---
name: fraud-pattern-signal-extractor
description: "Analyzes raw case notes, transaction data summaries, or investigation logs and extracts emerging fraud patterns as structured signals — each with a confidence rating, affected surface, suggested alert-logic change, and decisioning-criteria update. Use when reviewing recent fraud cases, triaging a spike in a specific fraud type, translating investigator findings into detection rules, or preparing a fraud trend brief for risk operations."
argument-hint: "[case-data] [fraud-context]"
allowed-tools: ["Read"]
---

# Fraud Pattern Signal Extractor

## Purpose
Turn unstructured fraud case notes, transaction summaries, and investigation logs into a ranked set of concrete fraud signals — each mapped to a specific alert-logic change and decisioning-criteria update — so risk operations can move from anecdote to deployable detection quickly. Solves the gap between what investigators observe and what the rules engine actually enforces.

## When to Use
Invoke this skill when the user asks to:
- Extract patterns from a batch of recent fraud cases or chargeback investigations
- Diagnose a spike in a specific fraud type (ATO, first-party, synthetic identity, promo abuse, card testing)
- Translate investigator narrative notes into detection rules or alert logic
- Review transaction data summaries for shared attributes across confirmed-fraud cases
- Prepare a fraud trend brief or emerging-threat summary for a risk review
- Identify which existing rules are missing a newly observed fraud behavior

## Inputs Required
Before starting, collect:
- **Source material**: Case notes, transaction data summaries, investigation logs, or chargeback records (paste or describe)
- **Fraud context**: What triggered this review — a spike, a scheduled review, a specific loss event
- **Case outcomes**: Which cases are confirmed fraud vs. suspected vs. false positive (if labeled)
- **Current detection setup**: Existing rules, alert types, or decisioning model in place (high level is fine)
- **Product/channel affected**: Onboarding, payments, withdrawals, card, transfers, etc.
- **Risk tolerance**: Priority between minimizing losses vs. minimizing false-positive friction

If case outcomes are unlabeled, treat all cases as suspected and flag confidence accordingly. If the current detection setup is unknown, generate rule suggestions as net-new logic.

## Instructions

### Step 1: Normalize and Cluster the Source Material
Read all provided material and extract discrete observations. For each case or record, capture:
- Fraud type (ATO, synthetic identity, first-party/friendly fraud, card testing, promo/bonus abuse, mule activity, account farming)
- Entry surface (onboarding, login, payment, withdrawal, profile change)
- Observed attributes (device, IP/geo, velocity, amount patterns, timing, funding source, behavioral tells)
- Outcome label if provided

Cluster observations by shared attributes — group cases that plausibly stem from the same actor, tactic, or vulnerability. Note cluster size (number of cases supporting each grouping).

### Step 2: Extract Signals from Each Cluster
For every meaningful cluster, define a **signal** — a specific, observable, machine-detectable condition. Each signal must include:
- **Signal name**: Short descriptive label
- **Definition**: The precise observable condition (e.g., "3+ distinct cards attempted on one account within 10 minutes, all sub-$2 authorizations")
- **Supporting evidence**: How many cases exhibit it and the shared attributes
- **Fraud type**: Which category it maps to
- **Confidence**: High / Medium / Low, based on case count, outcome labels, and specificity
- **False-positive risk**: What legitimate behavior could also trigger this

Prioritize signals that are specific and observable over broad behavioral hunches. A signal a rules engine cannot evaluate is not a signal.

### Step 3: Rank Signals by Operational Priority
Score each signal on two axes and rank:
- **Loss exposure**: Estimated frequency × average case value (use provided data; estimate conservatively if absent)
- **Deployability**: Can it be turned into a rule now, or does it need new data/tooling?

Rank highest-exposure, most-deployable signals first. Flag any signal that is high-exposure but not yet deployable as a **data gap** requiring instrumentation.

### Step 4: Map Each Signal to a Detection Response
For each ranked signal, specify:
- **Alert logic change**: The concrete rule condition to add or modify, in near-pseudocode (e.g., "IF card_attempts >= 3 AND window <= 600s AND avg_auth < $2 THEN flag=card_testing, action=step_up")
- **Decisioning criteria update**: How this affects the decision — hard decline, step-up auth, manual review queue, velocity limit, hold
- **Threshold rationale**: Why this cutoff — tied to the observed case data, not arbitrary
- **Recommended action tier**: Block / Challenge / Review / Monitor — calibrated to the stated risk tolerance
- **Tuning note**: What to watch after deployment (expected false-positive impact, review-queue volume)

### Step 5: Summarize the Emerging Threat Picture
Write a short synthesis: which fraud type is trending, which surface is most exposed, whether the pattern suggests a single organized actor or diffuse opportunistic activity, and the single highest-priority action. Be direct about what the data does and does not support.

## Output Format
- **Structure**: Threat summary up top → ranked signal table → per-signal detection response blocks → data gaps → recommended next action
- **Length**: 800–1,200 words
- **Format**: Structured document — signal ranking as a table; detection responses as labeled blocks with pseudocode
- **Sections**: Emerging Threat Summary → Extracted Signals (ranked table with confidence + exposure) → Detection Responses (per signal: alert logic + decisioning update + action tier + tuning note) → Data Gaps → Priority Action
- **Tone**: Fraud operations specialist — precise, evidence-bound, no speculation presented as fact; explicitly separates confirmed patterns from hypotheses

## Quality Checklist
Before delivering output, verify:
- [ ] Every signal cites a case count or specific supporting attributes — no unsupported assertions
- [ ] Each signal has a confidence rating tied to evidence strength, not sentiment
- [ ] Every signal maps to a concrete alert-logic change written as an evaluable condition (pseudocode present)
- [ ] Each detection response names a decisioning tier (Block / Challenge / Review / Monitor) calibrated to stated risk tolerance
- [ ] Signals are ranked by loss exposure × deployability — not listed arbitrarily
- [ ] False-positive risk is named for every signal that triggers customer-facing friction
- [ ] Any high-exposure but non-deployable pattern is flagged as a data gap, not buried as a rule

## Notes
- The most common failure mode is over-fitting a rule to a handful of cases — flag any signal supported by fewer than 3 cases as Low confidence and recommend Monitor rather than Block.
- Card testing and ATO signals are usually deployable immediately; synthetic-identity and first-party fraud signals often need new upstream data and belong in the data-gap section.
- When risk tolerance favors low friction, default new signals to Challenge or Review tiers before recommending hard Block — a false decline on a legitimate customer is a real cost.
- Threshold values should always trace back to the observed data (e.g., "cases clustered at 3–5 attempts, so set at 3") rather than round numbers chosen for convenience.

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## How to use this skill

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**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
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