AI document fraud detection

Document fraud detection refers to the automated identification of falsified, altered, or forged documents, invoices, payslips, bank statements, contracts, or ID documents, submitted to an organisation. As document volumes grow, manual review becomes impractical, making automated fraud detection an essential layer of financial and operational control.

A document fraud detection tool analyses incoming documents to flag signs of tampering or inconsistency. Unlike manual checks, it processes high volumes in real time, combining business rules, reference data, and analytical models to surface suspicious documents before they generate financial exposure.

Most fraud detection tools rely on pattern matching alone. Koncile takes a contextual approach: for each document type, the platform develops specific models alongside domain experts (accountants, legal specialists, operations teams), who encode the rules and logic relevant to each context, enabling coherence checks the way a human expert would. Rather than simply flagging PDF alterations or abnormal patterns, it evaluates whether a document's data is reliable as a whole, cross-referencing figures against each other and against sector-specific rules.

Fake payslips are among the most commonly falsified documents, particularly in credit or tenancy applications. Koncile applies two levels of control: metadata analysis, where alterations typically leave clear forensic traces, and amount coherence checks, gross pay, net pay, social contributions, built on a deep knowledge of payroll regulations, designed with domain experts to be immediately operational.

The most targeted documents are supplier invoices, payslips, bank statements, contracts, and administrative certificates. Document fraud affects every sector, but is particularly prevalent in finance, HR, and procurement, anywhere high-value decisions rely on document-based evidence.

Two integration modes are available. Via the platform, documents are uploaded directly and Koncile returns a reliability score for each one, with automatic alerts triggered on high-risk files. Via API, documents are sent to Koncile's endpoint and the score and alerts are returned in real time, for seamless integration into your existing workflows.

Effective detection relies on two complementary levels: file metadata analysis, which reveals traces of modification, and semantic verification of the document's content, which catches internal inconsistencies even when the document appears intact on the surface. It is also essential to define rules specific to each document type — on a payslip, for instance, the relationships between gross pay, net pay, and contributions follow precise rules that the tool must understand. That is why Koncile builds its models with domain experts, ensuring controls that are reliable and operational from day one. Finally, detection must cover the entire incoming document flow, not just the documents that appear suspicious at first glance.

Implementation starts with choosing the integration mode: direct access via the platform, or an API connection to plug Koncile into the organization's existing systems (ERP, HRIS, credit portal…). This integration is compatible with automation tools such as Zapier, n8n, or Power Automate. The next step is configuring the control rules specific to each document type, which is done in plain language, with no custom development required. At Koncile, this configuration is carried out with the relevant domain experts — finance, HR, and compliance — to ensure the models are reliable and fully operational from the very first day.