What is Fraud Check AI and how does it work?
Fraud Check AI is an AI-powered tool that detects file manipulation and fraudulent document creation. It analyses file structures with advanced algorithms to spot potential fraud.
It also checks and compares data to find errors, discrepancies, and inconsistencies, whether within a single document or across multiple datasets. These checks can also include verifying a document’s contents against external data sources to confirm authenticity.
What types of documents or files can Fraud Check AI analyse?
The system supports many formats such as PDFs, structured documents (e.g. invoices, pay slips), JSON datasets, and more. For image uploads, it flags them for manual review since certain metadata checks aren’t always possible.
Can I define my own fraud rules or thresholds?
Yes. You can create custom fraud detection rules tailored to your business logic and risk thresholds. These rules operate on top of built-in static and dynamic checks for deeper, flexible fraud detection.
What is the fraud detection accuracy or error rate?
In internal testing, our checks on valid documents achieve 98–99% accuracy relative to what can be reliably extracted, though actual accuracy depends on file quality and extractability.
How fast are fraud checks performed?
Checks are generally completed in seconds for standard documents. For more complex sets (50+ pages or multiple files), parallel processing ensures timely results.
What static and dynamic checks does Fraud Check AI perform?
Static checks include metadata analysis (creation date, modification, producer name), layout/profile checks, and structure consistency. Dynamic checks include cross-validation with external databases (ABN, BSP, company registry), math logical consistency, and geographic/time anomalies.
Does Fraud Check AI integrate with external systems or APIs?
Yes. It can be integrated via API or within your workflows. The product is designed to plug-in to existing systems, enabling document verification automation without requiring a full rework of your tech stack.
How does the system handle image-based document uploads?
Uploads in image formats (e.g. JPEG, PNG) are flagged for manual review when certain checks aren’t possible (e.g. metadata or producer info). Whenever possible, we run image-based fraud detection and anomaly checks.
How does Fraud Check AI reduce manual reviews?
By automating verification, anomaly detection, and cross-database validation, the system pre-flags documents with risk scores and highlights discrepancies. This means your compliance or review teams only focus on flagged items.
Is the system continuously learning or does it require retraining?
Yes. Fraud Check AI supports continuous learning: over time, it adapts to new fraud patterns, refines rule weights, and updates models to reduce false positives and maintain high accuracy.
Can it detect document tampering across multiple related files?
Yes. It can compare related documents or datasets, spotting inconsistencies across them (e.g. a pay slip vs bank statement) and flag inter-document mismatch or manipulation.
How do you validate against external data sources and what databases are supported?
We connect to trusted government/business registries (e.g. ABN, BSP, company lookup) and verify authenticity of identifiers or claims. These cross-checks strengthen fraud detection and content verification.
What compliance or regulatory concerns are addressed?
Fraud Check AI helps meet KYC, AML, and document verification requirements by automating authenticity checks, preserving audit trails, and ensuring verified document integrity.
What happens after a document is flagged as fraudulent or suspicious?
You receive a detailed report showing which checks failed or triggered flags, the fraud risk score, and sub-explanations to make decisions or escalate for manual review.
How scalable is Fraud Check AI for large document volumes?
The solution is built for scale. It uses parallel processing, asynchronous checks, and optimised pipelines so it can handle bursts of high volume or batch processing needs.
