TLDR:
Vendor roadmaps are full of pre-built AI features: transaction monitoring agents, identity verification copilots, case management assistants and onward. Most of those features need customization before they work for your business. And the most valuable fraud workflows are the ones no vendor will ever ship, because they’re too specific to how one analyst at one company operates.
Consider the fraud analyst at a fintech company running Buy Now, Pay Later (BNPL) abuse prevention across multiple markets. His morning starts with daily KPIs: approvals, maturing fraud, data for assessing days past due, alongside first payment default rates. He slices by income band, credit score, risk grade and product type to find where performance is drifting.
When a metric deteriorates, he digs into the affected cohort, runs historical simulations against rule payloads and tests whether a tighter threshold would reduce defaults without cutting healthy gross merchandise value.
No vendor has shipped a pre-built agent for that workflow. No vendor will. The workflow is too specific to one analyst’s reality, one company’s risk model and one market’s fraud dynamics. The building blocks to automate it already exist inside the AI tools you have access to and skills are the first step in building agentic workflows.
Six Skills to Start With
These skills are starting points, not finished products. Each one describes a workflow in which the structure remains the same, but the inputs change every day. For each skill, the best way to build it is through the interview method: tell your AI tool what the skill should do, and let it ask you detailed questions.
1. Morning briefing builder
Turns yesterday’s transactions and model performance into a daily briefing with trends, anomalies, priorities and recommended next steps. Use it every morning before opening any dashboard to understand the day’s priorities in five minutes instead of scrolling through multiple tabs.
Start the interview with: “Create a skill for my morning fraud briefing. Interview me about what data I review, what trends matter most and how I want the summary structured.”
A dedicated article covers the briefing skill in depth: How to Build an AI Fraud Briefing Skill. It walks through the six components of a well-configured briefing, shows what good output looks like and includes a pre-built skill you can download.
2. Decline spot-check
Pulls a batch of recent declines, groups them by firing rule and flags likely false positives based on signal patterns. Run it every morning as your first task to catch overnight patterns, false positives and rule drift before your stakeholders see the impact.
Start the interview with: “Create a skill for my morning decline spot-check. Interview me about my process.”
We’ve built a skill that you can test out and refine that you can download at the bottom of this article.
3. Network investigation assistant
Pulls linked account and signal data to summarize how accounts, devices, emails, phones, IPs and payment methods connect around a flagged entity. Use it when a case looks coordinated or high-risk, and you need a structured view of how entities connect before escalating.
Start the interview with: “Create a skill that helps me investigate fraud networks from linked account data. Interview me about how I do this today.”
4. Fraud pattern spotter
Turns a reviewed fraud case into a reusable pattern by summarizing the behavior, the key signals that identified it and what analysts should look for next time. Use it when you want to turn one solved case into repeatable detection logic that the team can reference again.
Start the interview with: “Create a skill that helps me identify new fraud patterns from reviewed cases. Interview me about how I do this today.”
5. Rule change justification writer
Turns a proposed rule change into a structured justification with the trigger, evidence, expected impact and the business rationale. Particularly useful when the approval process requires written documentation and stakeholders expect a consistent format.
Start the interview with: “Create a skill that helps me write rule change justifications. Interview me about my approval process and what stakeholders expect to see.”
6. Regulatory mapping assistant
Maps internal rules and controls to regulatory requirements, enabling teams to demonstrate coverage and identify gaps. Use it when documenting compliance coverage, preparing for an audit or checking whether new rules support required regulatory controls.
Start the interview with: “Create a skill that maps our rules to compliance controls. Interview me about the regulations we need to support and how our risk team works today.”
What a Real Skill Interview Looks Like
The interview method sounds abstract until you see it in action. Here’s an actual exchange between a fraud analyst and their AI tool, building the decline spot-check skill:
Analyst: I want to build a skill for my morning decline spot-check. I pull the last 24 hours of declines, look at which rules fired, flag anything that looks like a false positive and note any patterns. Interview me about my workflow so you can build the skill instructions.
AI: Let’s start with scope. When you pull declines, do you look at all declines across every action type, or do you focus on specific ones?
Analyst: I monitor three main ones: account_registration, transaction_deposit and transaction_withdrawal. I don’t look at card_payment declines because a separate team handles that.
AI: Do you pull all declines from the last 24 hours, or do you filter by score range?
Analyst: All of them. Usually 80-200 declines per day across the three action types. I group them first, then scan.
AI: How do you group them?
Analyst: By action type first, then by firing rule within each action type. So I’d see “account_registration / rule: new_email_domain” with a count of how many declines hit that combination. The most common ones are new_email_domain, high_velocity_per_device, and datacenter_ip.
AI: What counts as a false positive in your environment? What signals tell you a declined transaction might actually be legitimate?
Analyst: If the email is old, over a year, that’s usually legitimate. If the device shows up on only one account in our system, that’s a good sign. If the phone is verified and linked to only one or two other accounts, that’s legitimate. And IP: if it’s residential and the billing country matches the IP country, that’s fine. But if it’s a data center or a TOR browser, I’d never call that a false positive.
AI: So you’re layering signals. A datacenter IP address, a brand-new email, and high velocity are a warning signal.
Analyst: Exactly. The skill needs to understand signal layering.
AI: What happens when you find a cluster of declines with the same rule firing? Like 50 account_registration declines all on the new_email_domain rule in the last 24 hours?
Analyst: That’s a pattern worth investigating. It could be a bulk credential stuffing attack, or it could mean our email age threshold is too strict and we’re declining legitimate users. If I see a spike like that, I flag it as “investigate” and manually pull a sample to understand what’s happening.
