RETRACTED: Interpreting, Querying, and Securing Complex Financial Documents: A Blockchain-Enabled, OCR & Language Model Approach with Enhanced Cybersecurity Features

Abstract

Background: Financial documents are inherently complex and sensitive, necessitating sophisticated tools for their in- terpretation, querying, and securing. Accessing and interpret- ing crucial information within these documents is vital for decision-making but often proves challenging due to their intricate structure and the sensitive nature of the data they contain. Ensuring robust security and confidentiality for this data is paramount, prompting the need for advanced, integrated solutions. Methodology Overview: To address these challenges, the ”Bank-AI” application is introduced as a comprehensive tool integrating Optical Character Recognition (OCR) technology and advanced language models within a secure, blockchain- enabled framework. The OCR component is precisely engi- neered to extract text and data from diverse financial docu- ments accurately. Meanwhile, state-of-the-art language models are employed to facilitate intelligent and efficient querying and interpretation of the extracted data, providing users with insightful and actionable information. The system’s security is significantly enhanced through blockchain technology, offering tamper-proof, secure handling, and management of sensitive financial data, with advanced cybersecurity features ensuring data integrity and confidentiality. Results: Early evaluations indicate that the “Bank-AI” application markedly improves the efficiency of extracting, interpreting, and securing information from complex financial documents. It not only expedites operational workflows but also fortifies the security protocols safeguarding sensitive financial data. The application shows promising efficiency, accuracy of app is 88% and 90% respectively over time for different query types and potential in revolutionizing the management and analysis of financial documents, making it an invaluable asset for businesses and financial analysts alike. Future Work: While this study establishes a solid ground- work for the ”Bank-AI” application, there is ample scope for further research and development. Future work may explore optimization strategies for enhancing the application’s perfor- mance across various financial document types and domains. Investigating its scalability and adaptability to different finan- cial environments and extending its capabilities to handle a broader array of document structures and formats will also be crucial in future development phases.