Project Abstract

My motivation for this project came from observing how much time and money accounting firms spend on existing document processing software, like Dext. Many of these tools are expensive, unintuitive, and still require a fair amount of manual effort. With the growing push for digital transformation in finance, I saw an opportunity to develop a smarter, more cost-effective solution using artificial intelligence. The aim of my project, EagleScan, is to design an AI-powered document processing tool that can extract and categorize financial data from invoices, receipts, and bank statements, reducing both workload and software costs for accounting firms. What makes EagleScan unique is its balance between advanced automation and affordability, positioning it as a practical alternative for smaller firms who may not be able to invest in high-end solutions. Technically, I’ve approached this by designing a cloud-hosted platform using AWS, integrating services like Textract for OCR, and building a user interface with React.js and a Node.js backend. My main focus has been on building a scalable infrastructure, accurate data extraction pipeline, and a user-friendly dashboard. So far, I have completed the backend API, AWS integration, and a functioning frontend for document management. This project could demonstrate how targeted use of cloud and AI technologies can bridge the gap between functionality and affordability in financial tech tools.

Keywords: Intelligent Document Processing, Artificial Intelligence, Cloud Based Platform

 Conference Details

Session: A

Location: Sir Stanley Clarke Auditorium at 11:00 13:00

Markers: Monika Seisenberger, Hoang Nga Nguyen

Course: BSc Computer Science with a Year in Industry 4yr FI

Future Plans: I have a job lined-up