PalTech turned a BFSI analytics leader’s endless dashboards into concise actions, cutting 80% analyst workload

Executive Summary

A leading analytics partner to credit unions across the U.S., had more data than they knew what to do with. Their dashboards sprawled across 100s of performance metrics—powerful on paper, but overwhelming in practice. Clients struggled to find real direction, forcing their’ own analysts to interpret each KPI manually. 

PalTech stepped in to build a sleek, intelligent platform that automates analytical thinking. Leveraging Snowflake, Power BI, Azure, and a hybrid ML–GenAI approach, we condensed endless charts into short, customized insight summaries. Credit union execs no longer see walls of numbers; they get an accurate and concise action plan. 

Today, the company scales its analytics offerings without scaling headcount. They’ve turned raw data into meaningful direction—fast, consistent, and refreshingly user-friendly. 

Business Problem

Data Overload Stalls Decisions

The client specializes in analytics for credit unions—smaller financial institutions that handle everything from loans and deposits to regulatory compliance. While they offered deep insights (think member segmentation, loan benchmarks, marketing analytics), the analytics dashboard was data heavy and insight scarce with several pages in Power BI covering more than 40+  KPIs. Each dashboard crammed in dozens of KPIs—everything from net worth to delinquency ratios—without a clear summary. 

Credit union leaders found themselves drowning in numbers, asking questions like, “Which KPIs really matter?” or “What exactly should we do next?” The problem was twofold: first, the reports themselves were dense; second, analysts spent hours distilling them for each client. With hundreds of credit unions in play, this approach couldn’t scale. 

Worst of all, this data overload eroded the client’’ core value proposition. Clients wanted quick, strategic answers—yet the existing setup demanded more interpretation than they could handle. Their in-house data team took an initial stab at a GenAI-only approach, but the tool struggled with complex calculations and failed to deliver reliable results at scale. They needed a smarter way to fuse human-like interpretation with bulletproof data processing.

Solution Implemented

A Hybrid Engine That Marries ML Precision and GenAI Foresight

Paltech’s answer was to build a “Hybrid AI” platform—where the heavy lifting happens in machine learning, and the final output is shaped by a carefully orchestrated GenAI layer. First, we revamped the client’s data infrastructure on Snowflake, Azure, and Power BI, creating a rock-solid foundation for handling thousands of credit unions’ data.

Key highlights of our implementation: 

• Data Backbone Overhaul: Migrated raw credit union data (member info, loan stats, regulatory filings) into Snowflake, integrating Azure and Power BI for stable, high-performance analytics.
  
• ML-Driven Clustering: Developed machine learning models that group credit unions by net worth, membership size, delinquency ratios, and more—pinpointing each CU’s strengths and weaknesses.
  
• Metric Scoring & Comparison: Calculated numerous of KPIs (e.g., return on assets, delinquency percentage) to mark each metric as “good,” “average,” or “needs attention.” Peer benchmarking highlights where a CU stands within its exact financial cluster.
  
• Custom Generative AI Layer: Fed the curated ML outputs into a specialized GenAI engine, armed with Financial Services context. This layer interprets numeric results, weaves in domain knowledge, and writes a concise summary with actionable tips—no more sifting through endless dashboards.
  
• Q&A Style Insights: The system can also act like a conversation partner. Users simply ask, “How do we reduce delinquency?” and get a clear, data-backed response referencing both internal best practices and third-party research. 

By splitting the work—numbers crunched by ML, insights articulated by GenAI—we eliminated the accuracy issues typical of a purely GenAI approach. The finished product churns out crystal-clear action points, helping the Credit Unions know exactly what matters and why. No human analyst required.