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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Non-Payment Risk Automation Using Machine Learning and its Deployment on Android Application. |
| Authors Name: | Sourav Chitnis , Shashank Jagtap , Parth Apamarjane , Akshay Patil , Prof. Naved Raza Q. Ali |
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| Author Reg. ID: |
IJNRD_195713
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| Published Paper Id: | IJNRD2305457 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Loan Repayment, Machine Learning, Random Forest algorithm, XG Boost algorithm, Heroku. |
| Cite Article: | "Non-Payment Risk Automation Using Machine Learning and its Deployment on Android Application.", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.e468-e484, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305457.pdf |
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| 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 |
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Published Paper ID:IJNRD2305457 Registration ID: 195713 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: e468-e484 Country: Pune, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305457 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305457 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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