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Governed Agentic AI for Fraud & AML

GRAIL

Agents investigate.
People decide.

Bring evidence, analysis, and recommendations into a governed investigation workflow. Keep policy, approval, and accountability with your institution.

Discuss GRAILExplore the workflow

Governed Risk & Agentic Investigation Layer

A separate P4S product

From alert to a reasoned recommendation.

Investigation teams bring together customer profiles, transactions, screening results, and source material before making a decision. GRAIL focuses agentic AI on that evidence-intensive work.

The operating model is explicit: agents gather context, run checks, and prepare recommendations. Authorized teams review the evidence, approve decisions, and retain control of consequential actions.

The use-case portfolio

Four areas of work.
One governed operating model.

Select the agents and workflows around your institution’s priorities, data access, and control requirements.

01

Investigate

Bring the relevant context into the case.

AML alert investigation
Gather customer and transaction context, review activity, and prepare an investigation recommendation.
Sanctions & watchlist review
Examine profiles, aliases, geography, and ownership around a screening hit for human disposition.
Adverse media analysis
Organize relevance, severity, justification, and citations for compliance validation.
Fraud investigation
Bring suspicious transactions, device information, behaviour, and linked accounts into an EFRMS case review.
02

Assess & remediate

Prioritize the work that needs attention.

Customer risk assessment
Prepare an explainable risk assessment using the signals and categories agreed with the institution.
KYC remediation
Identify missing information and data-quality issues; prepare remediation priorities and outreach needs.
Risk-based prioritization
Organize worklists around risk, KYC gaps, alert history, geography, and product context.
eKYC onboarding review
Bring onboarding evidence together for an authorized review of the customer file.
03

Draft & report

Turn findings into a reviewable narrative.

SAR / STR drafting
Consolidate investigation findings into a draft suspicious activity report or suspicious transaction report.
Referenced transactions
Connect the narrative to the transactions and source material considered in the investigation.
MLRO review
Keep approval and submission with the Money Laundering Reporting Officer (MLRO); the agent does not file the report.
04

Optimize controls

Propose changes. Keep control owners in charge.

Monitoring rule tuning
Suggest threshold changes and simulate their impact before human validation.
Name screening quality
Examine matching-rule weaknesses and potential coverage gaps for review.
Requirements-to-controls mapping
Map applicable regulatory requirements to controls for owner assessment; changes remain subject to validation and approval.

Implementation starts with an agreed use case and control scope. End-to-end orchestration is an expansion path described below.

Alert to decision

One workflow.
Clear responsibility at every stage.

Agent preparation and human decision-making are visible parts of the same process.

  1. Agent01

    Receive

    Start with the alert and its case context.

  2. Agent02

    Gather

    Collect permitted profiles, transactions, checks, and sources.

  3. Agent03

    Analyse

    Assess the evidence and the relevant risk signals.

  4. Agent04

    Recommend

    Draft findings, rationale, and a proposed next step.

  5. Human05

    Approve

    Review the sources and make the authorized decision.

  6. Human06

    Act & record

    Carry out the approved action and record the outcome.

Audit layer across the workflowEvidence & citationsRationale & confidenceNamed approvalAction record
An investigator tracing a source reference to a recommendation in an anonymous evidence dossier

Evidence before decisions

A recommendation your team can examine.

Bring the source material, transaction references, reasoning, and confidence into the review. Give investigators a structured starting point for challenge, validation, and follow-up.

  • Connect findings to the source information considered.
  • Present the rationale alongside the recommendation.
  • Keep reviewer decisions and follow-up actions in the case record.

The control model

Five guardrails.
Governance built into the workflow.

01

Bank-owned policy

AI does not override the institution’s policies or risk appetite.

02

Human-controlled filing

The agent can draft an STR. The MLRO approves and submits it to the Financial Intelligence Unit (FIU).

03

Protected consequential actions

High-risk case closure remains human-only. Account blocks and watchlist closures require authorized human approval.

04

Named accountability

An authorized approver owns the decision; an AI recommendation does not transfer responsibility.

05

Traceable investigation

The workflow retains evidence, source citations, reasoning, confidence, and action records for review.

