Optimizing Bank Loan Approval with Cutting-Edge Deep Learning model

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