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 most
important 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 of
handling and granting loans underwent a significant change with the introduction of use cases
concerning machine learning and data science.
Hence, this data-driven research utilized advanced machine learning techniques to analyze and
manipulate 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 from
the client. The dataset was tested using two different methodologies: a logistic regression model
and a Neural Network algorithm. Both of these methodologies produced high-level accuracy
rates. However, the latter outperformed the currently used methodologies by over 20%, resulting
in 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 the
Neural Network model. A performance assessment was conducted based on a confusion matrix
evaluation to demonstrate its feasibility and performance