How PalTech helped a US fund administration company reduce technical debt, remove repetitive work and build an owned data foundation for analytics and AI.
Client persona: US fund administration company supporting investment-fund operations across multiple administrators, accounting sources and reporting workflows.
Executive summary
- Challenge: Core fund operations depended on multiple third-party administrators, spreadsheet-based validation and vendor-controlled reporting, limiting data ownership and the speed of change.
- Approach: Modernize the core, from applications to data, and automate the work around it with the right mix of workflow, RPA and AI.
- Results: Development and QA effort assessed at roughly 70% lower, with full lineage, automated testing, stronger security, faster recovery and governed self-service reporting in Power BI.
- Outcome: The engagement evolved from point projects into a broader technology and AI partnership across modernization, automation and AI.
The problem was not one system. It was the work happening between systems.
The client ran core fund-administration activities through multiple third-party administrators and a mix of internal processes. The model was dependable, but every change carried friction. Reporting depended on vendor schedules. Reconciliation required teams to bring together data from different sources. Validation logic often lived in spreadsheets or people’s knowledge. New fund onboarding meant more extracts, mappings and checks.
The result was a familiar financial-services problem: the data existed, but too much effort was spent preparing, reconciling and validating it before the business could use it. The client also lacked full ownership of the data, logic and reporting roadmap that determined how quickly operations could change.
PalTech approached the engagement as two connected transformations
| MODERNIZE Bring critical data, reporting and controls onto an owned cloud platform so the client could reduce vendor dependence and technical debt. |
AUTOMATE Find repetitive operational work and use workflow automation, RPA or AI according to the nature of the problem. |
ENABLE Create governed data and reusable business logic that could support reporting, analytics and future AI without rebuilding the foundation each time. |
Automation started with specific pain points, then became a broader operating model
Some of the earliest opportunities were in reconciliation and document-heavy processing. These were high-friction workflows where people spent time reading, matching, checking and re-keying information. PalTech began there, but the intent was never to build a collection of disconnected bots.
The team used a simple decision rule: if the process was deterministic, use standard automation. Where legacy interfaces made API-led integration impractical, use RPA or Power Automate. Where the work involved interpretation, ambiguity or unstructured information, use AI. Human review stayed in the loop when confidence or business risk required it.
The team also treated automation as a process-improvement exercise, not simply a tooling exercise. Before automating a step, the workflow was reviewed to understand why the manual activity existed, what controls had to remain, and whether the step should be removed, simplified or automated. That reduced the risk of digitizing inefficient work and made it easier to choose between workflow automation, RPA and AI.
That discipline mattered. It kept the program focused on ROI rather than AI adoption for its own sake. Over time, the work expanded into an automation-focused team looking across operations for manual steps that could be removed, simplified or redesigned.
A representative example: document intelligence
Fund administration involves agreements, forms and records with varying layouts, scanned text, signatures and handwritten information. Conventional OCR can extract text, but it does not always know when an interpretation is uncertain.
PalTech’s document-intelligence pattern automates routine extraction and makes uncertainty visible. The application can identify a field, suggest an interpreted value, show a confidence level and route a low-confidence item to an operations user for confirmation. The user validates or corrects the value before structured data moves downstream. The goal is not to remove human judgment; it is to reserve that judgment for the cases that need it.
The automation work exposed a bigger constraint: the data foundation had to change
Point automation can remove effort from one step, but it does not solve repeated mappings, inconsistent business rules or weak data ownership. As more workflows were examined, it became clear that automation would scale only if the underlying data and controls became reusable.
PalTech therefore built an in-house data platform on Snowflake and dbt. The platform brought fund, accounting, reporting and reference information into a governed environment owned by the client. Reusable ingestion patterns reduced the effort to onboard new sources. dbt captured transformation logic as version-controlled models. Automated tests moved data-quality checks earlier. Lineage and observability made failures easier to trace. Role-based access and masking strengthened control over investor data.
This also changed the relationship between operations and engineering. Business teams no longer had to treat every data issue or reporting change as a one-off request. Common ingestion patterns, reusable models and automated checks gave engineering a repeatable way to respond, while operations gained more confidence that the same definitions and controls would be applied across funds and reports.
Power BI provided governed self-service reporting, while Snowflake Cortex created a path to natural-language and AI-assisted access to trusted data. The important change was not the list of technologies. It was that data, logic, testing and reporting were now assets the client could control and reuse.
What changed with an owned platform
| Before | What PalTech changed | Business effect |
| Vendor-controlled changes | Owned Snowflake + dbt platform | More control over roadmap and cost |
| Manual data checks | Automated tests, lineage and observability | Issues caught earlier and easier to trace |
| Repeated source onboarding | Reusable, configuration-driven ingestion | Faster onboarding of new funds and sources |
| Slow reporting changes | Governed Gold marts + Power BI | Self-service reporting on trusted data |
| Ad-hoc access and sensitive data exposure | RBAC and masking | Stronger, auditable data controls |
Data modernization and automation now reinforce each other
The client’s transformation is strongest when these two tracks are viewed together. The data platform provides trusted information, shared rules and a common analytical base. The automation program uses those assets to remove manual steps. In return, each automated workflow produces cleaner, more structured information that can feed the platform and downstream reporting.
This is why the engagement moved beyond a data-platform build and beyond point automation. PalTech is working across the environment as a technology and AI partner: reducing technical debt, modernizing the core, identifying manual work and choosing the simplest technology that can solve each problem well.
Business value came from repeatability, not just individual use cases
The delivery team assessed development and QA effort at roughly 70% lower under the new model, supported by reusable transformations, automated testing and standardized engineering practices. The platform also introduced full lineage, configuration-driven scalability, stronger security and faster recovery. These figures are directional and reflect the delivery team’s measured assessment.
That repeatability is important in fund administration because growth often means more funds, more reporting variations and more operational exceptions. An owned platform and a consistent automation model allow that growth to be absorbed with less incremental engineering and less dependence on manual controls.
The more strategic gain is that future work starts from a better base. A new fund, report or automation no longer has to recreate the same data preparation and controls. New use cases can reuse the platform, business logic and automation patterns already in place. That changes the economics of modernization over time.
From project delivery to transformation partnership
What began with specific reconciliation and document-intelligence opportunities evolved into a broader partnership across modernization, automation and AI. PalTech’s teams now look at both the technology estate and the operating workflows around it: where legacy architecture slows change, where people are doing repetitive work, and where AI can solve problems that simpler automation cannot.
The guiding principle is straightforward: reduce technical debt where it constrains the business, automate repetitive work where the return is clear, and use AI where intelligence is genuinely required. That balance gives the client a practical path to modernize operations without turning every problem into an AI project.