Loan Insight Analyzer: Transforming Loan Analysis with Machine Learning and Deep Learning

Traditional assessing applicant data is a common requirement of traditional loan approval procedures, which can result in biases and inefficiencies. An automated system has been created to evaluate user histories and offer helpful recommendations for securing loans in order to remedy this. This system evaluates user profiles using machine learning (ML) and deep learning (DL) techniques in order to assess loan eligibility. In order to predict loan approval decisions based on financial and demographic data, such as gender, marital status, income, loan amount, and credit history of Indian clients, this study uses a variety of machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The system also includes government programs that are intended to help users. The created solution reduces reliance on arbitrary human judgments and speeds the loan approval process while improving fairness and transparency through automation of decision-making. The effectiveness of the suggested model is demonstrated by its accuracy of 82%, 80%, 79.03%, 84%, and 81.78% for the DT, SVM, RNN, and LSTM models, respectively.