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SAR Geographic Attribution Review

Applies field-aware fallback rules to determine and standardize suspicious activity locations in SAR filings when branch or location data are missing, ambiguous, or inconsistent.

The Problem

“Standardize suspicious activity geography in SAR filings when location fields are missing, ambiguous, or inconsistent”

Organizations face these key challenges:

1

Branch or suspicious activity location fields are blank, stale, or populated inconsistently

2

Multiple candidate geographies exist across branch, account, customer, transaction, and narrative fields

3

Free-text SAR narratives contain useful location clues that are not captured in structured fields

4

Analysts apply fallback logic differently, creating inconsistent reporting outcomes

Impact When Solved

Reduce manual geographic review effort for ambiguous SARs by 40-70% depending on narrative qualityIncrease standardized location coverage across filings with missing branch/location fieldsProvide auditable attribution rationale with source-field traceability for every assigned geographyImprove downstream geographic risk analytics, clustering, and suspicious activity trend reporting

The Shift

Before AI~85% Manual

Human Does

  • •Review SAR branch, account, customer, and transaction fields to identify possible activity locations
  • •Read SAR narratives and compare location clues against structured record details
  • •Apply manual fallback logic in spreadsheets to choose a reporting geography
  • •Normalize location names and document the basis for the final attribution

Automation

    With AI~75% Automated

    Human Does

    • •Approve or override medium- and low-confidence geographic attributions
    • •Resolve exceptions where source fields, narratives, or reference locations materially conflict
    • •Review attribution rationales and traceability for audit and regulatory readiness

    AI Handles

    • •Analyze SAR structured fields and apply ordered fallback rules to assign a standardized geography
    • •Extract location evidence from SAR narratives and link place names to normalized geography records
    • •Rank competing candidate locations, generate confidence tiers, and produce attribution rationales
    • •Route high-confidence SARs for straight-through processing and triage ambiguous cases for human review

    Operating Intelligence

    How SAR Geographic Attribution Review runs once it is live

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence91%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

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