Banks had limited visibility beyond their own internal data, resulting in misjudgment of legitimate customers and fragmented risk analysis. Strict privacy and banking secrecy regulations prevented the sharing of transaction histories, which restricted the accuracy of fraud detection models.

Thanks to AIDatacy’s Interbank AML Hub, the system aggregates encrypted transaction amount data from multiple banks and established accumulated value thresholds over predefined time periods to compute an interbank risk score, enabling more precise distinction between genuine and fraudulent activities. It also identifies suspicious transaction chains without exposing any sensitive details, thereby reducing false positives caused by rare but keeping legitimate customer behaviors at individual banks.

Banks could only analyze transactions within their own systems, which limited their ability to detect fraud across institutions. Strict privacy and banking secrecy regulations prohibited any data sharing, creating blind spots in fraud monitoring. However, the system still needed to trigger real-time alerts whenever a transaction appeared suspicious​.

With AIDatacy’s encrypted SQL engine, banks can securely query a centralized, encrypted database operated by the State Bank. Each query, executed using an encrypted IBAN, verifies the existence of a suspicious account without ever exposing sensitive information. The system returns only a boolean result (YES/NO), ensuring real-time interbank fraud detection while maintaining confidentiality, digital sovereignty, and full regulatory compliance.