Classification results of the studies analyzed in A State-of-the-Art Review to Examine the Impact of Intelligent Document Processing in Banking Automations

This spreadsheet presents the meticulously classified results from the conducting phase of our systematic literature review titled "From Manual to Automated: A State-of-the-Art Review to Examine the Impact of Intelligent Document Processing in Banking Automations." Each entry within this document represents an individual study analyzed during our research, categorized according to a carefully designed classification framework to ensure a comprehensive and clear understanding of the evolving landscape in banking automation through intelligent document processing technologies.

Classification Framework Overview:

Date: Captures the publication year, offering insight into the temporal distribution and evolution of research within this domain.

Contribution: Distinguishes between journal articles and conference papers, providing a perspective on the contribution type and its academic or professional context.

Type of Proposal: Details the focus of each study, whether it be a method, algorithm, framework, etc., highlighting the diversity of approaches in the field.

Integration with RPA: Identifies studies that specifically address integration with Robotic Process Automation (RPA), distinguishing them from those with a broader automation focus.

Application Domain: Specifies the domain of application, illustrating the range of sectors within banking where intelligent document processing is applied.

Validation: Indicates whether the study includes a validation component, and if so, whether it is of an industrial or scientific nature.

Life Cycle Stage: Classifies each study according to the document processing phase it addresses, from data capture to information extraction and beyond.

Techniques Grouping: Provides a high-level characterization of the techniques employed, offering insights into the methodological landscape.

Type of Model: Offers a more granular view of the models used, from traditional machine learning to advanced neural networks.

Model Generalization: Categorizes the proposed solutions as either supervised or unsupervised learning, shedding light on the learning paradigms adopted.

Data Preparation: Highlights whether the study discusses data preprocessing, which is crucial for the success of intelligent document processing systems.

Subarea within AI: Classifies the study within specific AI subareas, demonstrating the interdisciplinary nature of the field.

Integration Proposal: Analyzes the potential for integration with other platforms or tools, indicating the study's applicability in broader technological ecosystems.

Offering of Open Source Resources: Identifies if the study makes its resources available for open use, promoting transparency and replicability in research.

This classification scheme is instrumental in providing a structured, in-depth analysis of the field's current state, trends, and future directions. The framework aids in navigating the vast amount of information in the domain, offering researchers, practitioners, and policymakers a clear vision of the significant aspects of each study to foster informed decisions and further innovation in banking automations through intelligent document processing.