Unlike traditional optical character recognition (OCR) tools, Textract applies machine learning algorithms to intelligently identify tables, numbers, and contextual text, ensuring greater accuracy. The extracted data is then structured into JSON format and automatically populated into LeaseSpark’s required data fields. Over time, the system learns from any corrections made by users, improving accuracy and confidence scores for complex or customized queries.

Enhancing the User Experience:

Recognizing that automation is only as useful as its usability, we introduced two key enhancements to further optimize the user experience:

1. Traffic Light Data Confidence Scoring System

To provide transparency on data accuracy, LeaseSpark assigns a confidence score to each extracted data point using a traffic light system:

• Green (High Confidence) – The extracted data is highly accurate.

• Yellow (Moderate Confidence) – The data is likely correct but should be reviewed.

• Red (Low Confidence) – The extracted data requires verification.

This system allows credit analysts to quickly identify potential inaccuracies and focus their attention where it’s needed most.

2. Data Finder for Enhanced Verification

• To further streamline verification, LeaseSpark introduces a Data Finder feature. Users can click on any extracted data field within the application, which then directs them to the exact location within the original PDF document, highlighting where the data was sourced from.

• This feature eliminates the tedious task of searching through financial documents, significantly reducing analysis time and improving accuracy.

By integrating AWS Textract, S3 buckets, and LeaseSpark’s intuitive interface, we have transformed a process that traditionally took up to 90 minutes into one that can be completed in under five minutes. The automation of financial statement analysis saves valuable time for credit analysts while reducing errors and enhancing decision-making capabilities. Furthermore, the machine learning algorithm is consistently being improved every time it is used through a training mechanism.

Conclusion

The adoption of machine learning and intelligent automation in financial services is no longer a future vision—it’s happening now. AWS Textract, paired with LeaseSpark’s intuitive enhancements, is revolutionizing financial statement analysis by reducing processing time, improving accuracy, and increasing usability. As the finance industry continues to embrace automation, solutions like these will become indispensable in accelerating risk assessment and credit decisioning.

LeaseSpark is paving the way for a new era of efficiency and intelligence in financial analysis, where technology enables professionals to focus on what truly matters—making informed, data-driven financial decisions.