Overview

A Fortune 500 investment management company’s Credit Risk team was spending excessive time manually extracting financial data from annual reports. Analysts had to pull 100+ data points from lengthy reports that varied in format, layout, and language. This manual process caused delays, errors, and slower lending decisions.

The company partnered with FinX to automate the extraction of financial statements, ratios, notes, and KPIs using AI combined with domain-specific rules and expert validation for sensitive data points. The goal was to reduce processing time, improve accuracy, and enable analysts to handle more applications efficiently.

Challenges

• Analysts were manually extracting 100+ data points from inconsistent annual reports, leading to errors and long turnaround times.
• Reports were lengthy, multi-format, and contained scattered financial disclosures, making manual extraction time-consuming and prone to mistakes.
• The process limited analysts’ capacity, delaying credit analysis and lending decisions.
• Reports came in multiple languages and layouts, requiring a solution that could handle diverse formats without compromising accuracy.
  

Solution

• FinX implemented AI-driven extraction of financial statements, ratios, notes, and KPIs from multi-format annual reports.
  
• Domain-specific rules were applied to ensure accuracy and context-specific validation.
  
• Sensitive data points were verified through FinX’s Mojo platform for expert validation.
• The workflow automated data extraction, normalization, and enrichment, delivering a ready-to-use dataset for credit analysts.

Results

• Processing Time: Reduced document handling from 6–8 hours to under 1 hour.
• Accuracy: Achieved 98%+ accuracy on KPIs and financial metrics.
• Efficiency: Cut credit analysis preparation time by 60–70%.
• Scalability: Enabled analysts to process 3x more applications without adding headcount.