Problem
A risk analytics platform serving thousands of traders, quants, and risk managers had a fundamental discoverability problem: users couldn't find the reports, models, and data views they needed. Keyword search returned irrelevant results, power users had built their own workarounds, and new users gave up. The platform had the data — it just wasn't accessible.
Financial terminology is domain-specific and inconsistent. "Credit exposure," "counterparty risk," and "CVA" can refer to the same concept depending on the desk. Keyword search couldn't bridge those gaps — semantic understanding was required.
Opportunity
Replace keyword-based search with NLP-powered semantic search that understands financial terminology, synonyms, and intent — making the platform's full depth of data accessible to all 5,000+ users, not just those who knew the exact naming conventions.
Design Decisions
Semantic search over keyword matching
The fundamental decision was to invest in NLP-based embeddings rather than improving the keyword indexing. Keyword improvements had a ceiling — they couldn't handle synonyms, context, or intent. Semantic search could. The tradeoff was higher implementation complexity and a need for domain-adapted embeddings for financial terminology.
Domain adaptation for financial language
Off-the-shelf NLP models performed poorly on financial terminology. We invested in fine-tuning and domain adaptation using internal query logs and document corpora — a step that was easy to skip but proved critical for relevance quality.
Incremental rollout with engagement measurement
Rolled out to a subset of users first with explicit engagement tracking to validate that semantic search produced better outcomes — not just better-sounding results. This evidence-based approach built internal confidence before full deployment.
Trade-offs
What we gained
- 25% increase in platform engagement
- Accessible to new users without deep naming knowledge
- Handles synonyms, acronyms, and cross-desk terminology
- Reusable NLP infrastructure for future features
What we gave up
- Higher implementation complexity vs. keyword improvements
- Ongoing model maintenance and retraining
- Domain adaptation effort upfront
Opportunity Cost Evaluation
Improving keyword search would have been faster and cheaper — but it would have hit a ceiling quickly. The platform's value was in its depth of data; if users couldn't find that data, the depth was irrelevant. Semantic search was the only investment that addressed the root cause rather than patching symptoms.
Success Metrics
- Increased analytics platform engagement by 25% across 5,000+ users
- Reduced search abandonment and workaround behavior
- Improved new-user time-to-first-useful-result
What's Next
- Extend semantic search to cross-platform data discovery
- Add conversational query support using LLMs
- Use search logs as signal for platform improvement priorities