AI-Powered Insurance Claims Automation (InsurTech)
Client
A health insurance in Thailand offering both group and individual plans, managing 1M+ customers.
Business Challenge
- Claims processing time was 5–8 days on average.
- High volume of fraudulent or exaggerated medical claims.
- Manual document validation created bottlenecks in operations.
Objectives
- Reduce claim processing time to under 24 hours.
- Use AI/ML to detect anomalies and prevent fraud.
- Digitize and automate medical document intake and validation.
Solution by Tech4Biz
1. AI-Driven Document Extraction & Validation
- Built an OCR + NLP pipeline using Tesseract + BERT.
- Parsed and verified documents (hospital bills, discharge summaries, lab reports).
- Trained models to identify suspicious billing patterns and duplicate claims.
2. Claims Scoring Engine
- Developed a fraud scoring algorithm using historical claims and external datasets.
- Risk scores fed into a workflow engine to auto-approve, reject, or escalate claims.
3. Patient & Hospital Behavior Analytics
- Built anomaly graphs using Neo4j to detect fraudulent clusters.
- Deployed models that learned from hospital treatment cost deviations and flagged outliers.
Results & Impact
| KPI | Before | After | Impact |
|---|---|---|---|
| Average Claim Settlement Time | 5–8 days | 18 hours | -75% |
| Fraudulent Claims Caught | ~3% | 11.2% | +274% |
| Manual Review Workload | 100% | < 20% | 80% Automated |
| Customer Satisfaction (NPS) | 52 | 82 | Major jump |
Tools & Stack
- ML Models: XGBoost, BERT, PyTorch-based CNNs for document image matching
- Workflow: n8n + Airflow
- Infra: AWS (SageMaker, Textract, DynamoDB)
- Data Privacy: DLP tools, HIPAA-ready compliance pipelines
Key Differentiators
- End-to-end AI workflow from document ingestion to payout decision.
- Dynamic scoring based on both structured and unstructured data.
- Plug-in system for adding new insurance verticals (life, motor, etc.).