Non-Payment Risk Automation Using Machine Learning and its Deployment on Android Application.

Registration ID: IJNRD_195713

Published ID: IJNRD2305457

DOI: Click Here to Get

Sourav Chitnis , Shashank Jagtap , Parth Apamarjane , Akshay Patil , Prof. Naved Raza Q. Ali

Loan Repayment, Machine Learning, Random Forest algorithm, XG Boost algorithm, Heroku.

One of the most significant and well-known elements of research in the banking and insurance industries is loan prediction. Non-payment risk is a significant concern for financial institutions as it can lead to financial losses and impact their overall stability. Processes for making decisions can become much more effective and accurate by automating the prediction of nonpayment risk. In the proposed study, we automate the nonpayment risk using the well-known machine learning technique XGBoost. The ensemble learning algorithm XGBoost is renowned for its superior performance with structured data and classification issues. XGBoost can efficiently analyses historical data and discover significant patterns related to nonpayment risk by utilizing its strong features. The XGBoost model can be used to forecast nonpayment risk for fresh, unforeseen cases after training. The model creates a probability score that indicates the chance of nonpayment by taking into account pertinent case-specific data, such as client demographics, transactional information, and credit history. To evaluate the performance of the XGBoost model, various metrics such as accuracy, precision, recall, and F1 score can be utilized. The proposed system will contribute to the growing body of literature on the use of machine learning in financial risk management and highlight its potential for improving efficiency and reducing risk and also provide recommendations for future research.

Published Paper   E-Certificate

Sourav Chitnis, Shashank Jagtap, Parth Apamarjane, Akshay Patil, & Prof. Naved Raza Q. Ali (May-2023). Non-Payment Risk Automation Using Machine Learning and its Deployment on Android Application.. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), e468-e484. https://ijnrd.org/papers/IJNRD2305457.pdf

Volume 8 Issue 5, May-2023

Pages : e468-e484

Paper Reg. ID: IJNRD_195713

Published Paper Id: IJNRD2305457

Downloads: 000121988

Research Area: Computer Engineering 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305457.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305457

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave | IJNRD.ORG | IJNRD.COM | IJPUB.ORG

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

Indexing Coverage includes Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar (AI-Powered Research Tool), Microsoft Academic, Academia.edu, arXiv.org, ResearchGate, CiteSeerX, ResearcherID (Thomson Reuters), Mendeley, DocStoc, ISSUU, Scribd, and many more recognized academic repositories.

How to submit the paper?

By Our website

Click Here to Submit Paper Online

By Mail

Send your paper to editor@ijnrd.org

Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: IJNRD is an International Peer-reviewed, Refereed, and Open Access Journal with Transparent Peer Review as per the new UGC CARE 2025 guidelines, offering low-cost multidisciplinary publication with Crossref DOI and global indexing.

Subject Category: Research Area

Call for Paper: More Details