How Inscribe uses Amazon Bedrock to stop document fraud in seconds

2026-07-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy · Depth: Intermediate, long

Summary

Inscribe, a company specializing in AI-powered document fraud detection, leverages Amazon Bedrock to identify tampered, fabricated, and AI-generated financial documents in under 90 seconds. This represents a 20x improvement over traditional manual review, which takes 30 minutes per application. With fraud appearing in 1 of every 16 documents and AI-generated forgeries growing 5x from April to December 2025, Inscribe's agentic AI system addresses the scale, adaptability, and consistency challenges faced by financial institutions. The multi-model architecture on Bedrock utilizes Anthropic Claude Haiku 4.5 for high-volume parsing and classification, Meta Llama 3.1 70B and Meta Llama 4 for transaction enrichment, and Anthropic Claude Sonnet 4 and 4.5 for complex cross-document analysis and report generation. Proprietary ML models on Amazon SageMaker AI perform pixel-level forensics and network pattern detection. Customers like BHG Financial, Logix Federal Credit Union, and BCU have reported preventing millions in fraud losses and achieving significant review time reductions.

Key takeaway

For AI Architects designing fraud detection systems, consider an agentic AI approach on Amazon Bedrock. This multi-model strategy, combining specialized FMs like Claude Haiku, Meta Llama, and Claude Sonnet with proprietary ML on SageMaker, drastically reduces review times and enhances detection accuracy for sophisticated fraud. You can achieve significant cost savings and scalability while maintaining compliance and adapting to evolving fraud tactics.

Key insights

Agentic AI with a multi-model architecture on Amazon Bedrock significantly enhances document fraud detection speed and accuracy.

Principles

• No single model suits every task.
• Coordinate models for better results, lower cost.
• Serverless scaling handles volume swings efficiently.

Method

Inscribe's agentic AI system breaks down fraud detection into steps, coordinating specialized FMs (Haiku for parsing, Llama for entities, Sonnet for cross-document reasoning) and proprietary ML models, then synthesizing a final decision.

In practice

• Use Claude Haiku 4.5 for high-volume parsing.
• Employ Meta Llama for cost-efficient entity extraction.
• Leverage Claude Sonnet for complex cross-document analysis.

Topics

• Amazon Bedrock
• Agentic AI
• Document Fraud Detection
• Foundation Models
• AWS
• Machine Learning
• Financial Services

Best for: AI Engineer, MLOps Engineer, AI Architect

Related on AIssential

• From hours to minutes: How Agentic AI gave marketers time back for what matters — Agentic AI solutions can drastically cut content publishing times while improving quality and accessibility.
• Enable safe agentic payments with built-in guardrails using Amazon Bedrock AgentCore payments — Amazon Bedrock AgentCore payments enables secure agentic transactions by enforcing infrastructure-level guardrails against financial risks.
• FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection — Agentic LLMs struggle with evidence-grounded SMS-to-webpage fraud detection when URL reputation shortcuts are removed.
• Amazon’s OpenAI gambit signals a new phase in the cloud wars — one where exclusivity no longer applies — AWS's new offerings position it as a comprehensive platform for agentic AI, integrating top models with robust security and specialized applications.
• Securing AI Workloads in AWS: Why Bedrock and SageMaker Need Runtime Detection and AI-Powered Response — AI workloads in AWS are high-value targets requiring integrated security beyond posture management due to AI-speed attacks.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.