Normalized claim
Time: 30% decrease
Reduced claim resolution time by 30%, significantly improving customer experience scores.
Infosys Customer Intelligence Platform Delivers World-Class Customer Experience with AWS
Use case typeFraud detectionUpdated Jun 13, 2026
Infosys implemented a Customer Intelligence Platform (CIP) leveraging AWS cloud-native services to enhance customer service automation and claims processing in the auto insurance industry. The platform integrates real-time data ingestion, knowledge graph building, fraud detection, as well as ML-driven hyper-personalized recommendations using Amazon SageMaker, Amazon Fraud Detector, Amazon Neptune, AWS Glue, Amazon Kinesis, Amazon S3, and Amazon Redshift. This solution automates claims processes, detects fraud, delivers hyper-personalized customer insights, and improves customer experience by reducing resolution times and increasing campaign effectiveness.
Reported outcomes
Time: −30%
Time & speed
Catalog median for time & speed deployments: −50% across 295 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Infosys
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Partner Network Blog
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Customer Intelligence
- 2Fraud Detection
- 3Claims Processing
- Built a robust data ingestion and processing pipeline using Amazon Kinesis, AWS Glue, Amazon S3, and Amazon Redshift.
- Developed machine learning models with Amazon SageMaker to drive automated damage detection, policy recommendations, and fraud detection via Amazon Fraud Detector.
- Implemented a knowledge graph using Amazon Neptune to correlate customer and claims data, enabling smarter decisions and personalized experiences.
Architecture
The architecture consists of real-time data streaming with Amazon Kinesis, data processing using AWS Glue and Amazon S3 for storage, and analytics with Amazon Redshift. Machine learning models are trained and deployed using Amazon SageMaker, and fraud detection is handled by Amazon Fraud Detector. A knowledge graph for customer and claims relationships is built with Amazon Neptune.
Sources & evidence1
Evidence: Low35/100Evidence strength
- Customer explicitly identified
- Quantified outcome available
- Technical implementation details available
ExpandedExpanded
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2025.
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: Blog PostPublished: Sep 15, 2022Publisher: AWS Partner Network BlogEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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