Real-Time Transaction Screening with Policy-Governed Orchestration
Reduce false positives, lower manual review costs, and support compliant real-time and cross-border payments with patent-pending RDS-HIVE orchestration. Purpose-built for AML transaction screening across instant payment networks, SWIFT corridors, and high-volume cross-border payment compliance environments.
Governed Decision Stages
Patent-Pending RDS-HIVE Decision Orchestration
Traditional transaction screening systems rely on static rulesets or isolated AI models. RDS-HIVE separates transaction screening into governed decision stages: scope determination, signal interpretation, and validation, reconciled through structured consensus.
By separating detection, interpretation, and validation, RDS-HIVE reduces unnecessary escalation, lowers total transaction screening costs, and prevents alert storms common in legacy transaction filtering systems.
Deterministic Screening
Mandatory sanctions, jurisdiction, vessel, and identifier controls enforced through deterministic rules.
Probabilistic Interpretation
Contextual interpretation of free-text narratives and name similarity through probabilistic models.
Hive Mind Consensus
Independent decision proposals evaluated through structured consensus. Convergence increases confidence, divergence triggers escalation.
Policy-Bound Authorization
Final authorization remains governed by policy. No single model or rule engine holds sole authority.
Effective AML Payment Screening Requires Separation of Decisions
By isolating scope determination, signal interpretation, and validation, RDS-HIVE reduces defensive over-screening and unnecessary alert escalation common in legacy transaction filtering systems. This structured separation supports higher straight-through processing in instant and cross-border payment environments.
Real-Time Payment Screening
Millisecond execution across instant payment and cross-border payment networks.
Always-On Availability
Resilient infrastructure designed for continuous uptime across SWIFT payment screening and instant corridors.
Reduced False Positives
Context-aware interpretation reduces unnecessary alerts common in rule-only transaction screening systems.
Policy-Aligned Controls
Deterministic guardrails enforce sanctions and regulatory obligations at every decision stage.
Dual Control Built-In
Maker-checker separation ensures no single model or rule engine is authoritative.
Flexible Deployment
Augment existing screening engines or deploy as standalone payment screening software via API.
Lower Total Screening Cost
Reduce manual review workload, unnecessary alerts, and compliance overhead across high-volume payment environments.
The RDS-HIVE Consensus Model
Multiple independent evaluators assess each transaction in parallel: rule-based sanctions screening, name similarity analysis, narrative interpretation, and behavioral correlation. When evaluators converge, automation is permitted. When evaluators diverge, policy determines escalation. The RDS-HIVE consensus model reduces unnecessary payment holds while preserving regulatory rigor in cross-border and correspondent banking flows.
Sanctions Screening
Name Similarity
Narrative Analysis
Behavioral Correlation
Model Risk Mitigation
Independent evaluators reduce single-point-of-failure exposure
Full Decision Provenance
Each evaluator's contribution is recorded and auditable
Regulator-Ready
Aligns with dual-control and four-eyes governance principles
End-to-End Decision Traceability
For every screened transaction: original RAW message preserved, canonical structured representation stored, contextual augmentation documented, screening matches recorded, interpretation rationale captured, validation pathway logged, and final authorization timestamped.
Governance-Grade Decision Records
Every decision includes timestamped evaluator outputs and a structured chain of evidence. Supports audit review, sponsor bank oversight, and supervisory examination. Also supports correspondent banking scrutiny and enterprise compliance governance.
Structured Narrative Parsing
Free-text fields are parsed to extract risk signals. The original raw message and canonical representation are both preserved.
Screening Match Records
Every match that contributed to the decision is recorded: list source, name variations, confidence scores, and jurisdiction indicators.
Audit-Ready Evidence
Complete chain of evidence exportable for any transaction. Every evaluator contribution, timestamp, and validation action is preserved.
Supervisory Ready: Supports audit-ready transaction screening with explainable AML screening records suitable for regulatory examination and sponsor bank reporting.
Explainability Without Sacrificing Control
Rule-only transaction filtering often over-screens under uncertainty. Black-box AI systems may reduce alerts but increase model risk exposure. RDS-HIVE separates inference from authority.
Architecture Principle: Inference and authority are separated by design. Probabilistic models inform, deterministic rules enforce, and policy governs. This strengthens real-time transaction monitoring without introducing black-box risk.
Designed for Supervisory Review and Continuous Assurance
Suitable for internal audit, external regulators, and enterprise model risk governance. Every screening decision is reconstructable and defensible.
Immutable Audit Trail
Full decision reconstruction with timestamped evaluator outputs and validation transparency.
Scenario Testing and UAT
Run simulated transactions through defined scenarios to validate screening behavior before and after deployment.
Performance and Alert-Rate Reporting
False positive rates, decision latency, and throughput metrics for continuous assurance.
Supervisory Examination Support
Reconstruct any screening decision on any date for internal audit, external regulators, or enterprise model risk governance.
Reduce Alert Volume. Enable Real-Time Payments. Maintain Regulatory Control.
Download the RDS-HIVE whitepaper to explore how patent-pending Hive Mind orchestration lowers transaction screening costs while preserving auditability.
RDS-HIVE represents a structured approach to real-time AML transaction screening, built on policy-governed orchestration, deterministic controls, and full decision traceability.