Fusion of Multiple Deep Learning Models for Multiple Data Sets by Extracting the Images from Heatmap for Calculating Credit Card Score

Credit Score plays a role in various fields, especially where it requires a lot of data, such as total income, credit history and can also make decisions in Google Analytics. It can represent a mathematical model which are based on statistical methods and can be used with a large amount of information. Various methods are used to identify an efficient score. It plays a crucial role in financial decision making, which impacts institutions and customer satisfaction. Traditional credit scoring models mostly rely on statistical methods and structured data, which sometimes fail to capture complex datasets. In this research, we are using an ensemble deep learning approach that will enhance the score of the credit by converting the data into heatmap images, which we will use as the representation of our data. We will use Principal Component Analysis (PCA) for dimension reduction, alongside Convolutional Neural Networks (CNNs), which will help in feature extraction from heatmaps. For extracting CNN features, we will use two models: Bagging Regressor and XGBoost Regressor. Our findings from the dataset it will help to demonstrate that heatmap-based learning models will improve score accuracy and provide a novel approach for financial assessment.