Automation at Scale: 0% to 85%

Challenge

Jumio processed millions of identity verification transactions globally, relying heavily on manual review by human agents. This created bottlenecks in throughput, inconsistencies in quality, and unsustainable cost structures as volume scaled.

Solution

As Senior Team Lead ML, I led a 20-person research organization across three core verticals to systematically automate the verification pipeline:

Data Extraction

Built ML models for automated data extraction from identity documents—passports, driver's licenses, national IDs—across 200+ countries. Combined OCR with deep learning classifiers to handle diverse document formats, orientations, and image quality.

Fraud Detection

Developed computer vision models to detect document tampering, photo substitution, and presentation attacks. Created multi-signal fusion systems that combined visual, textual, and metadata features for robust fraud scoring.

Biometric Verification

Implemented face matching and liveness detection systems to verify that the person presenting the document is the legitimate holder, working across diverse demographics and device conditions.

Results

• Automation increased from 0% to 85% across the pipeline
• Filed 10 patents covering novel approaches to identity verification AI
• Reduced average verification time from minutes to seconds
• Maintained accuracy standards while dramatically increasing throughput

Key Insight

The path from 0% to 85% automation wasn't a single model—it was a system of systems. Each percentage point required understanding where humans still outperformed models, building targeted solutions for those gaps, and designing confidence thresholds that balanced speed with accuracy.