Test Payment Probing

Criminals use small, exploratory transactions to identify and exploit financial institutions' detection thresholds. This enables them to refine their methods and evade AML monitoring controls when moving larger illicit sums.

Criminals systematically probe an institution’s internal detection thresholds, such as automated AML alert triggers and transaction monitoring rules, by sending multiple low-value transactions. By observing which transactions fail to generate alerts, they can map and exploit weaknesses in the bank’s AML controls, refining subsequent laundering methods to evade detection. This process reveals insufficient or overly simplistic internal controls, highlighting blind spots in governance and monitoring processes.

• Tracks user session information, IP logs, and transaction status changes, highlighting repeated initiation and quick cancellation of small transactions used to gauge thresholds.
• Supports investigation of abnormal login patterns and rapid reversals, suggesting deliberate testing of AML controls.

• Provides detailed records of all financial transactions, including timestamps, amounts, parties, and transaction identifiers.
• Enables detection of patterns where repeated small payments are followed by much larger transfers, indicating threshold probing attempts.
• Supports identification of rapid or consecutive small transactions across multiple accounts or channels, aiding in investigating potential AML evasion strategies.

• Includes transaction details and metadata from e-wallets and fintech platforms, enabling the identification of small-scale probing behaviors across digital channels.
• Helps correlate changes in transaction patterns or account details after initial test payments, indicating criminals' adaptation to detected monitoring rules.

• Contains verified customer profiles, financial backgrounds, and expected account activity.
• Assists in comparing recent small transactions against a customer’s normal financial habits to flag unusual probing attempts.
• Helps investigators correlate suspicious low-value ‘test’ transfers with known legitimate or high-risk customer attributes, refining monitoring thresholds.

Deploy specific scenario-based monitoring rules that flag repeated small test payments from the same or closely related accounts, particularly when quickly followed by larger transfers. This approach ensures any suspicious threshold probing attempts are immediately escalated for further review, preventing criminals from successfully identifying and exploiting detection triggers.

Assign elevated risk ratings to customers whose activity patterns reflect repeated test payments or irregularly timed small transactions, prompting stricter monitoring and reduced threshold tolerances. This ensures that accounts engaging in probing behavior cannot continually exploit standard alert limits.

Perform periodic internal or external evaluations of transaction monitoring thresholds using test transactions or simulated attacks. These exercises help identify blind spots, recalibrate rules, and prevent criminals from reverse-engineering the AML system through incremental probing of detection limits.

Set velocity or frequency caps on low-value transactions for new or high-risk accounts, and require additional screening for repeated micro-transfers. This limits adversaries' ability to probe thresholds by enforcing extra verification when numerous small payments are attempted in quick succession.

• Criminals initiate small deposits or withdrawals to identify the transaction amounts or frequencies that trigger enhanced scrutiny or alerts.
• By monitoring the bank’s response, they learn how to stay under the radar and later aggregate larger sums without triggering the bank’s AML controls.

• Bad actors send minimal crypto amounts to or from exchanges to determine the threshold at which the platform flags a transfer.
• After identifying these triggers, they adjust future transactions to fall just below the alert criteria, exploiting the platform’s monitoring rules.

• Offenders perform numerous low-value card transactions—often online—to monitor for fraud or AML triggers.
• Once they identify which usage patterns remain unflagged, they scale up illicit transfers under those thresholds, evading detection systems.

• Criminals load or transfer small balances across prepaid cards or e-wallets to determine the level at which a provider enforces KYC or alerts.
• They then use the identified limit to conduct larger transactions that remain below detection parameters.

• Offenders leverage multiple micropayments between personal or mule accounts to identify the platform’s AML constraints.
• Through repeated small transactions, they gauge how easily they can evade suspicious activity flags.

• Criminals execute minimal crypto deposits or trades to see whether exchange monitoring systems trigger alerts.
• Once the limits are understood, offenders can structure larger illicit transfers below detection parameters.

• Criminals send frequent, nominal transfers via mobile apps to see if the system flags them.
• Using easily accessible mobile devices and multiple SIM cards, they repeatedly test AML thresholds and adapt accordingly.

• Criminals conduct numerous small-value transactions to gauge the threshold at which automated flags or merchant alerts are triggered.
• Once they learn the threshold or suspicious transaction triggers, they adapt subsequent transactions to remain under detection parameters, facilitating larger-scale laundering activities.

• Offenders send small remittances across various corridors to observe whether they prompt additional due diligence.
• Low-value cross-border transfers help identify each corridor’s threshold or KYC intensity.

• Individuals often deposit or withdraw small sums to determine when financial institutions apply enhanced scrutiny.
• Parallel usage of multiple accounts helps refine knowledge of detection thresholds at different banks.

• Fraudsters execute recurring micro-transactions across different user accounts to see where abnormal usage or fraud detection triggers arise.
• The ability to send payments globally aids iterative testing of alert thresholds.

Illicit operators systematically send small transactions to:

• Identify the precise amounts, frequencies, or patterns that do not trigger an institution’s automated AML alerts.
• Refine subsequent larger-scale laundering techniques based on observed gaps, staying below detection thresholds across multiple accounts or jurisdictions.

Financial institutions are unwittingly exploited in this technique because:

• Their threshold-based monitoring rules are tested with repeated low-value transactions, revealing the points at which alerts are triggered.
• Once criminals learn these limits, they structure larger illicit transfers to remain under the identified thresholds, circumventing the institution’s existing AML controls.