Product & Service Usage Data

• Reveals sudden spikes in transfer-API calls or bulk-payment uploads by customers whose historical product usage was low, signalling new automation.

Details how a business or account holder utilizes specific financial products and services, including usage frequency and transaction methods. This helps identify investments made predominantly in cash or through opaque channels that deviate from sector norms.

• Consolidates customer usage patterns across financial products and services.
• Highlights sudden or unusual increases in collectible auction activity by customers not previously engaged in such trading.
• Detects abrupt shifts in usage volumes that may signify layering or integration of illicit funds under the guise of collectible transactions.

• Details how accounts utilize financial products and services, including usage frequency, transaction volumes, and service types.

By comparing typical diplomatic mission usage with actual activities, investigators can detect anomalies, such as personal expenditures or sudden spikes in service usage, pointing to potential misuse of diplomatic privileges.

• Tracks the lifecycle of insurance products, including policy inception, premium schedules, and early surrender events.
• Identifies patterns of frequent or early surrenders, highlighting potentially illicit layering or integration through seemingly legitimate insurance payouts.

Aggregates detailed metrics on how financial products are actually utilized, including usage frequency, transaction volumes, and product feature engagement. For overfunding schemes, it reveals customers who show minimal legitimate interest and instead focus on rapid, high-volume deposits and early withdrawals, indicating potential laundering.

• Provides insights into how customers utilize specific financial products and services, including frequency, volumes, and product types.
• Reveals patterns where foreign exchange or hedging products are deployed gratuitously or at odds with a company’s stated operational needs.
• Aids in flagging customers whose FX usage deviates significantly from their usual business profile, indicating potential manipulation.

• Consolidates information on a customer's typical usage patterns and transaction volumes across financial products.
• Helps identify anomalous spikes in product usage (e.g., sudden or repeated purchases of expensive goods) that may indicate laundering via high-value asset transactions.

• Monitors multiple insurance product purchases under a single customer account and subsequent early redemptions or cancellations.
• Identifies unusual usage timelines, such as policy cancellations soon after purchase, which are inconsistent with normal policy life cycles.
• Tracks changes in beneficiary details shortly before disbursement requests, potentially indicating layering attempts.
• Highlights customer willingness to incur financial penalties or fees for quick withdrawals, suggesting laundering through policy surrenders.
• Reveals cross-institution or historical patterns of repeated purchase-and-redeem cycles, indicating systematic money laundering behavior.
• Captures customer focus on early redemption terms, signaling potential misuse of flexible insurance products.

• Shows how insurance policies are utilized, including the frequency of claims, early cancellations, or unusual usage patterns.
• Reveals abnormal usage such as short policy durations, large refunds, or partial surrenders, indicating potential manipulation.

Tracks historical usage patterns of loyalty programs, including normal accumulation and redemption behaviors, enabling the detection of sudden changes in point usage volume or frequency. This helps uncover abrupt increases or rapid redemptions commonly associated with the layering of illicit funds through loyalty schemes.

• Monitors changes in an entity’s product or service lines to newly introduce offshore gambling.
• Tracks usage frequency and transaction volumes tied to gambling services, detecting anomalies or sudden spikes.

These insights confirm the legitimacy of declared gambling operations and flag abrupt shifts in business focus indicative of potential money laundering schemes.

Tracks customer usage patterns across financial products, including remote deposit capture, detailing sudden shifts in deposit channel preferences or high-value check deposits. Such data aids AML detection by identifying anomalous RDC usage and deviations from expected customer behavior.

• Documents the normal range and frequency of product or service usage (e.g., transaction size, refund rates) under typical merchant conditions.
• Highlights anomalies such as unusual spikes in refund or credit activity and repetitive small incoming payments without legitimate rationale.
• Enables investigators to spot funneling or structuring tactics designed to commingle illicit proceeds with normal merchant transactions.

Shows how accounts engage with the gaming platform (e.g., login frequency, game progression, social interactions). Comparing normal user behavior against accounts focused on frequent digital asset trades without genuine gameplay helps detect laundering in virtual worlds.