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Case Study

RiskCorrect

How RiskCorrect Built a Secure Foundation for AI-Powered Claims Processing

The Client

RiskCorrect is a data-driven workers’ compensation and claims management platform that helps employers reduce administrative complexity, improve claim oversight, and make more informed decisions across the claim lifecycle.

By centralizing claim data, documentation, and operational workflows into a more connected system, RiskCorrect helps organizations reduce costs, improve consistency, and create better outcomes for both employers and employees.

 

 

Risk Correct logo
Business-Execution

Summary

TYPE OF PRODUCT

AI-Powered Claims Management Platform

BUSINESS VERTICAL

Insurance, Risk Management & Workers’ Compensation

TECH USED

Amazon Macie, Amazon GuardDuty, Amazon S3, AWS IAM, AWS KMS, Amazon ECS Fargate

SERVICE
Full-service Development Team—

Solution Architecture, Delivery Management, AI/Data Engineering, DevOps Engineering, Quality Assurance

DESCRIPTION

RevStar partnered with RiskCorrect to strengthen the security posture of its AI-powered claims assessment platform by implementing automated sensitive-data discovery, Amazon GuardDuty EBS Malware Protection, and a SAST gate within the CI/CD pipeline for workloads processing regulated claimant information.

RESULTS

Enabled scalable AI-powered claims processing workflows that reduced manual summary effort, improved reporting consistency, and supported measurable cost-saving opportunities for RiskCorrect and its clients.

We’ve reduced about 20 hours of labor a week while significantly improving the accuracy of our data. For our clients, we’re seeing realistic 15–20% decreases in premiums. You’re talking about millions of dollars in savings.
Tobin Robeck CEO, RiskCorrect

The Challenge

As RiskCorrect expanded its AI-powered claims assessment platform, protecting sensitive claimant information became increasingly critical. 

After enabling automated sensitive-data discovery with Amazon Macie, RiskCorrect identified a live PII exposure within minutes of enabling automated sensitive-data discovery, reinforcing the need for stronger controls around regulated claimant data.

Processing large volumes of unstructured claims documents containing personally identifiable information (PII) required stronger visibility into sensitive data, automated threat detection, and security controls capable of protecting regulated workloads while supporting continued platform growth.

The Problem

RiskCorrect needed a scalable and proactive approach to protecting sensitive claimant data while supporting its AI-powered claims assessment platform. The organization sought to strengthen data security, automate the identification of regulated information, and reduce manual security oversight without disrupting existing claims workflows.

The Solution

RevStar acted as RiskCorrect's strategic cloud partner, designing a secure, AWS-native foundation that protects sensitive claimant information while supporting the organization's AI-powered claims assessment platform. Working as an extension of the client's team, RevStar embedded intelligent security controls throughout the platform, including Amazon Macie for automated sensitive-data discovery, Amazon GuardDuty EBS Malware Protection for workload threat detection, and secure development practices across the delivery pipeline.

Built on scalable cloud-native architecture, the solution establishes a resilient foundation for secure AI-driven claims processing while enabling RiskCorrect to confidently scale its platform, protect regulated information, and support future innovation.

The Team

We have a growing cost-effective hybrid team, with onshore Product Managers and developers from our build center in Colombia. Operating three parallel teams for different products and support.
Two people collaborating at a whiteboard covered with sketches and sticky notes, planning a project layout.

Results

RevStar helped RiskCorrect strengthen the security and resilience of its AI-powered claims assessment platform while supporting continued innovation. Automated sensitive-data scanning can detect exposed sensitive data within 5–7 minutes of activation, giving the team near real-time visibility into data risk across the platform. All 11 of RiskCorrect’s Amazon S3 buckets are encrypted, none are publicly accessible, and scanning runs continuously rather than as a one-time assessment.

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