Foundation Models for eKYC¶
Definition¶
Foundation models are large-scale pre-trained models (CLIP, DINOv2, SAM, GPT-4V) that provide general-purpose representations applicable across eKYC tasks — sometimes with zero or few-shot capability.
Relevant Foundation Models¶
| Model | Type | eKYC Application |
|---|---|---|
| CLIP | Vision-language | Zero-shot document classification, text-image matching |
| DINOv2 | Self-supervised vision | General feature backbone for liveness, recognition, forensics |
| SAM | Segmentation | Document segmentation, face segmentation |
| GPT-4V / Claude Vision | Multimodal LLM | Document understanding, quality assessment, anomaly description |
| FLIP-MCL | Face anti-spoofing specific | Domain-generalized liveness using CLIP features |
Foundation Model Approaches¶
| Approach | How It Works |
|---|---|
| Feature extraction | Use frozen foundation model as feature backbone + train task-specific head |
| Fine-tuning | Adapt foundation model to eKYC task with LoRA/adapter layers |
| Zero-shot | Use foundation model directly without task-specific training |
| Prompt engineering | For LLMs: describe the task in natural language |
Key Takeaways¶
Summary
- Foundation models are changing eKYC ML — pre-trained representations reduce task-specific data needs
- DINOv2 features + task-specific head is a strong baseline for any eKYC vision task
- CLIP-based liveness (FLIP-MCL) achieves state-of-the-art cross-dataset generalization
- Multimodal LLMs can understand documents holistically — emerging application for complex document analysis
- Foundation models are too large for mobile — use for server-side processing or distill to mobile models