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    Use-case library
    Treasury & Liquidity

    Payment Fraud & Anomaly Detection

    Score outgoing payments for fraud and anomaly before they release.

    Predictive MLAutonomy: Act on exceptionRisk & ControlMedium effortOrg maturity: AI-EnabledCFO
    What changed

    Predictive models now score every outgoing payment against learned patterns — flagging anomalous amounts, new beneficiaries and duplicate or out-of-policy transactions — and hold the riskiest for review before release.

    How it's built

    Predictive ML (rung 1 of 6). Score, forecast, classify, cluster or detect anomalies from your own history.

    Not an agent: the sequence of steps is fixed in advance, so the whole path can be tested end to end.

    No language model in the path: every step below runs by rule, by a trained model, or by a person.

    Autonomy: T2 · Act on exception
    Acts unattended on the confident majority; routes the rest to a human. Human role: reviews only the routed cases.
    How it is governed
    Model risk management: validation, monitoring, bias testing, periodic revalidation.
    What the certification exams call this
    • Anthropic (Claude Certified Architect – Foundations (CCAR-F))Outside the Claude exams. A trained score is data or a tool result the model reads.
    • OpenAI (OpenAI agent guidance (Agents SDK, Codex, OpenAI Academy))Outside the agent guide. A trained model is a data source or tool.
    • Google Cloud (Generative AI Leader)Machine learning: supervised, unsupervised and reinforcement learning (exam §1.1). source
    • Microsoft (AI-901 (Azure AI Fundamentals) and AB-100 (Agentic AI Business Solutions Architect))Custom AI models: AB-100 asks when one should be created. source

    How Koko classifies AI · the full ladder is taught in AI & Agentic Delivery Foundations.

    Try it

    Illustrative demo

    Worked example of "Payment Fraud & Anomaly Detection" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.

    Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.

    What this run should concentrate on.

    Run live in Claude

    Workflow

    1. 1.RulesIngest the outgoing payment batch with beneficiary and history context.
    2. 2.Predictive MLScore each payment for anomaly (amount, beneficiary, timing, duplication).
    3. 3.RulesHold high-risk payments with the model's reason codes.
    4. 4.RulesRelease clean payments straight through within policy limits.
    5. 5.HumanRoute held payments to treasury for review and disposition.

    Prompt recipe

    Score the attached payment batch for fraud/anomaly risk using beneficiary history and policy. For each flagged payment, explain the trigger (new beneficiary, amount outlier, duplicate, off-policy). Recommend hold or release; never auto-release a flagged payment above [threshold].
    Try in Ask KokoAI
    Controls required
    • High-risk payments held for human review before release
    • Beneficiary changes verified out-of-band
    • Approval limits and segregation of duties enforced
    • Full audit trail of scores and dispositions
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