In financial services, there is always talk about transformation — and lately, even more about AI. But for most institutions, the real bottlenecks haven’t changed.
Manual data entry. Document reviews. Tedious cut-and-paste.
These slow, error-prone processes are still buried deep in core workflows, and our recent webinar, Beyond OCR: AI for Financial Workflows, showed how Linedata and nRoad’s new partnership is on the cutting edge of addressing these bottlenecks. We shared what’s possible when you stop chasing hype and start solving problems with purpose-built AI — embedded directly inside the systems teams already rely on.
This wasn’t a theoretical conversation. It was a live look at how Linedata Capitalstream is helping banks and lenders automate the extraction, normalization, and validation of financial data without sacrificing control, auditability, or accuracy.
The Manual Problem That Never Went Away
Every financial institution we talk to is wrestling with some version of the same challenge: how to efficiently extract data from unstructured and semi-structured documents — whether it's tax returns, financial statements, or onboarding files.
These aren’t superficial use cases. They’re central to credit decisioning, servicing, compliance, and portfolio monitoring.
“I still get asked to print, scan, and resend PDFs to my own bank — and they take 4 to 7 business days to process it,” said Gary Brackenridge, EVP at Linedata, during the session. “That tells you where the industry stands.”
Bringing AI Inside Capitalstream
Capitalstream now includes AI-powered autospreading functionality automating the extraction and mapping of financial data directly within the platform.
There’s no handoff to third-party tools or additional integration effort.
Here’s how it works:
- The user uploads or attaches a financial document to a customer record
- Capitalstream reads and extracts key data — income statement, balance sheet, footnotes, and more
- The data is automatically mapped to the customer’s unique spread template or chart of accounts
- Analysts retain full control, with the ability to trace values back to the source, validate accuracy, and make adjustments as needed
- Any edits feed back into the model to improve results over time
It’s fast, traceable, and analyst led. The way it should be.
The Demo: Three Years of Spreading in Under Ten Minutes
Ron Meyer, 25-year banker, and current Sr. Business Advisor at Linedata, led a live demonstration of this functionality inside Capitalstream. He uploaded three years of financials and walked through the autospreading process in real time — from document upload to model training.
The full process took under ten minutes. It was fast, but more importantly, it was controlled. Analysts can make corrections, validate sources, and apply their own judgment — all while the platform learned and adapted.
“Five minutes saved per document doesn’t sound like much — until you multiply it by every analyst, every document, every day,” Ron said. “That’s where the ROI starts to scale.”
Catch the full webinar replay
The Outcomes: Accuracy, Speed, and Control
This functionality is already delivering value in production environments across major financial institutions.
Key business benefits shared during the webinar:
- 95%+ accuracy on complex, multi-year statements
- 60%+ reduction in turnaround time
- Full traceability from data point to source
- Built-in learning loops that adapt to each institution’s approach to spreading
What’s equally important is that none of this sacrifices oversight. Analysts remain in control. The platform does the heavy lifting — and the team applies their expertise where it matters.
“Two humans can interpret the same financial line item differently,” said Claude-Vincent Gillard, Advisor and Industry Veteran at nRoad. “AI gives you consistency and scalability, without losing the nuance that experienced analysts bring.”
Rethinking Roles, Not Replacing Them
One of the more powerful parts of the conversation focused on the human side of automation. This isn’t about replacing analysts — it’s about giving them better tools so they can focus on higher-value work.
“When you automate the tedious tasks, you retain institutional knowledge — and people,” said Aashish Mehta, CEO of nRoad. “You reduce errors, increase consistency, and avoid the real cost of turnover.”
That’s not a footnote. It’s a strategic outcome. In an industry where talent retention is a major challenge, smarter tools that support your people matter just as much as the efficiencies they deliver.
Final Thoughts
There’s no shortage of excitement around AI in financial services — but most of it still lives in theory. What we showed in this webinar was real: AI that’s embedded inside Capitalstream, focused on a tangible problem, and already in use across leading institutions.
✅ Automating a historically manual process
✅ Reducing turnaround time and improving accuracy
✅ Giving analysts better tools — not replacing them
✅ Laying the foundation for intelligent, end-to-end automation
If you missed the webinar, you can still catch the replay above.
We’re always happy to continue the conversation — especially when it leads to smarter, faster, and more efficient lending.
Connect with us today.