30% increase in vulnerability detection accuracy.
Manual QA samples 1% of calls. Traditional statistical models hit 80% accuracy on the 100% they score. MOJO-CX's multi-shot classification lifts that accuracy by 30 percentage points — on every call, every day.
The vulnerability detection arms race in regulated industries has three tiers: manual sampling (1% coverage, high human accuracy but unauditable at scale), statistical models (100% coverage, 80% accuracy), and now multi-shot LLM classification (100% coverage, 80% + 30pp = effectively 100% effective accuracy). The third tier is what closes the Consumer-Duty maths gap.
FCA Consumer Duty applies to 100% of customer interactions. Traditional QA samples 1% of calls. The maths gap was the problem the entire regulated industry was carrying — and the FCA had made clear that sampling alone was no longer a defensible operating model for vulnerability identification.
Existing speech-analytics vulnerability models hit roughly 80% accuracy on the 100% of calls they scored. Good — but the 20% miss rate at scale meant hundreds of vulnerable-customer interactions per week going un-flagged across a typical book.
- Multi-shot LLM classification built into MOJO-CX's Auto-QA pipeline. The same conversation is scored against multiple model prompts, and the consensus is used as the signal — dramatically reducing false negatives on the edge cases that matter most.
- 100% call coverage retained. No regression to sampling, no trade-off between accuracy and scale.
- Customer-bespoke vulnerability taxonomies. The Insurer's risk team defined what "vulnerable" meant in their specific book and product mix; the model trained against it.
- Audit-evidenced for FCA Consumer-Duty inspection. Every flag carries the conversation timestamp, the language patterns that triggered it, the agent action, and the disclosure made (if any).
- +30 percentage points in vulnerability detection accuracy vs statistical-only baseline
- 100% call coverage maintained — 800k+ calls modelled in the deployment period
- Audit-ready Consumer-Duty evidence trail running by default on every interaction
- Hundreds of vulnerable interactions per week moved from "missed" to "flagged and actioned"
- Risk team confidence in the operating model materially improved at the next FCA touchpoint
Manual sampling at 1% and statistical models at 80% accuracy were both leaving vulnerable customers unprotected. Multi-shot classification on 100% closed the gap. It's the difference between "we have a vulnerability policy" and "we can show you we acted on it, on this call from last Tuesday".
Leader AI scores the calls. Analyst AI clusters the misses and tunes the model. Continuous improvement, automated.
Leader AI runs the multi-shot vulnerability classification on every conversation. Analyst AI reads the model's lower-confidence cases, clusters them by topic and pattern, and surfaces the edge cases for risk-team review — generating the next iteration of the classifier without a data-scientist in the loop. The accuracy compounds with every model refresh.
Meet Leader AI and Analyst AI →See your own vulnerability coverage gap through MOJO-CX.
Two-week Analyst AI Auto Discovery on one queue. We benchmark your current detection coverage and show you the gap multi-shot classification closes.