Comparison
EchoDepth vs alternatives
How does FACS-based emotional AI compare to sentiment analysis, traditional QA, and other vulnerability detection approaches? Here's an honest comparison.
Feature comparison
Key differences between vulnerability detection approaches.
| Feature | EchoDepth | Sentiment Analysis | Traditional QA | Self-Declaration |
|---|---|---|---|---|
| Coverage | 100% | 100% | 2-5% | Self-selected |
| Detection method | Physiological signals (FACS, VAD, prosody) | Keyword/phrase matching | Manual review | Customer disclosure |
| Timing | Real-time | Real-time | Post-interaction | At disclosure |
| Detects masked distress | Yes | No | Sometimes | No |
| Modalities | Video, voice, text, image | Text only | Varies | Verbal/written |
| FCA audit trail | Automatic | Partial | Manual | Manual |
| Pre-decision intervention | Yes | Sometimes | No | No |
| Hardware required | Standard camera/mic | None | None | None |
EchoDepth vs Sentiment Analysis
Sentiment analysis reads words. EchoDepth reads people.
A customer saying "I'm fine, let's proceed" while displaying facial stress markers will be classified as neutral or positive by sentiment analysis.
44 facial Action Units, voice prosody, and VAD scoring reveal emotional state regardless of words spoken. Involuntary signals are harder to mask.
EchoDepth vs Traditional QA
QA finds problems after harm occurs. EchoDepth prevents them.
Most vulnerable customer interactions are never reviewed. Issues are discovered through complaints, not proactive detection.
Every interaction is scored. Vulnerability flags appear before decisions are made, enabling intervention rather than remediation.
EchoDepth vs Self-Declaration
Vulnerable customers often don't self-identify.
Many vulnerable customers don't recognise their vulnerability, feel shame about disclosure, or fear it will affect their application outcome.
Physiological markers of distress and cognitive load are detected automatically, regardless of whether the customer chooses to disclose.
EchoDepth vs Generic Emotion AI
Built for regulated financial services, not general purpose.
Consumer emotion AI is designed for marketing, not regulatory compliance. Audit trails, vulnerability tiering, and Consumer Duty evidence are absent.
Purpose-built for UK financial services. Consumer Duty audit trails, vulnerability tiering, GDPR-compliant architecture. ISO 9001 infrastructure.
Why EchoDepth
Key differentiators
FACS-based, not keyword
44 facial Action Units tracked per frame based on the Facial Action Coding System. Involuntary physiological signals that can't be masked or scripted around.
Voice-first for phone
Collections and complaints happen on the phone. EchoDepth's prosody analysis works with audio-only interactions.
FCA Regulatory Sandbox
Tested within the FCA's regulatory innovation programme. Built for UK financial services compliance from the ground up.
No specialist hardware
Works with existing webcams, phone cameras, and audio systems. No wearables, sensors, or capital expenditure.
Pre-decision flags
Vulnerability signals appear before loans are declined, accounts closed, or recovery actions initiated. Intervention, not remediation.
Audit trail automatic
Consumer Duty evidence generated automatically. Timestamped records that vulnerability was assessed before every decision.
See the difference
Book a discovery call to see EchoDepth analyse a sample interaction — and compare the output to your current approach.