FutureVault’s Intelligent Document Processing engine delivered a 1,325% ROI for a large financial and insurance institution by saving more than 100,000 hours of processing work.  

In large financial services and insurance organizations, documents sit at the center of nearly every operational and compliance process. As volumes grow and files are merged over time, what begins as a manageable workflow often becomes a structural constraint—millions of pages that exist, but cannot be efficiently understood or acted upon without significant manual effort. 

Recently, FutureVault worked with a large financial and insurance institution with more than 7 million clients and ~480B of AUM, to replace an impractical, labor-intensive document review process with an automated, scalable solution using Intelligent Document Processing (IDP).  

The result? A 1,325% ROI and—just as importantly—a successfully executed project that would not have moved forward using traditional processing methods. 

When millions of pages make a project too expensive to attempt, automation isn’t an optimization—it’s the only way the work gets done. 

The Reality Most Organizations Face 

The uncomfortable truth across financial services and insurance: projects of this nature and scale almost never move forward when the only option is manual processing. 

The reality is that manual document processing at this scale introduces compounding challenges and enterprise risks: 

• The cost grows faster than budgets allow 

• Timelines stretch beyond acceptable planning horizons 

• Internal teams are pulled away from higher-value work 

• Quality and consistency of processing and data extraction degrade over time 

• Leadership confidence in delivery erodes 

As a result, organizations often postpone action, accept partial visibility, or leave valuable data locked inside documents indefinitely. 

This project faced that exact inflection point. 

The Challenge: Massive Scale, No Structure 

For this particular document and data extraction processing projection, this institution was managing tens of millions of pages embedded inside large, unstructured PDF files. Each PDF contained between 10 and 200 individual documents, merged together without separation, indexing, or labeling.  

Each page required specific data to be extracted – based on document type, context, and other requirements – to be output in a CSV and table format, delivered to the institution and ingested into other enterprise systems for data governance and recordkeeping. 

As a consequence, prior to kicking off the project: 

• There was no reliable way to determine what documents existed inside each file 

• Individual documents could not be easily identified, referenced, or reviewed 

• Downstream teams lacked the clarity required for validation, compliance, and distribution 

To address this, a Table of Contents (TOC) was required for every PDF ti clearly define: 

• Which documents were included 

• Where each document began and ended 

• The purpose and context of each document 

This information did not exist anywhere else. It had to be derived directly from the documents themselves. 

Why the Traditional Approach Breaks Down 

Without automation via Intelligent Document Processing, the only path forward would have been manual review and human intervention—page by page. 

The numbers quickly made that reality clear: 

• 20.5 seconds per page, on average 

• 20,000,000 total pages 

• 113,900 hours of effort 

Even at a conservative labour rate of $25 per hour, the cost for this project alone would have well-exceeded $2.85 million. Industry-standard rates would have increased that figure substantially. 

But the more significant issue isn’t just the cost—it was feasibility. 

A project requiring over 100,000 hours of manual effort: 

• Competes with day-to-day operational priorities 

• Requires sustained staffing over multiple years 

• Is vulnerable to turnover, inconsistency, and fatigue 

• Delays the value of the outcome until near completion 

In real-world conditions, initiatives like this are frequently scaled back, delayed indefinitely, or never approved at all. 

This is the critical distinction: manual processing does not just cost more—it prevents the work from happening in the first place. 

The Objective: Make the Work Possible 

The goal was not simply to reduce costs. It was to make the project not only achievable but make it a huge success by extracting data and outputting it into the appropriate formats.  

This solution was designed to:  

• Understand what existed inside each PDF 

• Accurately identify document boundaries 

• Generate meaningful summaries and classifications 

• Produce structured outputs without human review 

• Scale to millions of pages without linear increases in time or cost 

The Solution: Intelligent Document Processing at Scale 

FutureVault’s Intelligent Document Processing (IDP) platform was deployed as the foundation of the solution, supported by automation and large language models. 

Step 1: Ingest Large, Unstructured PDFs 

FutureVault received PDFs containing millions of pages, each composed of dozens or hundreds of merged documents. 

Step 2: Page-Level Intelligence 

FutureVault’s IDP engine analyzed each page individually to: 

• Extract key data signals and structural indicators 

• Detect document boundaries within merged files 

• Establish context at the page and document level 

This eliminated the need for pre-labeled files or manual separation. 

Step 3: Document Identification and Summarization 

For each embedded document: 

• Private large language models summarized the first page 

• Summaries were used to describe the document’s purpose 

• Classification occurred automatically and consistently 

Step 4: End-to-End Automation 

Custom automation and extraction scripts handled: 

• Data extraction, collection and validation 

• Contextual assessment 

• Submission of structured outputs back to as CSV files back to the institution and into their enterprise systems 

Step 5: Structured Delivery 

A master Table of Contents was generated for each PDF, delivering the final output to the institution, enabling clear, reliable visibility into every document and the data contained within.  

A Modular Approach to IDP: Built Like LEGO Bricks 

One of the key reasons this project was such a huge success, resulting in a massive ROI is that FutureVault’s IDP is modular by design. 

Rather than a linear system, IDP components can be assembled based on the specific requirements of a project—much like LEGO bricks. 

For this engagement, the solution combined: 

• Page-level data extraction 

• Document boundary detection 

• LLM-based summarization 

• Automated document classification 

• Workflow orchestration and integration 

Each capability was deployed where needed, without forcing unnecessary complexity into the system. 

The Results: A 1,325% Return on Investment 

Speed 

• 6.2 million pages processed in two weeks 

• A task that would have taken years manually was completed in days 

Cost 

• Estimated manual cost: $2.85M+ 

• Total roject cost: $200K 

ROI 

• 1,325% return on investment 

• Immediate savings with long-term operational benefits 

Why This Matters for Enterprises with Similar Document Processing Projects  

This use case highlights a broader shift underway across financial services and insurance. 

The question is no longer whether organizations can afford to automate document processing—but whether they can afford not to. 

Automation with FutureVault’s Intelligent Document Processing engine: 

• Makes large-scale projects viable 

• Removes dependency on manual labor 

• Reduces operational risk 

• Accelerates time to value 

• Enables better decision-making through access to information 

Most importantly, it allows organizations to leverage, extract, and act on data that would otherwise remain inaccessible – or in other words, captive data.  

The Takeaway 

At enterprise scale, documents are either a constraint or an invaluable asset that can be used as an enterprise catalyst. 

By replacing manual review with Intelligent Document Processing, this institution transformed an unmanageable problem into a completed project—on time, on budget, and with measurable return. 

The true outcome was not just efficiency or cost savings. It was clarity, control, and the ability to move forward where traditional approaches would have stalled. 

For organizations facing similar challenges, the lesson is clear: when automation makes the work possible, value follows.