Online & Digital Payment Platform Data

• Provides records from e-wallets and fintech payment processors, including transaction details, user accounts, and usage patterns.
• Detects whether a single agent or sub-agent is splitting or aggregating transactions across multiple digital platforms.
• Supports identification of structured deposits, rapid fund movements, and other anomalies possibly obscured by multi-provider usage.

• Consolidates transaction details, user identifiers, and operational data from e-wallets or app-based remittance services.
• Highlights inconsistencies in user information across multiple platforms, detecting potential misuse or rapid account switching.

• Consolidates transaction details from fintech payment processors, mobile wallets, and online payment interfaces.
• Helps map repetitive and rapid routing of funds across diverse payment services, consistent with automated layering.
• Identifies unusual usage across multiple digital payment methods lacking clear economic rationale.

Payment platform data captures usage records from e-wallets and fintech processors, including:

• Multiple payment accounts or methods tied to the same user.
• Transaction timelines and volumes across different platforms.

This data helps pinpoint customers employing diverse payment instruments to execute and later dispute transactions, revealing recurring chargeback abuse patterns.

• Contains detailed records from e-wallets and other digital payment services, including user identifiers, transaction timestamps, amounts, and operational metadata.
• Helps uncover potentially high-volume or atypical payments associated with the sale, distribution, or facilitation of exploitative content involving minors.

• Captures transaction details, user identifiers, e-wallet usage, mobile payment methods, and related operational data from digital platforms.
• Identifies suspicious deposit or withdrawal patterns, frequency, and velocity across multiple accounts.
• Correlates with casino transaction records to detect rapid chip transfers and potential collusion or funneling of funds in chip dumping schemes.

• Captures transaction details from digital auction sites, e-wallets, and similar fintech platforms used for collectible purchases and sales.
• Helps identify frequently used online channels with limited record-keeping that facilitate partial concealment of collectible trades.
• Detects large volumes of auctions or private sales inconsistent with a customer’s profile, indicating potential money laundering activity.

• Consolidates transaction details from P2P or digital payment services, including user identifiers, transfer volumes, and timestamps.
• Supports identification of repeated small payments that collectively form significant sums, potentially used to layer illicit funds.
• Facilitates detection of unusual P2P transaction clusters lacking a legitimate economic rationale.

Tracks e-wallet and digital payment usage, including transaction details, user identifiers, and refund/chargeback records. This helps detect potentially fraudulent or excessive refunds tied to substandard pharmaceuticals.

Compiles records of user identities, transactions, and operating details from digital wallets and payment platforms. This data helps uncover abrupt or large-value withdrawals and conversions from game environments to fiat, often via third-party brokers with lax AML controls.

Captures the movement of cryptojacking proceeds through online payment and e-wallet systems, especially as criminals may convert assets into spendable digital balances or funnel them through non-bank payment apps.

• Tracks e-wallet and fintech payment processor transactions, including user identifiers, transaction amounts, and metadata.
• Detects patterns of rapid or high-volume transfers through digital channels, often exploited by drug traffickers for layering.
• Facilitates the identification of cross-platform money flows that circumvent traditional banking controls.

Collects transaction details, user identifiers, shipping confirmations, and refund records from e-commerce and digital payment services. This data allows analysts to identify inflated pricing, detect unfulfilled orders, spot frequent high-value refunds with minimal justification, and pinpoint other suspicious patterns indicative of manipulated online sales or sham transactions.

• Captures bidding and payment logs on online auction and payment platforms.
• Identifies staged or nonexistent competition, repeated winning by the same bidder, or other auction irregularities, uncovering sham or fictitious sale events.

• Captures transaction records, user identifiers, and operational data from e-wallets or digital payment providers.
• Allows tracing of small contributions made through common payment tools, identifying repeated or coordinated transfers into a single beneficiary account.

• Captures e-wallet or digital account transactions, including cash-out requests.
• Identifies frequent or structured conversions of online balances to physical currency, supporting investigations of immediate cash-out schemes.

• Consolidates user account details, transaction histories, and payment flows on digital platforms and e-wallets.
• Enables detection of unusual bursts of in-game currency purchases, frequent microtransactions, and rapid withdrawals to external accounts or cryptocurrency conversions.

Includes records from e-wallets and fintech platforms detailing transaction amounts, payment methods, and funding sources. In lottery-focused laundering:

• Pinpoints the use of prepaid cards or anonymous payment methods for ticket purchases.
• Helps identify layering attempts through multiple online platforms.
• Correlates unusual digital payment flows with lottery purchases or redemptions.

• Consolidated records from e-wallets and fintech payment processors, covering transaction details, user identifiers, balances, and usage patterns.
• Helps reveal micro-amount flows dispersed across multiple digital platforms in short timeframes, indicative of micro-structuring.

Captures transaction details from mobile wallets and P2P payment platforms, including timestamps, amounts, user IDs, and transaction types. This data enables the detection of unusual volume spikes, structured transaction patterns, and frequent switching among different mobile payment services for layering.

• Stores transaction volumes, user account details, and cross-border payment flows for e-wallets or fintech platforms.
• Highlights incomplete or forged customer information for large or frequent gambling-related transfers.

This data aids AML detection by flagging suspicious digital payment behaviors and unverified accounts funneling illicit proceeds through offshore gambling.

Includes e-wallet transaction information, user account details, balances, and usage patterns. Analysis of cross-border transfers, partial anonymity features, and repeated low-value transactions helps detect layering and threshold evasion linked to offshore e-wallet issuers.

• Captures e-wallet and digital payment transactions, including user identifiers, timestamps, amounts, and device details.
• Reveals structured or frequent deposits funneled from digital payment accounts into multiple gambling accounts.
• Supports detection of layering attempts leveraging alternative payment methods to obscure the origin of funds.

• Captures transaction details from online payment processors and e-wallets, including user identifiers, transaction dates, and amounts.

• Helps identify abnormal purchase patterns for in-game assets, rapid liquidation of gaming items, and potentially high-frequency or large-volume transactions inconsistent with typical gameplay.

These records include transaction timestamps, amounts, user identifiers, device or platform usage logs, and cross-platform flow details from e-wallets, digital payment processors, and peer-to-peer platforms. This data is essential in tracing the layering of payment tokens across multiple online channels, identifying suspicious patterns such as smurfing, and detecting unverified or non-compliant users exploiting minimal KYC requirements.

Allows monitoring of P2P-specific transaction amounts, helping to identify patterns of structured transfers that remain below mandated reporting limits.

• Consolidates transaction details, user identifiers, balances, and platform usage metrics.
• Helps detect unusual payment flows, anonymous transfers, and unregistered e-wallet activities possibly linked to sexual exploitation proceeds.

• 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.