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HOW AMAZON BEDROCK HELPS DETECT DOCUMENT FRAUD IN 90 SECONDS
In finance and fintech, verifying bank statements, payslips, tax returns, or identity documents is becoming increasingly difficult as Generative AI can create highly realistic fake documents.
To solve this problem, 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 are not enough to evaluate whether the data in a document actually makes sense.
With Amazon Bedrock, Inscribe can leverage the contextual understanding and reasoning capabilities of Foundation Models to check relationships across multiple data fields, detect contradictions, and analyze anomalies that simple cross-checking might miss.
How does Amazon Bedrock help Inscribe detect fraud?
Instead of merely checking the visual appearance of a document, Inscribe uses Amazon Bedrock to analyze content more deeply, examining relationships between data fields and the consistency of the entire file. This is a key differentiator compared to methods relying solely on OCR or fixed rules.
Specifically, Amazon Bedrock supports the system across several main analysis layers:
- Check consistency between balances and transactions: AI can reconcile opening balances, incurring transactions (income/expenses), and closing balances to detect implausible numbers. For example, if total monthly transactions cannot produce the declared closing balance, the system flags this as a signal requiring further review.
- Cross-reference data across multiple documents in the same file: An individual document may look completely valid, but signs of fraud often only appear when comparing multiple sources. Amazon Bedrock helps analyze relationships across bank statements, payslips, tax declarations, or business information to uncover discrepancies in names, addresses, income, amounts, or transaction dates.
- Detect contextual contradictions: The system does not just look for formatting errors; it evaluates whether the information in the document makes sense in a real-world context. For instance, a high declared income that does not correspond to actual cash flow in the account, or business information on one document that contradicts data on another.
- Synthesize multiple signals into risk assessments: Instead of issuing isolated alerts, Amazon Bedrock can synthesize anomalies across multiple analytical steps into an easily understandable result. This helps risk underwriters quickly identify which files need deeper inspection and why.
Importantly, Amazon Bedrock helps the system not only answer “is this document suspicious?” but also explain “where the suspicious point is and why.”
Thanks to its ability to understand context and reason across multiple data sources, Inscribe can transition from a simple document verification model to a more comprehensive risk analysis workflow. For financial organizations processing large volumes of applications daily, this capability drastically reduces manual review times while allowing specialists to focus on cases with genuinely high risk.
Amazon Bedrock Architecture in Inscribe’s System
The processing flow can be conceptualized into four main steps:
1. Document Ingestion
PDFs or images are uploaded and stored on Amazon S3 to initiate the analysis process.
2. Data Extraction
Text content, data fields, and document structure are extracted and transformed into actionable data.
3. Analysis via Amazon Bedrock
Foundation Models on Amazon Bedrock are utilized for tasks requiring contextual understanding, cross-referencing, and anomaly detection across multiple documents.
4. Integration with Specialized Machine Learning
Alongside Generative AI, Inscribe continues to leverage Machine Learning models to detect signals such as image tampering, layout anomalies, or known fraud patterns.
This hybrid approach ensures the system is not entirely dependent on a single AI model.
Value Delivered by Amazon Bedrock to Document Verification
One of the most obvious advantages is processing speed.
Instead of spending nearly 30 minutes manually reviewing a single file, Inscribe’s system can flag suspicious documents in under 90 seconds.
More importantly, Amazon Bedrock adds a layer of contextual analysis to the review process. This is particularly valuable when businesses handle massive volumes of documents and fraud techniques become harder to spot using fixed rules.
Real-world Results: AI Drastically Reduces Fraud Review Times
| 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 effectiveness of this architecture is demonstrated through organizations using Inscribe.
BHG Financial recorded over a 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 and reduced review times by up to 99%.
Meanwhile, BCU reported preventing approximately $5.6 million in losses and uncovered fraud rings that previous manual reviews failed to identify.
These results show that AI’s value isn’t just about “working faster,” but also about scaling risk management operations without needing to scale headcount at the same rate as application volume.
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Conclusion
Inscribe’s use of Amazon Bedrock demonstrates that Generative AI can be embedded into business workflows far more complex than simple chatbots or text summarization. By combining Amazon Bedrock with OCR, Machine Learning, and AWS infrastructure, the system can analyze documents, detect contradictions, and assist risk assessors much faster. This serves as a practical example of how Amazon Bedrock can become a vital AI layer in enterprise unstructured data processing and risk management systems.