DOI: 10.5281/zenodo.10618277
title: Classification results of the studies analyzed in A State-of-the-Art Review to Examine the Impact of Intelligent Document Processing in Banking Automations

description: 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.