Hymans Robertson partnered with UIC Digital to harness the power of AI to solve one of pension administration’s most complex challenges, extracting and verifying key data buried within decades of unstructured member documents. The resulting AI-driven platform uses machine learning to read, interpret and cross-reference millions of records, enabling faster, more accurate data validation and transforming a previously manual, time-intensive process into an intelligent, scalable workflow.

Data shown is for demonstration purposes only

Results:

• 90% reduction in manual effort

• 10x faster data discovery and validation

• Multi-million £ potential cost savings anticipated on large-scale cleanses

• Full compliance readiness for upcoming 2026 regulations

• Higher staff confidence and significantly less rework

 

The Challenge

Hymans Robertson, one of the UK’s leading pension and investment consultancies, administers over 200,000 pension scheme members across more than 70 clients. For decades, structured member data in their UPM system has been accompanied by millions of unstructured “backfiles” — scanned letters, forms, and PDFs dating back 40 years or more. 

Historically, every backfile review was a manual process. Each record could take 15–45 minutes to review, with administrators opening hundreds of documents in search of a single missing or unverified data point. The work was described internally as “a very, very boring job” — repetitive, error-prone, and costly to clients. 

The additional challenge is that new regulations, due to take effect in October 2026 required that member data be accurate and always evidenced, not only at the point of retirement. Current, manual methods cannot scale to meet that obligation. 

 

The Solution

Following a successful proof of concept that demonstrated the potential of AI to interpret unstructured pension documents, Hymans Robertson commissioned UIC Digital to deliver a full production system: the TPA AI Backfile platform.
The platform automatically reads, interprets and indexes key data from backfiles associated with each pension scheme member. Using OCR and large-language-model analysis, it identifies data such as dates, salaries, contributions and spouse details, highlights their exact location within the original document, and presents them in a clear, auditable interface.
UIC provided end-to-end delivery, designing and engineering both the backend AI pipeline and frontend application. The front end was built using Hymans Robertson’s existing in-house design system to ensure consistency with their digital estate, while UIC focused on the user experience design and technical architecture.

(Data shown is for demonstration purposes only)

 

How We Did It: The PRIME Framework 

To move from a complex data challenge to a global production environment in months, not years, we applied our proprietary PRIME AI Framework. This high-velocity delivery vehicle bypasses “strategy-heavy” cycles in favor of tangible product increments. 

1. Identify (The Sprint) 

We didn’t spend months on discovery. Instead, we utilized short, focused workshops to pinpoint the specific “Value Levers”—the high-value data points that would have the greatest impact on regulatory compliance and operational cost. By bringing together the “Doers” and “Checkers” from the Glasgow headquarters, we ensured the AI was solving for real-world workflow pain points from day one. 

2. Build (Idea to Code) 

In this phase, we moved rapidly to turn the proof of concept into a robust technical foundation. We engineered the full stack solution, integrating AI document processing with a secure Azure-hosted backend. The focus was on “Workflow Fit”—ensuring the AI surfaced data points with a verifiable link back to the source page, creating the transparency required for pension auditing. 

3. Deliver (Global Production) 

The final stage of the PRIME framework is about achieving scale. We deployed the TPA AI Backfile platform as a production-ready ecosystem, not just a pilot. By integrating with Hymans’ single sign-on and existing design system, we ensured immediate adoption. This phase focused on continuous learning, allowing the tool to assist human expertise and support the established Doer/Checker process while scaling across multiple pension schemes. 

The Outcome: Unlocking Value 

By applying the PRIME framework, we transformed a “boring,” manual task into a streamlined, verifiable process. The speed of the PRIME methodology resulted in immediate gains: 

• Efficiency: Record review times reduced from 30 minutes to just 3 minutes. 

• Velocity: Data cleansing cycles that previously took four months are now completed in six weeks. 

• Regulatory Assurance: A consistent, auditable data trail that secures Hymans Robertson’s position for the 2026 regulatory shift.
  

 

Through the PRIME Framework, we moved Hymans Robertson from a whiteboard concept to a market-leading AI administration tool at pace.