Optimizing Bank Loan Approval with Cutting-Edge Deep Learning model

Abstract
For any bank or financial institution, managing loans and controlling leverage is one of the mostimportant tasks they have to undertake. A bank cannot function efficiently without a well-designed loan-to-deposit business model. As technology continues to evolve, the mechanism ofhandling and granting loans underwent a significant change with the introduction of use casesconcerning machine learning and data science.Hence, this data-driven research utilized advanced machine learning techniques to analyze andmanipulate the data, aiming to predict the best possible way to recommend a loan to a client.These predictions are based on modified yet unique features created from the data obtained fromthe client. The dataset was tested using two different methodologies: a logistic regression modeland a Neural Network algorithm. Both of these methodologies produced high-level accuracyrates. However, the latter outperformed the currently used methodologies by over 20%, resultingin an accuracy of 90%.The successful research results were obtained due to the use of a perfectly balanced, unbiased,and cleaned dataset, as well as the well-executed combination of activation functions for theNeural Network model. A performance assessment was conducted based on a confusion matrixevaluation to demonstrate its feasibility and performance