Building a Prequalification Scorecard to Automate Repeat-Client Screening

Efficient Capital Labs had a recurring problem with its repeat clients. Companies that came back every few months for additional funding were being put through the same full document review cycle each time, generating back-and-forth queries that frustrated clients and slowed the sales team, particularly at month-end and quarter-end.

Ankita identified the bottleneck and proposed a solution at an internal hackathon. She designed an eight-metric prequalification scorecard based on the criteria the underwriting team already relied on, and structured it into three decision zones: green (qualifies on all metrics, proceeds automatically), amber (fails on one or two metrics, routed to an underwriter for targeted review), and red (fails broadly, declined at the portal stage).

The system was built in collaboration with the engineering team. Ankita provided the formulas for each metric and reviewed the code to verify that the logic was translating correctly from the underwriting framework into the automated system. The goal was to let the portal surface a preliminary decision to the client in real time, reducing the number of queries raised for clients who clearly qualified.

After her hackathon presentation, the project was selected as one of four initiatives moving into active development. The engineering and leadership teams are currently building the dashboard. The sales team responded positively, noting that the system would free them to focus on new client acquisition rather than managing repeat-client queries.