A company tested Jev, a general-purpose classifier designed to work without large training datasets, on bank transaction classification and found it achieved only 40.7% accuracy—insufficient for real-world use. While faster and cheaper than fine-tuned alternatives, none of the tested approaches (baseline LLM, fine-tuned LLM, or Jev) reached the accuracy threshold needed without human oversight, suggesting that effective classification still requires substantial data and domain context.
A product leader argues that deciding what NOT to build is crucial in consumer financial apps. Avoiding feature creep in systems like document hubs and notification centers saves years of maintenance overhead. The key is addressing user needs through simple, existing solutions rather than building complex platforms.