Financial Distress and Bankruptcy Prediction
Open access
Peer reviewed
Scollr summary
What this paper is about
The research results indicate that DNN models still exhibit significant limitations in credit scoring applications, and further model improvements or hybrid integration with other models are therefore required to enhance their practical applicability in real-world scenarios.
Full abstract
Read the full abstract
Financial technology is playing an increasingly vital role in loan decision-making, and financial institutions are increasingly relying on machine learning techniques to support credit decisions. The purpose of this review is to provide a critical overview of analysis comparing the practical applicability of deep neural network (DNN) and logistic regression (LR) models within the credit scoring domain. This paper systematically collects existing studies on the application of DNN and LR models in credit scoring. The research objects include DNN and LR models, as well as their improved variants developed on the original model frameworks. On this basis, it integrates theoretical research findings with comprehensive analyses to investigate and evaluate the practicality of DNN models. The research results indicate that DNN models still exhibit significant limitations in credit scoring applications. Further model improvements or hybrid integration with other models are therefore required to enhance their practical applicability in real-world scenarios.
Direct answer
What can I do from this paper page?
Use this page to scan "An Analysis of the Practicality of DNN Models versus LR Models in Credit Scoring" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Financial Distress and Bankruptcy Prediction research, save the paper, or map adjacent work.
Research areas
Follow related topics
Citation
BibTeX
@article{Fei2026Analysis,
title = {An Analysis of the Practicality of DNN Models versus LR Models in Credit Scoring},
author = {Shenchen Fei},
journal = {Applied and Computational Engineering},
year = {2026},
doi = {10.54254/2755-2721/2026.ld35067},
url = {https://doi.org/10.54254/2755-2721/2026.ld35067}
}FAQ
Using this paper in a discovery workflow
How do I find related work for this paper?
Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.
How can I keep up with new Financial Distress and Bankruptcy Prediction research papers?
Follow Financial Distress and Bankruptcy Prediction research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.
Can I cite this paper from this page?
This page includes a static BibTeX block for An Analysis of the Practicality of DNN Models versus LR Models in Credit Scoring. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.
Follow this research in Scollr
Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.
Get the app