Case Study
AI-Powered Partner Database Drives Multimillion Value for Fintech
- Intelligent Automation (IA)
- Artificial Intelligence (AI)
- Banking & Financial Services
- Microsoft
- Intelligent Extraction & Processing
1
opportunities identified through revenue recovery and partner consolidation
98
faster contract metadata capture, from 25 mins. to 2 mins. per contract
700
qualified partners consolidated from a list of 1,400
Client Overview
This leading fintech equips financial institutions, businesses, and developers with mission-critical platforms across banking and capital markets, serving 20,000+ clients worldwide with a team of 50,000+. It is widely recognized among top core-banking providers with a product suite that spans the money lifecycle, from core banking systems to real-time financial data access.
Over several years, Ashling’s partnership with this fintech has evolved from project-based implementations into a strategic collaboration. Together, we've expanded from early automations to programs that explore the breadth of emerging Agentic AI technologies. More recently, the VP of Corporate Strategic Partnerships has been working with Ashling to tackle an increasingly complex use case: Redesigning their partner experience.
There are a number of potential use cases and a lot of interest around Agentic Automation. All partners I tried were to some degree capable, but they broke down. Ashling was the only one that didn’t break trust. Now, our goal is to work with them to identify the best opportunities for successful implementation.
From Scattered Data to a Single Source of Truth
Years of growth and acquisitions created a familiar problem: critical partner information lived across teams and systems. Partner contracts were in multiple repositories, siloed from revenue and spend data, and lines of business tracked their respective partners in spreadsheets.
Leaders struggled to get timely answers to simple questions like: ‘Who are our partners?’ or ‘Are we earning our entitlements and meeting our obligations?’ and whether each relationship was delivering value. This lack of visibility slowed them down, amplified risk, and left revenue opportunities untapped.
The Approach
The objective was clear: to create a single source of truth for all partner entities. For the first time in our client's history, they set out to build a comprehensive view, layering data from multiple systems, such as spend and revenue, with the full context of each relationship captured from contracts, and distilled for leadership stakeholders.
We began by gathering an inventory of more than 1,400 potential partners from every business segment and corporate function. Working with stakeholders, we aligned on what qualified as a partner and narrowed the list to just over 700 — creating a reliable, structured dataset that would serve as the foundation for AI and automation.
Building the Capability
We first unlocked value trapped in unstructured documents. Contracts and amendments arrived as PDFs, each with different formats and wording. Using Microsoft Power Apps, Power Automate, Power BI, AI Builder, and generative AI, we built a pipeline that retrieves contracts via API, uses Intelligent Extraction and Processing to classify documents, extract the fields that matter, and turn unstructured contract data into a searchable database that teams could trust. Generative AI then drafts engagement-level summaries that capture obligations, entitlements, and key terms. Business analysts review and approve inside the app, which creates a consistent human-in-the-loop checkpoint and an auditable trail.
The application unlocks three clear views once records are validated:
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All partners directory for fast discovery and filtering
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Partner profile with relationship type, identifiers, and AI-generated summaries
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Engagement details to drill into specific agreements and terms
Power BI brings it all together by mapping extracted contract ID's to partner IDs, spend and revenue. The result is a clear line of sight from contract to cash with the ability to benchmark performance and spot gaps quickly. Intake, evaluation, negotiation, validation, and integration now move through one controlled process. As new engagements close, partner data is captured and consolidated in the partner database, with key metrics feeding into the dashboard.
Three cheers for this team. I really can't explain what it means to me that this team can deliver the end-to-end results that they do. The seamless work product between our people is nothing short of amazing.
Results that Compound
Within weeks, contract metadata capture dropped from 25 minutes to 2 minutes per document. Analysts reclaimed hours and leaders finally had like-for-like comparisons across the portfolio. With consistent categorization, keyword search capabilities, and a shared view of critical partner metadata with spend and revenue, the company identified underperforming relationships, created leverage in negotiations, and paired revenue recovery with partner rationalization to surfaced multi-million-dollar opportunities that had been hiding in plain sight. They can now:
- Identify underperforming partnerships and take action
- Create clarity at the offset and leverage insights during negotiations
- Avoid waste from partnerships that fail to deliver value
- Pair traditional revenue recovery efforts with partner relationship audits
- Leverage AI-driven insights and search capabilities in their existing partner base
What Comes Next?
Reusable by design, the framework scales beyond partnerships to SLAs, vendor contracts, and data privacy programs, amplifying impact across the business. Conversations to extend this capability to other areas, such as procurement, are already in motion. With governed data and a common process, teams gain the clarity and control to maximize the value of every existing and new partnership.
Technology Used
