Financial data has increased rapidly in recent years, making it difficult for traditional accounting systems to manage large quantities of data quickly and efficiently. Traditional accounting systems cannot process data in real-time and therefore do not support predictive analytics or make it easier for organizations to make good decisions. This study proposes an explainable and secure real-time accounting analytics system that uses deep learning and big data technologies. This study uses a BiLSTM model to perform financial predictions and uses explainable AI techniques (SHAP and LIME) to increase transparency. The study incorporates security measures, such as encryption and anomaly detection, to secure financial data. The proposed system will facilitate the real-time processing of data, improve the accuracy of the predictions generated, and enable an organization to obtain a secure and transparent manner ofanalyzing financial information across Multiple industries.
A et al. (Wed,) studied this question.