Stop payment fraud, not business

Threat intelligence is embedded into the payment platform's risk engine to detect anomalous transaction patterns, flag suspicious activity, and trigger real-time interventions. AI/ML models analyze behavioral and transactional signals to identify fraud before it completes, enabling proactive blocking or step-up verification rather than reactive dispute resolution after the fact.

Instant payout solutions include risk controls such as Know Your Customer/Know Your Business (KYC/KYB) verification, real-time transaction monitoring, and configurable fraud rules to reduce exposure. While speed is a priority, the platform applies risk checks before disbursement to detect fraudulent payout requests. Balancing speed with fraud controls is a core design principle of the disbursement infrastructure.

Fraud prevention scales through AI/ML models that continuously analyze transaction data across the platform's full volume (processing hundreds of millions of transactions) to detect emerging patterns. Rules engines and risk thresholds can be tuned dynamically, and the platform's architecture is designed to maintain fraud detection performance as transaction volumes grow.

False positives are reduced by layering behavioral context, transaction history, and configurable risk rules rather than relying on single-signal triggers. Machine learning models refine scoring thresholds over time based on observed outcomes, and rules can be tuned by channel, customer segment, or use case to minimize unnecessary declines without increasing fraud exposure.

Yes. The risk and fraud prevention engine supports configurable rules that can be scoped by channel, transaction type, customer segment, or use case. This allows businesses to apply different risk thresholds for recurring billing versus one-time payments, or for card-present versus card-not-present environments, without applying blanket rules across all transactions.