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HOW DOES AMAZON BEDROCK HELP DETECT DOCUMENT FRAUD IN UNDER 90 SECONDS?
In finance and fintech, verifying bank statements, pay stubs, tax returns, or identification documents is becoming increasingly difficult as Generative AI can create highly realistic fake documents.
To address this challenge, Inscribe integrated Amazon Bedrock into its fraud analysis system, combining the reasoning capabilities of Foundation Models with specialized document processing and Machine Learning technologies. As a result, the system can detect anomalies in financial documents in under 90 seconds.
Why is Amazon Bedrock used for document fraud detection?
Traditional OCR tools can read text and numbers, but they fall short when assessing whether the data within a document actually makes sense.
With Amazon Bedrock, Inscribe can leverage the contextual understanding and reasoning capabilities of Foundation Models to examine relationships across multiple data fields, detect contradictions, and analyze subtle anomalies that simple cross-checking might miss.
How does Amazon Bedrock support Inscribe in detecting fraud?
Rather than merely inspecting the superficial appearance of a document, Inscribe uses Amazon Bedrock to dive deeper into content analysis, cross-field data relationships, and overall profile consistency. This is a crucial differentiator compared to methods relying solely on OCR or fixed rules.
Specifically, Amazon Bedrock supports the system across several key analysis layers:
- Checking consistency between balances and transactions: AI can cross-reference opening balances, transaction activity, and closing balances to flag illogical figures. For instance, if total monthly transactions do not add up to the declared ending balance, the system marks it for further review.
- Cross-referencing data across multiple documents in a profile: A standalone document may look entirely legitimate, but indicators of fraud often only surface when comparing multiple sources. Amazon Bedrock helps analyze connections between bank statements, pay stubs, tax filings, or business registrations to catch discrepancies in names, addresses, income, transaction amounts, or dates.
- Detecting contextual contradictions: The system goes beyond finding formatting errors to evaluate whether document information is plausible in a real-world context. For example, a high declared income that is inconsistent with actual cash flow in the account, or business information on one document failing to match details on another.
- Aggregating multiple signals into a unified risk assessment: Instead of raising isolated alerts, Amazon Bedrock synthesizes anomalies across multiple analysis stages into a clear, unified output. This allows underwriters to quickly identify which applications need deeper inspection and understand exactly why they were flagged.
Crucially, Amazon Bedrock not only helps the system answer “is this document suspicious?” but also provides explanations for “where the suspicious points are and why.”
Thanks to multi-source contextual understanding and reasoning, Inscribe moves from basic document verification to a holistic risk analysis workflow. For financial institutions processing vast volumes of applications daily, this drastically reduces manual review times while empowering specialists to focus on high-risk cases.
Amazon Bedrock Architecture in Inscribe’s System
The processing workflow can be visualized in four main steps:
1. Document Ingestion
PDFs or images are uploaded and stored in Amazon S3 to initiate the analysis pipeline.
2. Data Extraction
Textual content, data fields, and structural layouts are extracted and converted into actionable data.
3. Analysis via Amazon Bedrock
Foundation Models on Amazon Bedrock are leveraged for tasks requiring contextual comprehension, cross-document verification, and cross-source anomaly detection.
4. Combining Specialized Machine Learning
Alongside Generative AI, Inscribe employs Machine Learning models to detect specific signals such as image tampering, layout anomalies, or known fraud patterns.
This hybrid architecture ensures the system does not rely solely on a single AI model.
The Value Amazon Bedrock Brings to Document Review
One of the most immediate benefits is processing speed.
Instead of spending around 30 minutes manually reviewing a file, Inscribe’s system can identify suspicious documents in under 90 seconds.
More importantly, Amazon Bedrock adds a layer of contextual analysis to the review process. This is especially valuable when businesses must process high document volumes and deal with increasingly sophisticated fraud schemes that static rules cannot catch.
Real-World Results: AI Drastically Reduces Fraud Review Time
| Customer | Fraud Losses Prevented | Review Time Reduction | Key Outcome |
|---|---|---|---|
| BHG Financial | Millions prevented | 90%+ reduction | Systematic workflow that grows with you |
| Logix Federal Credit Union | $3M+ in 8 months | Up to 99% reduction | AI forgery detection |
| BCU | $5.6M prevented | Handles volume from 10 to 10,000 applications | Fraud ring detection |
The impact of this architecture is clearly demonstrated across organizations using Inscribe.
BHG Financial reported a over 90% reduction in manual review time, while preventing millions of dollars in potential fraud losses.
Logix Federal Credit Union prevented over $3 million in potential loan fraud within eight months, reducing review times by up to 99%.
Meanwhile, BCU shared that the system helped avert approximately $5.6 million in losses while uncovering fraud networks that prior manual reviews failed to detect.
These outcomes demonstrate that the true value of AI goes beyond doing things “faster”—it lies in scaling risk management operations without requiring a proportional increase in headcount.
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Conclusion
Inscribe’s implementation of Amazon Bedrock demonstrates how Generative AI can be integrated into business workflows far more complex than basic chatbots or text summarization. By combining Amazon Bedrock with OCR, Machine Learning, and AWS infrastructure, the system effectively analyzes documents, detects contradictions, and helps specialists evaluate risk much faster. This serves as a practical example of how Amazon Bedrock can become a critical AI layer in enterprise unstructured data processing and risk governance.