What challenges did MediConCen face before AI implementation?

OCR-generated text still required manual review before insurance claim decisions could be made. The claims workflow needed higher extraction accuracy, higher straight-through processing, faster turnaround, and strong security controls. The company also wanted to support multilingual claim understanding and future fraud detection.

Source

GCPEvidence: Medium60/100

MediConCen automates medical insurance claim processing with Gemini and Vertex AI

Use case typeFraud detectionUpdated Jun 13, 2026

MediConCen is a Hong Kong-based InsurTech company that automates medical insurance claim processing for insurers and medical institutions. The company identified that OCR alone still left claim decisions dependent on manual review because extracted text needed better understanding and summarization. Using Google Cloud, MediConCen launched an AI-enabled claims processing platform in April 2025 to improve extraction accuracy, speed, security, and multilingual processing.

Organization: MediConCen
Industry: Insurance
Location: Hong Kong
Published: April 2025

Reported outcomes

Accuracy: +98%

Quality & accuracy

Automation: +100%

Catalog median for quality & accuracy deployments: +40% across 55 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Accuracy: 98% increase

Google Cloud Customer StoriesApr 1, 2025Customer storyInferred claimMedium evidence strength

Field-level data extraction accuracy increased to 98%.

Normalized claim

Quantified impact: 100% increase

Google Cloud Customer StoriesApr 1, 2025Customer storyInferred claimMedium evidence strength

Straight-through processing rate increased by 100%.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: MediConCen
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Claims processing automation
2Document understanding
3Fraud detection

MediConCen built an AI manager using agentic AI that breaks claims handling into hundreds of subtasks and orchestrates nearly 100 Gemini instances.
The team used Gemini for medical-content understanding and Google Cloud for enterprise-grade security.
Vertex AI was used to fine-tune the generative AI models for better domain-specific comprehension.
Cloud Run was used to deploy the claims processing system for scalability and CI/CD support.
Cloud Key Management was used with customer-managed encrypted keys to strengthen data protection.
The company plans to use BigQuery for fraud detection based on similarity in claim content.

Technologies

Gemini Vertex AI Cloud Run Cloud Key Management BigQuery

Field-level data extraction accuracy increased to 98%.
Straight-through processing rate increased by 100%.
Fine-tuning typically completed in less than one day.

Architecture

The claims workflow uses an agentic AI manager that decomposes insurance claim handling into hundreds of smaller tasks and orchestrates nearly 100 Gemini instances. The platform runs on Cloud Run, uses Vertex AI for model fine-tuning, and protects data with Cloud Key Management customer-managed encrypted keys. BigQuery is planned for fraud detection.

Sources & evidence2

Evidence: Medium60/100Evidence strength