Problem

Field teams need interpretable credit-health indicators that combine financial records with contextual observations.

Implementation

The hackathon prototype joined application, data, and machine-learning services into a reviewable assessment workflow.

  • Explainable credit-health assessment
  • Structured and qualitative data inputs
  • Coaching insight generation
  • Semifinalist prototype delivery

Key technical decisions

  • Presented contributing signals alongside outputs
  • Separated model processing from the application API
  • Kept the interface focused on actionable coaching context

Security considerations

  • Treated financial fields as sensitive application data
  • Separated service credentials from client code
  • Limited the prototype to explainable outputs