AI: What about the opposite: a rule that fired zero times yesterday when it normally fires 15-20 times per day?
Analyst: The skill needs to consider a few scenarios. The rule might be disabled or broken, which is an obvious first check.
But it could also mean the rule worked. Some rules target specific fraud networks. When the rule successfully declines every transaction from that network, the fraudsters abandon the strategy. Traffic stays the same but the fraud signals disappear, and the rule stops triggering because there’s nothing left to catch.
The skill should flag both scenarios: “Rule X fired zero times yesterday (normally 15-20/day). Possible causes: rule disabled/broken, or targeted network has been disrupted. Check with the rules team and compare against overall volume trends for this action type.”
AI: What does your spot-check output look like?
Analyst: An executive summary at the top: “All normal” or “two patterns worth investigating.” Then a breakdown by action type showing the top three rules that fired and their counts. At the bottom, any specific transactions I want to drill into.
AI: Do you pull the actual transaction details for the ones you want to drill into?
Analyst: Usually three to five per morning. I review the full customer profile, including email, phone, device history and previous transactions, to determine whether the decline is a one-off or part of a larger pattern.
AI: What would make the skill most useful to you?
Analyst: If it could just ask “morning check” and get back a structured report: 24-hour declines, summarized by rule, anomalies called out and detailed t transaction data for the suspicious clusters. Something I can act on immediately.
AI: One last thing. Are there action types where you want special handling?
Analyst: Registration is my top priority because those are revenue-blocking. If we’re declining good signups, that’s immediate revenue loss. Deposit and withdrawal declines matter but they’re secondary.
AI: Let me draft the skill instructions based on this, and you can test it against real data tomorrow morning.
Notice what happened. The analyst described a workflow in two sentences. The AI asked 12 questions. The answers captured signal-layering logic, edge-case handling, output-format preferences and priority ranking that the analyst wouldn’t have thought to write down on their own. That’s the value of the interview method: it surfaces operational knowledge that exists in the analyst’s head but has never been documented.
Get the Pre-Built Agentic Skills
We’ve built complete, tested versions of the high-impact skills so you don’t have to start from scratch. All skills are ready to be customized and pasted into Claude, ChatGPT or Gemini.
Skills are organized by what they need to run. MCP skills connect directly to your SEON data. Agnostic skills work with any fraud data you provide.
Works with SEON’s MCP server
Requires a SEON account and MCP connection
| Skill | What it does |
| Fraud daily briefing | Synthesizes overnight transaction activity into a structured morning briefing. Surface volume trends, score anomalies and clusters worth investigating before your first meeting. |
| Transaction decline spot check | Pulls recent declines, groups by firing rule and flags likely false positives based on signal layering. Calls out rules that fire above or below the expected frequency. |
| Multi-accounting detector | Maps shared signals across accounts to surface coordinated multi-accounting patterns. Returns tiered similarity scoring across device, contact and behavioral signals. |
| False negative hunter | Scans approved transactions for clusters that match known fraud patterns. Identifies rings that passed through without triggering a decline. |
| False positive hunter | Audits recently declined transactions against your active ruleset to identify likely false positives. Surfaces accounts where signal layering suggests a legitimate user, not a fraud pattern, triggered the decline. |
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Works with any data source
Works with any AI tool and any data source
| Skill | What it does |
| Fraud OSINT | Structures an open-source investigation from a name, email or identifier. Returns a layered profile across public sources. |
| AML OSINT | Runs AML-focused open-source research on an entity or transaction. Returns adverse media, sanctions exposure and network connections. |
| Fraud & AML typology classifier | Identifies ACAMS/FATF typologies from a transaction description or alert. Returns the typology match, confirming signals and recommended next steps. |
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What to Build Next
Each skill compounds on the last. The decline spot-check teaches the AI your false-positive criteria. The morning briefing teaches it your baseline expectations. The network investigation assistant teaches it how you trace connections. After a month, AI isn’t answering ad-hoc questions. It’s running your methodology, calibrated to your environment, across every workflow you’ve encoded.
The analyst who builds five skills spends 30 minutes on work that used to take half a day. The advantage compounds because every skill gets more precise the more you use it and refine it. The interview takes 15 minutes. The skill runs every day after that.
Next in the series: How to Build an AI Fraud Briefing Skill — a deep-dive into the single highest-value skill, including the six components of a well-configured briefing and a pre-built skill you can download.
Prerequisites: How to Build Your First AI Skill (the interview method and best practices).
Frequently Asked Questions
Start with a morning briefing builder (daily synthesis of overnight activity) and a decline spot-check (batch analysis of recent declines grouped by firing rule). These two skills cover the workflows that consume the most analyst time each day. After those, network investigation, fraud pattern identification and rule change justification skills are the most common next steps.
SEON provides pre-built, tested skill files for the two most commonly requested workflows: the fraud investigation morning briefing and the decline spot-check. Both are ready to customize and paste into Claude, ChatGPT or Gemini. Download the skills here →
Build a decline spot-check skill using the interview method. Tell your AI tool: “Create a skill for my morning decline spot-check. Interview me about my process.” AI will ask about your action types, grouping methodology, false positive criteria, edge cases and output format. The resulting skill pulls recent declines, groups by firing rule, flags anomalies and outputs a structured report you can act on immediately.
Common workflows include: morning briefings (volume trends, score anomalies, queue status), decline spot-checks (rule-level analysis of overnight declines), network investigations (mapping connected accounts through shared signals), fraud pattern documentation (turning solved cases into reusable detection logic), customer communications (drafting restriction and verification messages), rule change justifications (structured evidence for stakeholder approval) and regulatory mapping (matching internal controls to compliance requirements).
