What challenges did Inscribe face before AI implementation?

Fraud and AI-generated forgeries in loan applications; manual review took around 30 minutes per application and missed deepfakes and coordinated fraud rings. Financial institutions needed faster document verification without losing explainability, accuracy, or compliance.

What was the impact of the AI solution?

Detects tampered, fabricated, and AI-generated financial documents in under 90 seconds. About 20x improvement over manual review. Reported customer outcomes include $3M+ prevented fraud losses in 8 months and more than 90% reduction in manual review time.

Source

Inscribe uses Amazon Bedrock agentic AI + SageMaker to detect document fraud in under 90 seconds

Use case typeFraud detectionUpdated Jul 1, 2026

Inscribe built an agentic AI system for financial document fraud detection that reasons across documents, runs parallel forensic checks, searches the web for verification, and produces audit-ready fraud reports. The solution uses Amazon Bedrock for model selection and orchestration and Amazon SageMaker AI for proprietary fraud models, alongside AWS infrastructure services for ingestion, scaling, storage, and observability.

Organization: Inscribe
Industry: Finance
Location: United States
Published: July 2026

Reported outcomes

20x

manual review speedupAutomation & deflection

90 secondsdocument fraud detection time3,000,000 USDpotential fraud losses prevented−90%manual review time reduction

Strategic outcomes

Risk & complianceImproved explainability and audit readinessScale & capacityScales with application volume without proportional headcount growthOther strategic outcomeReduced manual handoff and analyst workload

Primary read

Use case focus

Showing 3 of 3

1Fraud detection
2Document processing automation
3Agent orchestration

Fraud and AI-generated forgeries in loan applications; manual review took around 30 minutes per application and missed deepfakes and coordinated fraud rings.
Financial institutions needed faster document verification without losing explainability, accuracy, or compliance.

Built a multi-model agentic AI workflow on Amazon Bedrock that parses documents, coordinates specialized models, performs cross-document reasoning, and integrates external verification steps.
Hosted proprietary forensic ML models on Amazon SageMaker AI to detect pixel-level manipulation, template matches, and visual anomalies.
Used AWS infrastructure such as S3, SQS, EC2, RDS, ElastiCache for Valkey, and observability to support secure, scalable processing.

Technologies

Amazon Bedrock Amazon SageMaker AI Amazon S3 Amazon SQS Amazon EC2 Amazon ElastiCache for Valkey Amazon RDS

Detects tampered, fabricated, and AI-generated financial documents in under 90 seconds.
About 20x improvement over manual review.
Reported customer outcomes include $3M+ prevented fraud losses in 8 months and more than 90% reduction in manual review time.

Architecture

Documents are ingested to Amazon S3 and queued in Amazon SQS, processed by Celery workers on Amazon EC2, parsed by multiple foundation models on Amazon Bedrock, and evaluated in parallel by proprietary fraud models hosted on Amazon SageMaker AI. Results and reasoning traces are stored in Amazon RDS, with Amazon ElastiCache for Valkey handling short-lived data and observability tracking latency, errors, tokens, and request cost.

Sources & evidence1

Groundedness: 5/5Type: Blog PostPublished: Jul 1, 2026Publisher: AWSEvidence: VendorConfidence: Medium

Primary source

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