AML Resolve is the first operational module on the SilkRiver Compliance Operations Platform.

SilkRiver AML Resolve is the governed resolution layer for sanctions, PEP/RCA, and adverse media screening. It assembles context across systems, prepares reviewer-ready cases, and helps institutions reach faster, more defensible outcomes - including auto-clearing safe classes when evidence and confidence exceed customer-defined policy thresholds.

Proof strip

— No rip-and-replace
— Audit-ready evidence
— Policy-aware workflows
— Auto-clear safe classes
— Built for complex enterprises

A sharp visual of fragmented signals becoming a coherent case: alert, entity data, prior history, adverse media, policy context, resolution path, and audit artifact. The visual should show three possible outcomes: auto-clear, analyst-ready, or escalate.

A sharp visual of fragmented signals becoming a coherent case: alert, entity data, prior history, adverse media, policy context, resolution path, and audit artifact. The visual should show three possible outcomes: auto-clear, analyst-ready, or escalate.

The real problem

AML screening does not usually break on the obvious alerts

The real strain appears in the hard edge cases - the cases that require fragmented data to be assembled, judgment to be applied across functions, and a decision that a human reviewer can defend. Easy hits create volume. Hard edge cases create operational and regulatory strain.

In most institutions, analysts still become the integration layer. They gather evidence from multiple systems, reconcile conflicting signals, write rationales under time pressure, and hand off decisions that are often difficult to review later at scale. Human judgment should remain in control. Manual case assembly should not.

A two-lane visual: obvious alerts versus hard edge cases. The second lane should visibly show branching complexity and fragmented context.

What AML Resolve does

An overlay built for reviewer-ready case assembly and defensible case resolution.

SilkRiver sits on top of the institution's existing screening and case workflow environment. It pulls the relevant context, assembles an evidence pack, drafts a structured rationale, routes cases based on policy and threshold logic, and records the decision process in a durable, reviewable way. This is not a screening engine replacement. It is a managed, policy-aligned resolution layer that works within the current environment while improving how cases are resolved.

Gather relevant context
— Assemble evidence across approved systems
— Draft structured, consistent rationales
— Prepare reviewer-ready cases
— Auto-clear safe classes when thresholds are met
— Escalate exceptions and ambiguous cases
— Record what was reviewed, by whom, under which policy logic

Controlled automation, not loss of control

Control the threshold. Automate what you trust. Escalate what you do not.

AML Resolve is designed for progressive trust. Institutions do not need to leap from manual review to broad automation. They can begin with conservative thresholds and customer-approved safe classes, then expand auto-clear only as confidence grows. Every auto-cleared case still carries full evidence, policy traceability, and audit history. Human reviewers remain accountable for ambiguity, exceptions, and higher-risk work.

How it works

Step 1 - Gather context
SilkRiver uses approved integrations and identifiers to pull the relevant case context from existing systems and sources.

Step 2 - Assemble the case
The platform organizes the relevant signals, histories, and supporting evidence into a structured, reviewer-ready case.

Step 3 - Prepare the rationale
SilkRiver drafts a clear, consistent rationale and proposed resolution path under the institution's policy framework.

Step 4 - Route by threshold
Cases move into one of three paths: auto-clear when evidence and confidence exceed configured thresholds for approved safe classes; analyst-ready when human review is still required but the case is already assembled; or escalate when ambiguity, policy sensitivity, or risk exceeds the permitted threshold.

Step 5 - Record and report
Every case produces a durable evidence trail, policy context, and outcome record that can support QC, audit, and operational review.

A horizontal operating model with explicit paths for auto-clear, analyst-ready, and escalate. Include threshold logic and evidence artifacts.

Why SilkRiver is different

Most vendors optimize for triage throughput. SilkRiver is built for defensible case resolution.

Many vendors now promise faster alert handling, fewer false positives, and AI-generated recommendations. Those benefits matter, but they are increasingly table stakes. SilkRiver is designed around a different problem: the operational gap between a screening alert and a defensible decision.

That means SilkRiver is built to help institutions resolve the hard cases, not just the easy ones; assemble context across fragmented systems; preserve human accountability while reducing manual assembly; create auto-clear lanes that are governed by evidence and trust thresholds; give managers and QC teams operational visibility; and produce outputs that are actually usable in audit, exam, and oversight contexts.

Not just for analysts
Built for the people who have to stand behind the process.

Analysts
Less swivel-chair work, faster case preparation, and more consistent documentation.

Managers and QC
Visibility into queue aging, analyst performance, handling consistency, safe-class auto-clear rates, and sampling coverage.

Audit and oversight
Faster retrieval of what was reviewed, why the conclusion was reached, which policy version applied, and why a case was auto-cleared, reviewed, or escalated.

Enterprise readiness
Designed for regulated environments where trust must be earned.

SilkRiver is designed for governed autonomy, not black-box automation. That means customer-defined confidence thresholds, auto-clear only for approved safe classes, bounded actions and explicit escalation paths, role-aware access and policy control, versioned playbooks and routing logic, immutable evidence artifacts and durable audit history. Institutions can start conservatively, measure outcomes, and widen automation only where trust has been earned.

Part of a broader platform
Start with AML Resolve. Expand over time.

AML Resolve is the first operational module on the SilkRiver Compliance Operations Platform. The same evidence-led, policy-aware architecture can extend into adjacent workflows where institutions face the same core challenge: fragmented context, manual case assembly, and high-accountability decisions.

Expansion areas

1. KYC/KYB Resolve
2. Onboarding exceptions
3. Investigation-heavy compliance workflows

Start with a bounded, measurable AML Resolve pilot.

A good pilot should feel low-risk and operationally concrete. Start with one alert type, one team, conservative thresholds, and clearly defined safe classes. Shadow mode can be used first if desired. Then measure both workflow improvement and trust-based automation from day one.

Example success measures

• Handling time reduction
• Evidence completeness
• Analyst adoption
• Audit retrieval speed
• Safe-class auto-clear rate
• Manager visibility into queue performance