Our agentic AI and automation combines human expertise, agentic AI and rules based logic to convert your policies and risk appetite into consistent, auditable operations across screening, transaction monitoring, CDD/EDD, fraud and other regulatory processes. It’s designed to reduce operational drag, improve evidence quality, and help firms scale decisioning without compromising control.
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People + AI + RPA orchestration across end-to-end case workflows
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Agentic AI that progresses a case (not just scores a single event)
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Configurable human-in-the-loop with L1/L2 operating models
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Explainable outputs with drivers and confidence scores
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Integration-first (API and structured file ingestion)
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Audit trails, MI and QA controls built in from day one
What makes our approach different
Many organisations can build a model. Fewer can deliver it safely into a regulated operating model and keep it working as volumes, typologies, and expectations change.
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Nexus AML is an experienced delivery partner, combining deep financial crime operations expertise with practical automation engineering:
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Multi‑jurisdiction delivery experience (UK, EU, MENA, APAC, US) across different regulatory expectations
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A proven track record in large-scale financial crime operations and AI-enabled managed services since 2017
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Delivery models that support enterprise reliability, including defined SLAs, 24/7 availability approaches and incident response expectations
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Commercial options designed for operational leaders, including predictable unit-cost models (e.g., price per alert worked) aligned to throughput and control
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This is not “AI in a lab”. It’s technology that is built to run inside live, audited operations.
Why do firms need this now?
Financial crime operations are being pushed in multiple directions at once: alert volumes are rising, typologies are becoming more complex, regulation is evolving, and experienced headcount is finite.
Traditional “add more analysts” approaches don’t scale, and point solutions that only score transactions or generate risk labels often leave teams with the same bottlenecks downstream.
What firms need now is technology that can move work forward: triage, enrich, route, evidence, draft, quality-check, while keeping control and accountability where it belongs.
What “agentic AI” means for you
Most AI in financial crime is deployed as a single step: a score, a match probability, a risk label. Useful, but not sufficient.
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In our technology, agentic AI means:
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AI agents that follow defined workflows
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that can call tools and systems (case platforms, data sources, enrichment services)
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and can progress a case from one stage to the next, including routing to a human when required
In practice, agentic AI helps move work through the operation: from detection to triage, enrichment, evidence building, drafting and QA, while maintaining clear controls, decision logs, and accountability.
Our platform wraps around existing operations and systems to orchestrate end-to-end execution:
Data in​​
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Ingest from client systems through external APIs and structured file formats (e.g., CSV)
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Extend to new sources as your operation evolves
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Support external data enrichment, including public datasets, third-party providers and bank-specific systems, using client-defined logic and thresholds
Orchestration layer
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Coordinates RPA, AI models, and agent workflows
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Enables cross-domain risk routing, agents can spot risk outside their “home” process and route to the right workflow or team (e.g., sanctions risk surfaced during TM)
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Controls and evidence
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End-to-end audit trails and decision logs
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Built-in QA checkpoints and governance controls
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Role-based dashboards and MI views tailored to operations, QA and compliance leadership
Outputs to people and systems
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Push updates back into your existing case/alert management tools via API
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Route work to analysts and reviewers with clear evidence, rationale and next actions
Deployment
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Cloud-hosted on AWS, with hybrid/on-prem options explored by exception where required.
High-level architecture: built to orchestrate, integrate and audit
A deliberate balance: RPA rules, AI analysis and human execution
RPA rules and deterministic automation
Ideal for repeatable steps with stable logic: data handling, case creation, routing, structured checks, and consistent housekeeping tasks.
AI for analysis and judgement support
Used where pattern recognition, ranking, summarisation, entity resolution, or probabilistic reasoning adds material value, with clear explainability and measurable performance.
Human execution for accountability and judgement
Reserved for higher-risk or ambiguous cases, exceptions, and final decisions, supported by evidence and structured workflows.
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Our philosophy is simple: we balance these methods deliberately to improve outcomes and control, not to replace people. The goal is a safer, faster operation where humans spend time where their judgement is most valuable.
Humans at the centre: governance, QA and configurable control
Our platform is designed to fit around your people and your governance model, not the other way around.
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Our subject matter experts design the rules and agent workflows, and define escalation paths
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Teams monitor outputs, run quality assurance, and perform model governance
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Human-in-the-loop is configurable, so you can specify exactly where human intervention is required within each workflow
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The platform supports tiered operating models (L1–L2) and can mirror review structures commonly used in banks, including QC/QA mechanisms for consistency and auditability
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Final decision-making remains with humans, with clear evidence and decision trails to support oversight​
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This keeps accountability clear, while enabling automation to remove friction from the work that slows teams down.
In regulated operations, performance is only half the story. You also need defensibility.
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Our platform is designed so that each outcome can be understood, tested, and evidenced:
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Explainable decisions on every case, showing key drivers (SHAP-style) and confidence scores
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Configurable confidence thresholds that determine when items can be auto-disposed versus routed to human review
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A self-serve testing interface / sandbox, allowing teams to upload cases and immediately see disposition, explanation and confidence (with supporting API endpoints where required)
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Clear decision logging and traceability to support audits, internal governance and supervisory review
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Drift management and operational monitoring to ensure models and workflows remain stable and defensible over time
We also provide documented alignment and governance support across:
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Global financial crime regulatory frameworks (including FATF, UK MLR, EU requirements and relevant US expectations), with evidence/control mapping available
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Emerging AI governance frameworks across the UK/EU/US, supporting risk-based oversight and model governance expectations
Trust, explainability and regulator-ready evidence
Managed outcomes: measurable performance, operational control
Agentic technology matters when it improves real operational outcomes, consistently, at scale.
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Teams use the platform to drive:
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Fewer false positives and better discounting quality
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Faster case handling and fewer handoffs
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Reduced backlogs and improved SLA performance
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Predictable cost per case, supported by unit-cost commercial models where appropriate
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Stronger evidence packs and clearer rationale for supervisors and internal audit
To keep performance managed (not assumed), the platform supports:
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Role-based dashboards across ops, QA and compliance leadership
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A full measurement framework covering: alert reduction, handling time, SLA adherence, FP/FN, overrides/escalations, QA error rates, hours saved, and cost per case
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Auto-prioritisation and low-risk auto-disposition using configurable rules and confidence thresholds, with governance and auditability built in
