The Problem

A financial data provider based in Bulgaria set an ambitious goal to build the largest comprehensive database of company financials in the region. However, various factors threatened this vision. Several OCR vendors failed to meet their accuracy and scalability requirements, trapping 40% of important financial data in subpar documents.

Main Technical Challenges

Inaccurate Information Extraction

Current solutions failed to extract data accurately

Inadequate PDFs

Poor document quality reduced extraction effectiveness

Dispersed Financial Data

Data spread across various statement types with different representations

Manual Intervention

Adjustments needed for missing data points

Document Rejection

System rejected documents that didn't adhere to templates, causing silent data loss and hindering database growth

The client's current system created a significant bottleneck by labeling any document with missing information as "data deficient" and outright rejecting it. Additionally, the client's system was not built to handle files with insufficient data. As a result, the client discovered significant information gaps that they had not previously recognized. In accordance with their standard procedure for manual processing, the client requested that we overcome this obstacle and continue to fill the template with derived data.

Our Solution

As a POC, we worked on around 50 documents with varying difficulty levels. Our financial data extraction agent extracted the information with 95% accuracy, which the client found to be highly impressive. This led to scaled-up delivery, processing 1,000 documents in batches.

Solution Process Flow

Document Ingestion

Process various document formats

AI Processing

Extract and verify financial data

Data Reconstruction

Fill missing data points

Database Integration

Structured data storage

Getting Past Implementation Obstacles

Addressing the client's requirement for template adherence resulted in the biggest breakthrough. We created a complex workaround that significantly altered the client team's data processing capabilities in close collaboration with our in-house subject matter experts.

Reconstructing Data Intelligently

To guarantee that every document satisfies the precise template structure needed by the client's system, we developed automated systems that find and recreate missing data points.

Advanced Document Processing

We applied AI-driven features specifically designed to manage low-quality documents while maintaining 95% data quality standards across previously inaccessible data sources.

Intelligent Mapping System

We created systems that use rules to pull out information, making sure that different words for the same data are treated the same, unifying different ways of showing data.

Automated Value Derivation

New systems were created to fill in missing information by automatically changing and calculating values from notes and indirect references while keeping the data consistent.

Business Impact

• The system eliminated almost all of the data gaps that previously restricted the database's coverage width, increasing coverage from 60% to 99%.
• Increased language coverage from 8–10 languages to 40+ languages, enabling comprehensive regional coverage and establishing the client as the go-to source for financial information.
• Enhanced data accuracy from 70–75% to 98%, providing a solid basis for financial analysis and decision-making.
• By removing silent data loss and unlocking previously inaccessible information, we successfully built the largest comprehensive financial database in the region.

Strategic Business Results

Through this collaboration, the client's standing was improved from that of a regional player with data gaps to that of the leading source of financial data for all of their target markets. We helped them realize their ambitious goal and set new benchmarks for data quality and coverage in the financial information industry by resolving the template adherence issue and putting intelligent data reconstruction into practice.