CUSG is a leading technology partner for 100+ credit unions, helping them manage complex member data and operational insights. With a strong foundation in customer experience analysis and engagement tracking, CUSG empowers financial institutions with tools like MemberXP to capture service feedback and performance metrics.
Yet, while the platform collects vast amounts of structured data, extracting actionable insights required significant manual interpretation, limiting executives and non-analysts from quickly deriving business value.
The problem: dashboards without actionable insights
CUSG’s dashboards aggregated thousands of KPIs, performance metrics, and customer experience signals across 100+ credit unions. However, converting this wealth of data into meaningful narratives and actionable recommendations was a major challenge. Executives often struggled to identify high-level trends or red flags without analyst intervention, while analysts themselves spent hours manually summarizing data, slowing down decision-making cycles.
Frontline managers had limited access to tailored recommendations aligned with their roles, leaving critical insights locked inside dashboards. This manual, analyst-heavy process made it difficult for credit unions to scale insight delivery and respond quickly to member needs, highlighting the urgent need for AI for credit union data analysis.
The solution: AI dashboards for credit unions with data insights and recommendations
CUSG partnered with GoML to develop a generative AI MVP using Claude 3.5 on AWS Bedrock. The solution automatically summarizes dashboard data, generates insights, and provides role-based recommendations in real time.
Automated dashboard summarization
- LLM-generated summaries highlighting highs, lows, and trends.
- Persona-based executive summaries with plain-language takeaways.
AI-driven insights and recommendations
- Identified patterns in KPIs (e.g., declining NPS, surging call center traffic).
- Suggested actions (e.g., allocate resources, launch follow-ups, or address specific service gaps).
Conversational query engine
- Users could ask natural language questions about the data.
- Delivered responses in charts, graphs, or tables using AWS QuickSight.
- Differentiated responses for executives vs. analysts.
Guided prompts for deeper exploration
- AI suggested follow-up prompts (e.g., “Show me top 3 areas impacting NPS drop”).
Role-based responses
- Executives: high-level summaries and recommendations.
- Analysts: detailed tables and SQL-driven visualizations.
AWS-native integration
- Leveraged CUSG’s existing AWS stack: Lambda, and SQL Server.
- FastAPI backend for real-time AI-agent interaction.
Privacy and security compliance
- Role-based data access aligned with CUSG’s governance policies.
The impact: accelerating insights through Generative AI for dashboard insights
By automating insights, CUSG transformed complex dashboards into accessible, role-specific narratives for 100+ credit unions.
- 90% reduction in manual data interpretation time for executives.
- Real-time recommendations delivered directly through dashboards.
- Improved adoption of analytics tools across non-technical users.
Lessons for other organizations
Common pitfalls to avoid
- Relying solely on analysts to interpret dashboards for decision-makers.
- Delivering one-size-fits-all reports that don’t align with user roles.
- Underestimating security and role-based access needs in financial data.
Advice for teams facing similar challenges
- Start with generative AI for dashboard insights on a few KPIs to validate quickly.
- Design responses around user personas (executives vs. analysts).
- Invest early in AWS-native integration for security and scalability.
Want to transform dashboards with AI for credit union insights?
Let GoML build your AI-powered analytics assistant, just like we did for CUSG.
