Design and Development of Deep Learning based Financial Statement Analysis

Analysis of financial statements has an important role to play in evaluating the financial performance, stability, and health of organizations, providing the basis for investor, analyst, and stakeholder strategic decision-making. Conventional analysis relies greatly upon manual interpretation, statistical methods, and ratio analysis, which can be time-consuming, susceptible to errors based on human interpretation, and restricted in their ability to identify subtle, non-linear trends in extensive financial databases. In addition, traditional machine learning algorithms have difficulty with feature extraction and scaling in the context of unstructured or semi-structured financial data. To eliminate these shortcomings, this study introduces a new model of financial statement analysis using novel multi-layered Convolutional Neural Networks (ML-CNN). The CNN model is capable of automatically learning deep features and hierarchical patterns from well-structured financial data, enhancing the accuracy and efficiency of predictive and classification tasks. Unlike classical models, ML-CNN does not need large amounts of manual feature engineering and can efficiently deal with varied input formats. Comparative analysis is performed with currently used analytical approaches to measure performance in accuracy, precision, recall, specificity, and F1-score. The outcomes show that the designed CNN model outperforms, with 96% accuracy, 96.01% precision, 96% recall, 96.1% specificity, and 96% F1-score. These results validate the effectiveness of the model for providing consistent and automated financial statement analysis.