DeeMoney selects SEON for fraud and AML screening

DeeMoney announced it has chosen SEON as its fraud and compliance partner to strengthen risk detection across its cross-border money transfer services.

Under the agreement, DeeMoney will integrate SEON’s fraud prevention tools into onboarding and transaction monitoring to detect suspicious activity and support anti-money-laundering compliance.

The deal covers customer authentication, transaction scoring and real-time screening of payment flows, with SEON’s analytics linking into DeeMoney’s payment rails and customer lifecycle processes.

SEON uses device signals, digital footprint data and machine-learning risk scoring to flag high-risk accounts and transactions. Inputs include IP data, device and browser fingerprints, email and phone intelligence and behavioural patterns.

DeeMoney cited rising remittance volumes and tighter regulatory expectations for customer due diligence across the region as reasons for selecting an external partner.

The fintech plans to use the tools to reduce fraudulent activity, speed up verification where risk is low and provide evidence for compliance reporting. Implementation will support pre-transaction screening during onboarding and ongoing monitoring to detect anomalies in real time.

DeeMoney will retain control over final decisions on holds, manual reviews and escalations to its compliance teams.

Firms that handle cross-border transfers face risks such as account takeovers, synthetic identity fraud and unauthorized transactions, and must meet know-your-customer and anti-money-laundering obligations. Adding behavioural and device-level signals will allow automated risk scoring to be combined with human review to reduce false positives.

DeeMoney provides remittance services for customers sending money internationally from Thailand and serves migrant workers and expatriates. SEON has worked with digital merchants and financial firms to reduce chargebacks and fraud losses and to support compliance processes.

DeeMoney and SEON will monitor performance after deployment to measure changes in fraud loss rates, approval times for legitimate customers and the number of cases requiring manual investigation.