Agent prepares

  • Gather permitted data and search sources
  • Run checks and assess risk context
  • Draft narratives and recommendations
  • Simulate proposed control changes

Human approval required

  • Risk-category changes and case disposition
  • Watchlist closure and account-block proposals
  • KYC remediation and profile-update proposals
  • Threshold and rule-change recommendations

Human-only responsibility

  • Approve policy decisions and exceptions
  • Submit an STR to the FIU
  • Close high-risk cases
  • Deploy control changes and commit customer-record edits

Reference architecture

Connect agents, people,
and the control layer.

Define access and integration around the institution’s systems, responsibilities, and security requirements.

People

Investigators · Fraud team · Compliance · MLRO

Governance

Policy controls · Approval workflow · Audit log · Explainability · Model validation

Agents

Orchestrator · Alert investigation · Screening · Media · Risk assessment · Fraud · Reporting

Data & tools

Core banking · Transaction monitoring / EFRMS · KYC / CDD · Watchlists · Adverse media · Device data · Case management

Fit within the P4S portfolio

GRAIL is a separate governed agentic AI offering. Scope connections with AML screening, fraud risk management, network analysis, and API orchestration around the workflow being implemented.

LUMINA — AMLPayGuard — fraud riskLUMINA Nexus — network contextiROUTE — API orchestration

A phased implementation

Start focused.
Expand with validated controls.

Define the use case, baselines, guardrails, and review criteria before expanding the scope.

01 / CONTROLLED SCOPE

Assist

Start with a focused review.

Select AML alert investigation, screening-hit context, adverse media, or onboarding review. Agree the evidence sources, review responsibilities, and acceptance criteria.

02 / CONTROLLED SCOPE

Recommend

Validate consequential recommendations.

Expand into risk assessment, reporting drafts, fraud investigation, or remediation after validating the approval workflow, evidence quality, and audit controls.

03 / EXPANSION PATH

Orchestrate

Expand only after validation.

Future-state end-to-end investigation and control-optimization agents build on the validated operating model. Progress depends on bank ownership, model validation, and review of the controls.

Measure the implementation against agreed baselines.

Review analyst effort, review turnaround, recommendation quality, human overrides, evidence coverage, and audit completeness. Targets and timelines are agreed for the institution’s scope and validated through the engagement.

GRAIL questions

Clear boundaries.
A practical starting point.

Discuss the investigation workflow, approval responsibilities, and integration scope your team needs.

Discuss GRAIL
What does GRAIL mean?

GRAIL stands for Governed Risk & Agentic Investigation Layer. It is a separate P4S product focused on governed agentic AI for financial crime operations.

Is GRAIL part of AMIN?

No. GRAIL is a separate offering. Its scope brings agent workflows, policy constraints, evidence, approvals, and auditability together across fraud and AML use cases.

Does the agent make the final decision?

Agents prepare analysis and recommendations. Authorized teams approve decisions. High-risk case closure, STR submission, policy decisions, control deployment, and customer-record edits retain human ownership.

Can GRAIL file an STR or independently block an account?

No. The agent does not submit an STR or independently block accounts. The MLRO retains report approval and submission, and account actions require the relevant authorized human approval.

What does a first engagement cover?

Begin with a selected use case, the systems and data involved, permitted access, the review workflow, and the controls that must be validated. Agree baselines, acceptance criteria, and responsibilities before expanding scope.

Are the full roadmap and timelines fixed?

The implementation follows a scoped, phased approach. End-to-end orchestration is an expansion path. Available agents, integration work, rollout milestones, and validation criteria are confirmed for the engagement.

The P4S portfolio

Explore the connected disciplines.

P4S LUMINA
Anti-Money Laundering

LUMINA

Bring KYC, sanctions and watchlist screening, transaction screening, and rule configuration into focus with P4S’s anti-money laundering solution.

Explore LUMINA
P4S PayGuard
Enterprise Fraud Risk Management

PayGuard

Address fraud risk across digital banking and payments with PayGuard EFRMS, powered by artificial intelligence and machine learning.

Explore PayGuard
P4S iROUTE
API Orchestration & Routing

iROUTE

Connect source and destination APIs through a central orchestration interface, with routing, data transformation, and graphical monitoring.

Explore iROUTE

Choose the workflow.
Define the guardrails.

Talk to P4S about GRAIL for your fraud and AML operations.

Discuss GRAIL