Stay in control
of financial crime

DS AML monitors client and transaction activity through automated batch analysis and real-time payment screening.

The platform helps detect suspicious behaviour across accounts, payments, geographies, currencies, customer profiles and transaction patterns.

It includes 26 pre-built AML scenarios covering key typologies such as high-value transactions, risky geographies, structuring, behavioural change, dormant accounts, card activity, loans, sanctions, PEP exposure and adverse media.

Screen customers, transactions and entities during onboarding, payment processing, scheduled re-screening or on-demand investigation.

Sanctions lists are updated 6 times a day, helping institutions react quickly when new persons, entities or groups appear on relevant lists.

Supported sources can include EU, OFAC and UN sanctions lists, internal bank-managed lists, local PEP lists and optional global risk intelligence integrations.

DS AML includes configurable client risk scoring based on 6 risk-factor categories: client profile, geography, products and services, transaction behaviour, associated entities and enhanced due diligence triggers.

Risk scoring can be applied during onboarding, periodic review and event-triggered reassessment, giving compliance teams a structured view of customer risk across the full lifecycle.

Alerts are managed through structured investigation workflows.

Compliance officers can review client records, transaction history, scenario matches, risk scores, related entities, previous decisions and supporting evidence in one operational workspace.

Every decision, case note, screening result and parameter change is logged, creating a clear audit trail for internal review and regulatory examination.

The built-in Parameter Editor gives compliance teams direct control over scenarios, thresholds and risk settings.

Teams can enable or disable scenarios, adjust parameters, manage risk lists and configure monitoring logic without depending on developers for routine changes.

Before applying parameter changes to production, AML teams can test them against historical data. Backtesting shows how proposed threshold changes would affect alert volumes and which cases would be created or suppressed.

Fraud Management evaluates transactions, payment events and digital-channel activity in real time.

It combines rules, behavioural patterns, contextual data and risk indicators to help institutions approve, challenge, review or stop suspicious activity before it becomes a loss.

The solution is especially relevant for instant payments, card activity, digital banking, PSP environments and high-volume payment operations.

Fraud detection can be strengthened with behavioural profiling, device intelligence and session-level signals.

This helps detect account takeover, malware, bots, remote-access abuse, social engineering indicators, mule activity and other modern fraud patterns that may not be visible through transaction rules alone.

Fraud and compliance teams need decisions they can understand, tune and defend.

White-box rules, models and decision trees support transparent risk scoring, explainable alerts and better governance.

This helps analysts understand why an alert was created and gives management, auditors and regulators clearer evidence of how the system operates.

AML and Fraud Management solutions are designed to connect with existing banking, payment, onboarding, digital-channel and case-management environments.

Deployment can be adapted to the institution's infrastructure, data-residency requirements and operating model.

Digital Systems focuses on practical implementation, controlled integration and direct support from teams that understand regulated financial operations.