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CasePilot

AI-assisted fraud case management for phone fraud investigations, helping analysts resolve complex cases faster with more consistent decisions and reduced language-related friction.

The Problem

“Fraud investigators struggle to connect multilingual signals, prioritize high-risk cases, and document consistent decisions fast enough”

Organizations face these key challenges:

1

High manual effort to review call transcripts, notes, SMS evidence, and customer communications

2

Inconsistent analyst decisions across teams, geographies, and experience levels

3

Fragmented signals across KYC, AML, fraud, telecom, and threat intelligence systems

4

Difficulty linking local fraud events into cross-border or multilingual attack patterns

5

Slow triage of large alert volumes with limited analyst capacity

6

Language barriers that delay investigations and reduce evidence usability

7

Weak explainability and auditability when prioritization depends on analyst intuition

8

Manual case write-ups consume significant time and reduce investigation throughput

Impact When Solved

Reduce average fraud case handling time by auto-summarizing evidence and recommending next actionsImprove alert prioritization so analysts focus first on the highest-risk phone fraud casesSurface KYC, AML, sanctions, and fraud indicators from investigator notes, customer communications, and case documentsConnect breach exposure, smishing campaigns, devices, accounts, and identities into a unified fraud network viewSupport multilingual investigations with translation, normalization, and cross-language entity linkingStandardize case documentation and disposition rationale for audit and regulator review

The Shift

Before AI~85% Manual

Human Does

  • •Review call recordings, transcripts, account activity, and prior case notes across sources
  • •Summarize customer interactions and document key facts in the case file
  • •Compare evidence against fraud rules, policies, and past case experience
  • •Decide case disposition and determine follow-up or escalation actions

Automation

    With AI~75% Automated

    Human Does

    • •Review AI-generated case summaries, risk signals, and recommended next steps
    • •Approve or override proposed case dispositions and customer actions
    • •Handle ambiguous, high-risk, or policy-sensitive cases requiring judgment

    AI Handles

    • •Aggregate case evidence from calls, notes, account events, and prior case history
    • •Summarize interactions, extract entities, and highlight fraud indicators or contradictions
    • •Generate structured case narratives, likely dispositions, confidence levels, and rationale
    • •Recommend next-best investigative actions and route cases based on risk or completeness

    Operating Intelligence

    How CasePilot runs once it is live

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence95%
    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

    Technologies

    Technologies commonly used in CasePilot implementations:

    AML/KYC compliance workflow integrationOther
    4 mentions
    Regulatory and internal policy rulesOther
    4 mentions
    Risk scoring or case prioritization logicOther
    3 mentions
    Text analytics/NLP pipelineOther
    3 mentions

    Key Players

    Companies actively working on CasePilot solutions:

    FICOComplyAdvantageFeedzaiSAS Anti-Money Laundering

    Real-World Use Cases

    NLP-driven compliance and risk signal surfacing in KYC/AML operations

    Use language AI to help compliance teams spot risky or important information in text-heavy KYC work so they can focus on the cases that matter most.

    risk signal detection and prioritization from unstructured textconceptually strong but still high-level in the source; no proof of production deployment is provided.
    10.0

    AI-assisted fraud case management and analyst triage

    AI sorts suspicious transactions into the most urgent cases so fraud investigators know what to review first and what to escalate.

    Risk ranking and workflow orchestrationmature operational workflow augmentation; source summary explicitly points to case management processes as likely content.
    10.0

    Cross-border fraud signal mapping across multilingual operations

    Even if fraud clues come from different countries or languages, the bank can connect them through shared accounts, devices, IPs, and money flows to see the same criminal network.

    cross-entity network mappingproposed use case derived from the source’s application guidance for multilingual and cross-border workflows.
    10.0

    Breach-to-smishing intelligence for mobile fraud defense

    It tracks how stolen personal data from breaches gets turned into highly believable scam messages on phones and messaging apps.

    threat pattern linking from source data exposure to downstream attack campaignsactive intelligence offering and practitioner workflow, evidenced by repeated public analysis and webinars on live campaigns.
    10.0

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