Published on: April 2026
LOANINSIGHT: INTERPRETABLE MACHINE LEARNING FOR CREDIT APPROVAL
M. Jahnavi B. Shiva Ram S. Chakri R. Jashwanth
Dr.P. Ashok Kumar
Article Status
Available Documents
Abstract
Predicting who will get a loan is really important in today’s systems, and it is necessary to ensure that loan decisions are fair and based on data. To achieve this, a system was created that uses machine learning to determine whether a person is eligible for a loan in real time through an easy-to-use web interface. The system analyzes factors such as credit history, income, loan amount, loan duration, employment status, and other applicant details to decide whether a loan should be approved. It is built using Scikit-learn, and various techniques are applied to improve accuracy and performance. The model is saved so that it can make predictions quickly without retraining. Users can visit the website, enter their details, and get instant results along with a score that shows how confident the system is in its prediction. The system also keeps track of all predictions for future reference and analysis. Overall, this system provides a fast, efficient, and reliable way to make loan decisions, reduces manual effort, and shows how artificial intelligence can make financial processes easier, more accurate, and fair.
How to Cite this Paper
Jahnavi, M., Ram, B. S., Chakri, S. & Jashwanth, R. (2026). Loaninsight: Interpretable Machine Learning for Credit Approval. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.125
Jahnavi, M., et al.. "Loaninsight: Interpretable Machine Learning for Credit Approval." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.125.
Jahnavi, M.,B. Ram,S. Chakri, and R. Jashwanth. "Loaninsight: Interpretable Machine Learning for Credit Approval." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.125.
Search & Index
References
- 1. Powar, S. M. (2025).Loan Eligibility Prediction using Machine Learning. International Journal on Advanced Computer Theory and Engineering.DOI: 10.65521/ijacte.v14i1.215
- 2. Kumari, P. Senthil (2025).Loan Approval Prediction Using Machine Learning. International Journal of Advanced Research in Computer and Communication Engineering DOI: 10.17148/IJARCCE.2025.14721
- 3. Gogula, K., & Chattu, N. (2024).Loan Eligibility Prediction Using Machine Learning.
- International Journal of Soft Computing and Engineering (IJSCE).DOI: 10.35940/ijsce.C8144.14040924
- 4. Sinap, V. (2024).A Comparative Study of Loan Approval Prediction Using Machine Learning Methods.
- Gazi University Journal of Science, Part C: Design and Technology.DOI: 10.29109/gujsc.1455978
- 5. Mohammed, Y. S., et al. (2025).A Data Driven Model for Predicting Loan Approval Using Machine Learning Approaches. ERU Research Journal.DOI: 10.21608/erurj.2025.291674.1149
- 6. Haque, F. M. A., & Hassan, M. M. (2024).Bank Loan Prediction Using Machine Learning Techniques.
- arXiv / Research Publication.DOI: 10.48550/arXiv.2410.08886
- 7. Macwan, K. (2024).Improved Decision Support System for Personal Loan Eligibility Using Artificial Neural Networks. International Journal of Computer Trends and Technology (IJCTT).
- 8. Meenakshi, A., et al. (2025).Machine Learning Based Loan Eligibility Prediction and Automation.
- International Research Journal on Advanced Engineering Hub (IRJAEH).DOI: 10.47392/IRJAEH.2025.0058
- Kumar, P. Ashok, B. Vishnu Vardhan, and Pandi Chiranjeevi. "Investigating Context-Aware Sentiment Classification Using Machine Learning Algorithms." In XVIII International Conference on Data Science and Intelligent Analysis of Information, pp. 13-26. Cham: Springer Nature Switzerland, 2023.
- Sunkavalli, Jayaprakash, B. Madhav Rao, M. Trinath Basu, Harish Dutt Sharma, P. Ashok Kumar, and Ketan Anand. "Experimentation Analysis of VQC and QSVM on Sentence Classification in Quantum Paradigm." In 2024 International Conference on Computing, Sciences and Communications (ICCSC), pp. 1-5. IEEE, 2024.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 07 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.