We Are Developing an AI Solution to Address Fraudulent Invoice Risk

At X Mind Solutions, we are developing an AI-powered solution to help businesses identify fraudulent invoice risks earlier. Businesses’ requests for protection and control have highlighted the need for early warnings in this area. We do not yet have a product ready for use; our development efforts focus on assessing invoice data alongside other records of the commercial transaction to flag situations that require review.

KURGAN, the AI-based risk analysis platform introduced by the Ministry of Treasury and Finance to combat fraudulent invoices and documents, highlights the importance of recordkeeping and supporting evidence for businesses. Preparing to explain potentially risky transactions requires more than simply keeping the invoice. Being able to demonstrate, together, that the goods or services were actually received, how payment was made, and which documents support the business relationship is fundamental to an internal control approach.

We therefore recommend that businesses first keep their delivery and payment evidence well organised. Dispatch notes, shipping or logistics records, and bank payment receipts should be considered together to explain the transaction underlying an invoice. Choosing traceable payment methods over cash and maintaining a contract or written quotation for each engagement help ensure complete documentation. Creating an evidence folder linked to each invoice can also make preparations for providing explanations more systematic.

The other part of the control process involves verifying counterparties and documents. The supplier’s or customer’s MERSİS records, business activity code, and address details should be reviewed; UUIDs, dates, and tax identification numbers should be checked for consistency in e-Fatura and e-Arşiv documents. Knowing who the manufacturer or importer is within the purchasing chain also supports the assessment. Unusual amounts, discounts, and payment terms can be monitored using internal alert thresholds to trigger review.

In the solution we are developing, we aim to assess supplier and customer profiles, transaction patterns, invoice line items, and product details alongside logistics and payment trails. Automatic risk scoring based on these data is being designed to help prioritise transactions requiring review. The purpose is not to make a definitive determination about an invoice’s legal status, but to create an early warning mechanism that prompts businesses to check the relevant records.

We are open to shaping the solution together with pilot users. Businesses wishing to discuss their invoice control processes and document collection needs can contact X Mind Solutions. As we are still in the development phase, we are not offering an existing product, a completed implementation, or verified results demonstrating success. Our priority is to develop an approach that supports businesses’ checks based on records, evidence, and transparency, brings risk signals to human reviewers, and addresses user needs through pilot participation.