The equipment finance industry is undergoing a seismic shift as automation and machine learning tools redefine traditional processes. One such innovation is AWS Textract, a powerful machine learning tool that extracts structured text and data from scanned documents. When combined with Amazon S3 buckets for secure storage, Textract transforms the way financial professionals analyze financial statements . At LeaseSpark, we leveraged these technologies to develop a solution that dramatically enhances the efficiency, accuracy, and usability of credit analysis workflows. The Problem: Time-Consuming and Error-Prone Credit Analysis For credit analysts, reviewing financial statements is a crucial yet time-intensive task. Assessing an account’s creditworthiness requires manually extracting key financial ratios, pulling credit reports, and evaluating other determining data. This process can take anywhere from 30 to 90 minutes—depending on corporate credit policies—and is highly susceptible to human error. The manual nature of this workflow slows down decision-making and increases the risk of inaccuracies that can impact financial institutions' risk assessments. The Solution: Automation with AWS Textract and LeaseSpark LeaseSpark reimagined financial statement analysis by embedding AWS Textract into its application workflow, utilizing Amazon S3 buckets for document storage. Our solution allows users to upload a PDF financial statement directly into LeaseSpark’s interface, where Textract scans and extracts key financial data fields instantly.