Enhancement of Digital Credit Scoring in P2P Lending using a Robust Hybrid Voting–Stacking Ensemble Framework

Authors

  • Mohamed Galal Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt Corresponding Author
  • Sherine Rady Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt Author
  • Mostafa Aref Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt Author

DOI:

https://doi.org/10.47654/v30y2026i3p145-183

Keywords:

Ensemble modeling, Machine learning, P2P lending, Credit Scoring, Stacking, Fintech

Abstract

Purpose - This study evaluates a hybrid ensemble framework designed to enhance credit-risk prediction accuracy in peer-to-peer (P2P) digital lending platforms and to overcome the limitations of existing models.

Design/methodology/approach – The proposed Hybrid Optimized Ensemble Learning (HOEL) framework integrates seven base classifiers through a three-level optimization process that involves K-fold cross-validation, hyperparameter tuning, an intermediate weighted-voting ensemble layer, and a final stacking meta-learner.

Findings - Empirical results demonstrate that the HOEL framework outperforms individual classifiers, achieving an ROC-AUC of 97.62% and an accuracy of 96.15%. The ensemble’s layered design improves predictive stability and interpretability, confirming its robustness across small and high-dimensional datasets.

Research limitations - Although computationally intensive, the framework’s performance can be further optimized using cloud-based or parallel processing. Future studies could incorporate additional behavioral features to improve model generalization.

Practical implications - The framework can be integrated into a trusted P2P digital lending platform to improve loan-approval decisions for customers with limited credit history, thereby reducing lending risks.

Originality/value - HOEL's novelty resides in its three-level optimization architecture, which embeds an intermediate weighted-voting layer that stabilizes ensemble predictions before they are passed to a stacking meta-learner. This combination has not previously been applied to imbalanced P2P credit risk scoring and delivers measurable gains in both ROC–AUC and accuracy over conventional ensemble approaches. The voting layer reduces hyperparameter sensitivity and strengthens stacking stability, making the framework particularly effective for small and high-dimensional financial datasets. This study thereby advances the Decision Sciences literature on quantitative risk modeling by providing a replicable, data-driven credit-assessment framework that supports more informed financial decision-making under uncertainty.

Author Biographies

  • Sherine Rady, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Sherine Rady is a Professor at the Faculty of Computer and Information Sciences of Ain Shams University in Cairo, Egypt. She holds a B.Sc. in Electrical Engineering (Computer and Systems), Ain Shams University. She got her M.Sc. in Computer and Information Sciences from Ain Shams University and her Ph.D. from University of Mannheim in Germany. – Dr. Sherine Rady is a DAAD and JICA Alumni and her research interests are Artificial Intelligence, Data Mining, Computer Vision, Robotics, and Data Science.

    H-index: 12

    https://scholar.google.com/citations?user=S-RFMUYAAAAJ&hl=en

     

     

  • Mostafa Aref, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Professor of Computer Science, Ain Shams University


    Ph.D. of Engineering Science in System Theory and Engineering, June 1988, University of Toledo, Toledo, Ohio. The
    dissertation title "Evaluation of Heuristic Approach for Program Partitioning and Allocation Using Expert
    System."
    M.Sc. of Computer Science, October 1983, University of Saskatchewan, Saskatoon, Sask. Canada. The thesis title is
    "Automated Concept Discovery in Data Structure Applications".
    B.Sc. of Electrical Engineering - Computer and Automatic Control section, in June 1979 with general grade Distinction
    and Honor degree, Electrical Engineering Dept., Ain Shams University, Cairo, EGYPT. 

      All Since 2019 Citations 1020 628 h-index 15 11 i10-index 25 14

    https://www.researchgate.net/profile/Mostafa-Aref-2

     

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Published

2026-08-04

Issue

Vol. 30 No. 3 (2026): Advances in Decision Sciences

Section

Articles

License

Copyright (c) 2026 Mohamed Galal (Corresponding Author); Sherine Rady, Mostafa Aref (Author)

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Galal, M., Rady, S., & Aref, M. (2026). Enhancement of Digital Credit Scoring in P2P Lending using a Robust Hybrid Voting–Stacking Ensemble Framework. Advances in Decision Sciences, 30(3), 145-183. https://doi.org/10.47654/v30y2026i3p145-183