TRUSTNOVA: A Predictive Machine Learning Model for Loan Approval

Small low-income banks have problems in making accurate decisions in the approval of loans for low-income earners. Manual checking tends to make there be inefficiency bias and loss of opportunities. This study designs a Loan Approval Prediction System empowered with machine learning that automates the process and optimizes the processes involved in the assessment of eligibility for a loan. Data analysis on applicant details — income, credit history, and repayment capacity — would bring fairer, faster, and more accurate results in the lending decisions for banks. The model provides banks with the necessary conditions for making lending decisions that are fair and efficient. This will in the long run improve the financial inclusion of the unbanked and underbanked populations.