Fraud Detection and Security AI-AI-driven Fraud Detection

Example

Identifying unusual transaction patterns, such as a sudden increase in transaction volume or size, which could indicate money laundering or fraudulent activities.

Scenario

In the context of trade finance, anomaly detection algorithms analyze historical transaction data to establish a baseline of normal activity. Any deviation from this baseline is flagged for further investigation. For instance, if a company that typically makes small, regular purchases suddenly initiates a large, one-off transaction, the AI would flag this as a potential anomaly for review.

Example

Evaluating the risk level of transactions based on factors like transaction history, geography, and the entities involved.

Scenario

Before a bank issues a letter of credit, the AI assesses the risk associated with the transaction by considering the countries involved, the track record of the parties, and the nature of the goods being traded. This helps in making informed decisions on whether to proceed with the transaction or apply additional scrutiny.

Example

Automated analysis and verification of trade documents to detect inconsistencies, forgeries, or alterations.

Scenario

AI tools are deployed to scrutinize shipping documents, invoices, and other trade-related paperwork for discrepancies such as mismatched dates, quantities, or product descriptions. This can indicate attempts to over-invoice, under-invoice, or smuggle goods. In one scenario, the AI could flag a shipment where the stated quantity of goods on the invoice does not match the cargo description on the bill of lading.