From OCR to IDP: Transforming Banking Document Workflow with AI-Enhanced Robotic Process Automation

Abstract

Purpose: Banking institutions deal with extensive volumes of documentation, such as onboarding forms of customers, compliance records, transaction information, and loan applications, on a daily basis. Manual processing and hard-core automation systems delay, lead to high error rates, and high human work. Artificial intelligence entries into part of the banking processes are admitted, but the majority of the current systems do not possess the contextual understanding which restricts their scope of application in very complex and highly regulated settings. The paper suggests a superior AI-based Robotic Process Automation (RPA) system that will address these shortcomings by facilitating smart, flexible, and regulation conscious document processing.

Materials and Methods: The proposed platform incorporates smart document intake, context-sensitive document cognition and a dual rule-AI decision model. Risk sensitive workflow orchestration is a dynamic route mechanism that documents are directed into routing strategies according to complexity, confidence, and regulatory risk, where routine documents may be fully automated, and the high-risk or ambiguous documents must be sent to humans. Human in the loop validation mechanism will make sure that expert attention is given where it is most effective. Moreover, the system uses the dual-loop learning architecture which is constantly enhanced by human feedback and system-wide analytics to provide flexibility to changing types of documents and regulatory policies.

Findings: Experimental analysis shows significant improvements in processing time, reduction of errors and unwarranted human intervention, and regulatory transparency of explainable and auditable automation.

Unique Contribution to Theory, Practice and Policy: These findings show that context-aware risk-sensitive AI-enhanced RPA is a viable and scalable solution to current bank document processes.