Infall Insurtech
By Infall InsurtechInfall Universal Claim System uses LLM-powered NLP, a Decision Engine, and Claim Engine with AI-driven OCR to orchestrate Automation and Agentic workflows, fully automating end-to-end claims, cutting processing time by 50% while reducing fraud and boosting transparency.
- Stage
- In production
- Industries
- Healthcare
- Banking and finance
- Deployment
- Cloud (SaaS)
Why it is worth it
Vietnam-local understanding and knowledge about VN Insurance industry Unique AI-Applications perspectives Big Local Data (claim past data, car damages images, etc...) Develop and fine tune our own models and software (Hybrid LLM-Rule Architecture, Agentic Workflow Layer, Dynamic Fraud Graph, Seamless Legacy Integration)
- Lengthy, manual claims processing
- High error rates and data inconsistencies
- Fraud detection gaps
- Lack of transparency and poor customer experience
- Scalability and peak-load bottlenecks
- Regulatory compliance and auditability challenges
- Siloed data and disjointed systems
- Inefficient resource allocation
Opening the deck…
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Best customer profile
- Ideal Customer Profile for Infall Universal Claim System
- Industry & Segment
- Property & Casualty (P&C) Insurers: Mid-to-large carriers handling auto, home, and commercial lines—especially those facing high claim volumes and seeking automation (e.g., PVI).
- Health & Micro-Insurers: Organizations offering individual or micro-insurance products that require rapid, low-cost claims adjudication and digital intake.
- Specialty Lines & Third-Party Administrators (TPAs): Firms managing niche portfolios (e.g., travel, gadget, or agricultural insurance) that need flexible AI-driven workflows for diverse claim types.
- Company Size & Scale
- Annual Premium Volume ≥ $100 million (or equivalent in local currency): Enough scale to justify AI/ML investment and realize ROI through operational savings.
- Monthly Claim Intake ≥ 20,000 records: High-throughput environments where automating even 30–50 percent of claims yields significant time and cost reduction.
- Regional/National Footprint: Companies operating across multiple branches or regions that require centralized, scalable claim-processing infrastructure.
- Use Cases & Pain Points
- End-to-End Claims Automation: Insurers looking to reduce manual data entry and rule-engine bottlenecks—ideally automating first-notice-of-loss (FNOL), document ingestion, fraud scoring, and auto-settlement for low-risk claims.
- Fraud Detection & Risk Scoring: Carriers wanting to augment rule-based checks with AI-powered fraud graphs, anomaly detection, and predictive risk scores to flag suspicious activity in real time.
- Customer Transparency & Faster Payouts: Organizations aiming to improve policyholder satisfaction by offering self-service claim status portals, automated notifications, and next-day payout for approved claims.
- Regulatory Compliance & Reporting: Insurers in highly regulated markets (e.g., Vietnam’s circulars on claim handling) that need audit trails, explainable AI decisions, and built-in rule validation for local compliance.
- Technology & Integration Requirements
- Legacy System Interoperability: Companies with existing core platforms (e.g., PVI’s PIAS or Foxit-based policy engines) requiring seamless API-based integration of Infall’s OCR, LLM, and Decision Engines.
- Data Maturity & Digitization: Insurers that already digitize policy and claim records (PDF/PIC), enabling high-quality OCR and NLP performance.
- AI/ML Infrastructure Readiness: Organizations capable of hosting GPU-accelerated workloads (on-premise or cloud) to support continuous model training, transfer learning, and inference at scale.
- Specific Needs & Buyer Personas
- Chief Claims Officer / Head of Operations: Focused on SLAs, loss ratio improvement, and reducing cycle time from days to hours.
- CIO / CTO: Evaluating AI roadmaps and cloud-native architectures, seeking robust “plug-and-play” microservices (OCR, LLM, Decision Engine) that integrate with existing middleware (Kafka, Airflow).
- Fraud & Risk Management Lead: Interested in real-time analytics, GNN-based fraud networks, and confidence-thresholded agentic workflows to balance automation with manual oversight.
- Digital Transformation Office: Tasked with implementing end-to-end automation (FNOL → payment → feedback loop) to meet strategic goals around customer experience, operational efficiency, and regulatory compliance.
Notable paying customers
- PVI Insurance VNI BIC AAA
2023: Founded Infall Insurtech and launched the first AI-driven micro‐insurance platform in Vietnam. 2024: Achieved annual premium processing of ~120 billion VND on the Infall core engine; integrated services to big corporations in Vietnam. 2025: integrate services into banks, retail stores, etc. Q1 2025: Conceptualized and designed the Infall Universal Claim System, defining the hybrid LLM-Rule architecture and end-to-end automation workflow.