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

Registration ID: IJNRD_195713

Published ID: IJNRD2305457

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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.

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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

Research Area: Computer Engineering 

Author Type: Indian Author

Country: Pune, Maharashtra, India

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