99% Accuracy, 80% Faster: The Impact of AI on Finance Data Extraction 

Will 2025 finally be the year finance says goodbye to manual data extraction? 

From manually keying inputs from PDF statements to reconciling values in Excel, finance remains bogged down in repetitive, error-prone tasks. But what if there was a way to make data extraction smarter, faster, and more accurate?   

CrossCountry Consulting’s AI-powered data automation accelerator is paving the way for a new era of efficiency in financial operations, enabling the Office of the CFO to transform workflows, achieve unparalleled accuracy, and redirect energy toward strategic, high-value activities. 

Here’s a closer look at transformation in action. 

Turning the Page on Manual Finance Data Extraction    

As one of the biggest operational bottlenecks and resource drains, data extraction persistently creates process inefficiencies that reverberate throughout the organization. Delays in capturing key financial inputs generate compounding delays on period close cycles and speed-to-insights. 

• Wasteful processes: Processing hundreds of PDFs and reconciling data can devour countless work hours and reduce revenue visibility. Simply getting data into a structured, systematized format is where most companies are losing time.   
• Untrusted data: Manual data entry is inherently risky, leading to inaccuracies that can delay closing cycles and create material weaknesses from an audit perspective.   
• Lack of transparency: Manual methods lack audit trails, making it harder to trace transactions or spot inconsistencies. 

For organizations ready to adapt, data extraction can now be automated with AI for greater speed, accuracy, and transparency.  

AI-Powered Data Automation Driving Rapid Results 

Replacing manual work with an auditable, systematized approach is a significant achievement for any organization. Beyond the operational efficiencies delivered, the timeliness, quality, and structured data produced elevate finance functions above transactional tasks and enhance the value they provide to the wider organization.  

To date, we’ve helped companies unlock significant results with this approach: 

• Achieving 99%+ data accuracy: A robust approach to data validation and reconciliation delivers consistent, trusted data with traceability to source systems.   
• Reducing processing times by 80%: Bulk upload hundreds of PDFs in minutes, freeing up valuable time for actual analysis and decision-making.   
• Enabling full auditability: AI-powered automation ensures a fully traceable process, with comprehensive audit logs linking input documents to ledger transactions.   
• Accelerating close and speed-to-insights: Faster data ingestion reduces time-to-close and improves accuracy of revenue snapshots. 

Here’s an illustrative example of leveraging this accelerator to automate contribution/distribution matching and the generation of associated journal entries:

The key to moving beyond the chaos of manual data extraction lies in developing an optimized, step-by-step AI workflow.  

How AI Automates Data Extraction   

Step 1: Train the AI Model 

Your AI engine is deployed with models specifically configured to recognize and ingest target fields and patterns from your documents, and, over time, it learns to increase the precision of automated extraction.  

Step 2: Upload Documents in Bulk   

Input documents (.pdfs, xls, csv, or other input files) are bulk uploaded into the system. Processing only takes a few minutes.   

Step 3: Validate Data   

Every field is automatically validated for accuracy, and thresholds for expected values can be configured to capture unexpected outliers. If issues arise, secondary AI models tackle the problem or flag it for manual intervention.   

Step 4: Consolidate Data for Reporting   

After full validation, data is consolidated across dimensions into a standard format. Business rules to enrich transaction data can then be applied before posting.   

Step 5: Reconcile Transactions 

As needed, automated reconciliation of transactions can be configured to match across datasets on specific value fields. Similar to our tiered validation approach, this reconciliation is performed automatically, and exceptions requiring manual intervention are escalated. 

Step 6: Auto-Generate Journal Entries   

Validated, enriched, and auditable transactions are now ready for posting to your ledger. Manual or automated workflows can be configured to optimize this process depending on the degree of interconnectivity between systems.   

The result? Data accuracy, auditability, operational time savings, and a more valuable finance organization. 

This flexible, dynamic approach to automating data ingestion can be applied widely across the finance function, supporting revenue recording, transaction matching, compliance-driven reconciliations, and more.  

Transform Your Finance Operations   

Automating data extraction and transformation is more than just a tactic to help CFOs stay ahead of the competition. It’s part of a larger strategy for transforming the finance function. Adopting AI to systematize key manual processes enables teams to reclaim their time, focus on forward-thinking initiatives, and supercharge growth.   

If you’re ready to unlock the full potential of AI for finance, contact CrossCountry Consulting.