |
| Open access | gold |
| Keywords | machine learning, XGBoost, loan default, LendingClub, class imbalance, ensemble |
Importance rating
significant — thousands to millions affected
Abstract states accurate default prediction reduces financial losses and maintains profitability and stability of lending institutions; no scale quantified.
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