Screen payments against OFAC and 1,500+ global watchlists in real time. Reduce false positives with AI-powered decisioning and full audit trails - built for regulated institutions in 193+ countries.
Trusted by EY, Global Payments, Coinchange, Payment Source, and 250+ regulated institutions in 193 countries.
We started in sanctions screening. Over ten years, we watched the industry move from near-exact matching to defensive over-screening. The problem was never detection. It was the absence of decisioning.
Fuzzy matching expanded. Real-time rails accelerated. Systems screened everything defensively.
More screening. Not better outcomes.
We separated detection from interpretation. RDS-HIVE extends that into a full Transaction Decisioning Framework.
Structured, policy-governed decision process.
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.
Mandatory sanctions, jurisdiction, vessel, and identifier controls enforced through deterministic rules.
Contextual interpretation of free-text narratives and name similarity through probabilistic models.
Independent decision proposals evaluated through structured consensus. Convergence increases confidence, divergence triggers escalation.
Final authorization remains governed by policy. No single model or rule engine holds sole authority.
Traditional transaction screening treats detection as a single pipeline. In practice, screening outcomes depend on three distinct decisions that are often combined into one process, creating unnecessary alerts and operational complexity.
RDS-HIVE separates transaction screening into three governed decisions:
Determine which elements of a transaction are relevant before screening execution begins. Multiple representations of transaction data are evaluated independently to establish policy-aligned screening scope.
Screening produces signals, not outcomes. Deterministic rules and probabilistic reasoning independently evaluate screening results to propose policy-permitted dispositions.
Validation is applied proportionately based on policy, confidence, and regulatory obligation, ensuring decisions are finalized with explicit auditability.
By separating these decisions, screening execution occurs only after scope and interpretation are governed through policy controls.
High alert volumes often originate from uncertainty in raw transaction data rather than detection logic itself. Institutions frequently screen broadly to avoid compliance risk, which increases downstream review effort.
RDS-HIVE governs screening earlier in the lifecycle by:
Screening engines remain deterministic execution components, while decision authority is enforced through policy governance. This approach enables proportionate automation while maintaining reproducibility and audit transparency across the screening lifecycle.
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.
Millisecond execution across instant payment and cross-border payment networks.
Context-aware interpretation reduces unnecessary alerts common in rule-only transaction screening systems.
Deterministic guardrails enforce sanctions and regulatory obligations at every decision stage.
Maker-checker separation ensures no single model or rule engine is authoritative.
Augment existing screening engines or deploy as standalone payment screening software via API.
Reduce manual review workload, unnecessary alerts, and compliance overhead across high-volume payment environments.
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.
Independent evaluators reduce single-point-of-failure exposure
Each evaluator's contribution is recorded and auditable
Aligns with dual-control and four-eyes governance principles
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.
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.
Free-text fields are parsed to extract risk signals. The original raw message and canonical representation are both preserved.
Every match that contributed to the decision is recorded: list source, name variations, confidence scores, and jurisdiction indicators.
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.
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.
Suitable for internal audit, external regulators, and enterprise model risk governance. Every screening decision is reconstructable and defensible.
Full decision reconstruction with timestamped evaluator outputs and validation transparency.
Run simulated transactions through defined scenarios to validate screening behavior before and after deployment.
False positive rates, decision latency, and throughput metrics for continuous assurance.
Reconstruct any screening decision on any date for internal audit, external regulators, or enterprise model risk governance.
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.
