Fintech Use Cases

AI & Data Engineering in Action

Introduction

We're working with a mid-sized financial technology company that provides workflow and data solutions to mortgage lenders. Like many companies in adjacent sectors (fintech, proptech, business services), they operate at the intersection of complex vendor ecosystems, regulated processes, and data-heavy operations.

In companies like this, operational excellence and customer retention depend on two things:

• Reliable, governed data pipelines that create trust and efficiency.
• AI-powered insights that turn unstructured information (emails, conversations, documents) into actionable intelligence for account managers, vendor managers, and executives.

The following use cases illustrate how we help clients move from understanding their current state, to defining a clear roadmap, and ultimately delivering solutions that are adopted in practice.

Use Case 1: AI & Data Audit

Challenge

Leadership lacked a clear picture of how data engineering, reporting, and early AI pilots fit together. Reporting teams were querying production SQL directly, while AI experiments were disconnected from business workflows.

Solution (Audit)

Conducted a comprehensive current-state assessment of reporting functions (SQL, Power BI), data pipelines (Azure Data Factory), and AI readiness. Partnered with the CTO to map strengths, gaps, and quick wins across the stack.

Expected Outcomes

A baseline assessment that identified critical risks (direct DB queries), opportunities (shared datasets), and early AI pilots worth scaling. Alignment across leadership on priorities for modernization.

Use Case 2: AI Strategy & Roadmap

Challenge

Departments were experimenting with Microsoft 365 Copilot, but without structured guidance adoption risked being shallow and fragmented.

Solution (Strategy)

Developed an AI capability roadmap tied directly into the company's data engineering foundation. Ran discovery workshops across Sales, Vendor Ops, Support, and Engineering. Prioritized workflows for Copilot and GPT-powered assistants, focusing on scenarios that could deliver measurable business value.

Expected Outcomes

Three departmental pilots scoped and prioritized. An adoption playbook for scaling AI. Clear sequencing that tied AI investments directly to data governance progress.

Use Case 3: Implementation & Enablement

Challenge

Staff relied on inconsistent, ad hoc reports and lacked training on how to adopt AI into day-to-day work.

Solution (Implementation)

Redesigned reporting pipelines with Azure Data Factory, delivering the first shared Power BI dataset to replace direct queries. Built a reference report on top of curated data to serve as a model for others. Began enabling conversational reporting pilots by linking GPT models to governed datasets inside Microsoft 365.

Expected Outcomes

Trusted reporting across departments, a reduction in redundant ad hoc reports, and early prototypes that showed staff how AI could be embedded into everyday reporting workflows.

Use Case 4: Vendor Performance Monitoring Through Conversation Analysis

Challenge

Operational metrics like turnaround time and completion rates didn't capture the service quality embedded in lender–vendor conversations.

Solution (Audit + Strategy)

Reviewed conversation logs from the Order Management System (OMS). Proposed applying Azure OpenAI models to classify messages by product line, extract KPIs such as response times, and perform sentiment analysis. Designed Power BI dashboards to benchmark vendor responsiveness and satisfaction.

Expected Outcomes

Automated visibility into vendor communication quality. Early identification of underperformers. Dashboards for managers to use in vendor reviews.

Use Case 5: Account Manager Insights into Lender Experience

Challenge

Account Managers relied on anecdotal feedback and fragmented reporting to understand how vendors performed for specific lenders.

Solution (Implementation)

Aggregated all lender–vendor conversations from the OMS. Enriched data with sentiment scores, KPIs, and classifications. Delivered the results through secure Power BI dashboards tailored for Account Managers.

Expected Outcomes

Clear visibility into vendor mix performance by lender. Stronger evidence for recommending the right vendor mix. Improved client relationships supported by AI-powered insights.

Use Case 6: Generative AI-Enhanced Reporting and Monitoring

Challenge

Stakeholders needed insights quickly, but building a custom UI would take months.

Solution (Implementation)

Delivered interim Power BI dashboards powered by Azure SQL. Enriched reports with AI-derived classifications and sentiment. Added monitoring to measure AI accuracy and reliability, with results surfaced back into DevOps pipelines.

Expected Outcomes

Rapid time-to-value with insights accessible in Power BI. A scalable foundation for future AI dashboards. Increased confidence in AI outputs thanks to built-in QA and monitoring.