• V Vijay Chandra
• S Shaikshavali
• T Tarun
• U Bharath Teja
• P Nanda Kishore

Credit approval and credit scores are a multi-billion dollar business. Applicants range from students to large companies, from individuals to organizations. It is always a challenge for financial systems to process these applications through scanning. The process of accepting or rejecting an application is time-consuming as there are many variables to consider. Introduce and apply machine learning models using features such as credit history, applicants, credit history, income details and employment information. This project includes background information, sample selection, and more. The design will also be embedded into a web application that will allow users to access data and get real-time predictions about their performance. Applying machine learning models can reduce human bias and slow down processing time. Banks are already considering using AI to determine loan risk and repayment. This project is designed to create a loan agreement using machine learning and put it into a web application through the Flask framework. The goal is to create a powerful model that can identify applicants to predict the likelihood of receiving loan approval.

Machine learning, Bank loan, analytics, AI process, Flask framework

"Bank loan prediction system using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.a681-a688, May-2024, Available :http://www.jetir.org/papers/JETIR2405078.pdf

"Bank loan prediction system using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppa681-a688, May-2024, Available at : http://www.jetir.org/papers/JETIR2405078.pdf