Overview

With a need to monitor and extract financial data from approximately 50,000 global websites, our client sought a scalable and robust automation solution for unstructured financial data handling. The complexity of website structures, multilingual content, and document classification posed significant challenges to achieving this at scale.

Challenges

The client encountered significant challenges in extracting data from diverse and complex websites, which required tailored approaches and advanced tools to address operational inefficiencies effectively.

• Identifying and classifying newly published financial documents was a significant hurdle.
• Navigating and extracting data from complex site structures complicated the data extraction process.
• Multilingual websites posed language barriers that hindered accurate data handling.
• The dataset was cluttered with irrelevant or duplicate documents.

How we solved the problem

XDAS provided a holistic solution for financial data automation, starting with optimized website monitoring and automated data extraction from data sources to ensure timely data retrieval. Captured metadata was validated, structured, and stored to eliminate duplication and noise. AI-powered analysis offered deeper insights, while Human-in-the-Loop (HITL) validation ensured accuracy and quality. This streamlined approach enabled compliance-ready reporting and enhanced decision-making.

Automated data collection and link processing

Handling data from complex site structures, including static, dynamic, script-loaded, login-based, and CAPTCHA-protected sites, was addressed by deploying an HTML downloader to fetch offline HTML pages from specified URLs.

A PDF link extractor parsed these pages to identify new PDF links validated and categorized by a link segregator bot. The PDF downloader bot seamlessly handled the downloading of validated PDFs, ensuring accurate and efficient data extraction.

Metadata extraction and knowledge validation

XDAS utilized a PDF Metadata Extractor to process the documents and pull critical metadata from each PDF. This step was crucial to handling multilingual data, ensuring structured data processing across diverse content types. The metadata was then cross-checked against a knowledge base to ensure consistency and eliminate irrelevant or duplicate entries. This validated metadata was updated in real-time in the master repository, providing an up-to-date and reliable data source for further stages.

LLM-based financial data extraction

Advanced LLM extraction techniques were applied, using tailored prompts to extract the relevant financial data from the documents. The extracted data was then formatted into a JSON Transpose to match the client’s specifications, ensuring compatibility for easy integration with other systems.

HITL validation

XDAS integrated MOJO for a human-in-the-loop approach to ensure high-level accuracy. Trained human agents reviewed flagged records during HITL curation and validation. Final validation through HITL quality control ensured accuracy and consistency before transferring the data to the final repository for reporting and analysis.

The outcome was a streamlined, error-free data extraction process that enabled real-time insights and informed decision-making. By automating the flow from data extraction to validation and reporting, XDAS helped the client manage and analyze complex financial data, enabling better decision-making and compliance-ready reporting.

Results

99% accuracy in data extraction

XDAS automated data extraction, achieving 99% accuracy with advanced workflows and HITL validation.

3x faster results

XDAS delivered results 3x faster than manual methods, reducing processing time by 48%.

Seamless data processing at scale

XDAS handled static, dynamic, and CAPTCHA-protected sites, scaling to process data from up to 50,000 websites.

Multilingual data handling

XDAS ensured accurate metadata extraction and translation across languages for global reach.

Clean data for better decisions

Advanced classification filtered out irrelevant data, providing clean datasets for strategic decision-making.