Deep Learning for Document Understanding
Processing 2.5 million claims annually across auto, property, and casualty lines with $50B in premiums
78% reduction (14 days → 3 days)
4.5x improvement (15% → 67%)
$89M from fraud prevention
34 NPS points improvement
85% for simple claims
Manual claims processing averaged 14 days with 35% requiring multiple reviews due to documentation errors. Fraud detection relied on rule-based systems catching only 15% of fraudulent claims.
Deployed transformer-based document understanding model (BERT variant), convolutional neural networks for damage assessment from images, and graph neural networks for fraud pattern detection with multi-modal fusion.
Multi-modal deep learning system with transformer-based NLP
Custom vision transformer for 50+ damage types, Semi-supervised learning with 10M unlabeled documents
Hierarchical attention networks for document parsing, Zero-shot learning for new claim types
Redis for low-latency caching (sub-10ms), Apache Airflow for workflow orchestration
Revolutionized claims processing with 78% time reduction, 4.5x fraud detection improvement, and $89M annual savings.
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