A Fraud Case Log in Notion or Airtable

Most stores handle fraud reactively and forget it just as fast. A risky order comes in, someone makes a call, and the reasoning vanishes. Three months later the same pattern reappears and nobody remembers what happened last time. A fraud case log fixes this. It is a simple, structured record of every fraud decision your team makes — and over time it becomes one of your most valuable prevention tools.

Notion and Airtable are both excellent homes for this. Airtable gives you database power (linked records, rollups, filtered views); Notion gives you flexible documentation and easy sharing. Either works. This guide gives you a schema, a status workflow, and a linking strategy you can set up today.

Why bother logging cases

A case log pays off in four ways:

• Consistency. Everyone reviews orders the same way when the criteria and past decisions are written down.
• Pattern detection. When you can filter and sort past cases, repeat fraud rings and recurring signals jump out.
• Onboarding. New team members learn your fraud playbook by reading real cases, not a vague policy doc.
• Accountability and audit. If a payment provider or a dispute questions a decision, you have a dated, reasoned record.

Without a log, every reviewer starts from zero. With one, your team gets smarter with every case.

The case-log schema

Here is a reusable field set. Adapt it, but resist the urge to add so many fields that logging becomes a chore — a log nobody fills in is useless.

Core fields:

• Case ID — auto-numbered.
• Date opened — when the order was flagged.
• Order number — the Shopify order, ideally a link (more on this below).
• Order value — helps you prioritize.
• Status — the workflow stage (see next section).
• Risk score — from your fraud tool, if you have one.

Signal fields (multi-select or checkboxes):

• Signals detected — VPN/proxy, Tor, country mismatch, disposable email, mismatched billing/shipping, velocity (many orders fast), high-resale items.
• Traffic type — residential, VPN/proxy, data center.
• Geography — order country and IP country.

Decision fields:

• Action taken — shipped, held, cancelled, refunded, blocked.
• Reviewer — who decided.
• Reasoning — a short free-text note. This is the field that makes the log valuable; make people fill it in.
• Outcome — resolved clean, confirmed fraud, chargeback received, false positive.

Follow-up fields:

• Chargeback? — yes/no, with date if applicable.
• Amount lost / recovered.
• Tags — for grouping (e.g. ring-alpha, subscription-confusion).

A clear status workflow

Statuses turn a flat spreadsheet into a workflow. Keep them few and unambiguous:

1. New — flagged, not yet reviewed.
2. Under review — a human is assessing it.
3. On hold — fulfillment paused pending more info (e.g. waiting on customer verification).
4. Resolved – shipped — judged legitimate and fulfilled.
5. Resolved – blocked/cancelled — judged fraudulent and stopped.
6. Chargeback — a dispute arrived despite the decision; kept open for learning.

Build a Kanban / board view grouped by status so the team can see the queue at a glance. Anything sitting in "New" or "Under review" too long is a fulfillment risk and should be visible.

Linking cases to orders

The log is far more powerful when each case connects to its real order.

• In Airtable: store the Shopify order URL in a URL field, or link to an "Orders" table you sync from Shopify. Linked records let you roll up order value and customer history automatically.
• In Notion: paste the order admin link into the Order field, and optionally relate the case to a customer database page so you can see all cases for a repeat offender.

The two-way link is what surfaces patterns: click a customer and see every case they have ever triggered. That is how you catch someone who was a "false positive" last month and confirmed fraud this month.

Feeding the log automatically

Manual entry is where case logs die. Automate the top of the funnel. Using Zapier or Make, trigger on a flagged order and auto-create a case with the order number, value, signals, and risk score pre-filled — so the reviewer only adds the decision and reasoning, not the busywork.

The quality of those auto-filled signals depends on your detection layer. Shieldy Fraud Filter supplies exactly the fields your log wants — the signals it detected (IP, country, VPN/proxy/Tor, bots), the traffic type, and an AI risk score — while blocking the clearest fraud at checkout so it never needs a case at all. The result is a log full of genuinely instructive decisions rather than obvious junk.

Making the log earn its keep

A log is only an asset if you use it. Build a short monthly review into your routine:

• Filter for confirmed fraud and look for shared signals. If three cases share a country and a proxy pattern, tighten that rule.
• Filter for false positives (held or cancelled but actually legitimate) and loosen the rules that caused them.
• Check chargebacks against your decisions — the ones you shipped that later disputed are your blind spots.

Over a few months this feedback loop measurably improves both your catch rate and your false-positive rate.

A quick note on tooling: plans start at Free ($0), with Enterprise ($8.99/mo) and Shopify Plus ($16.99/mo) for higher-volume stores.

Set up the schema, wire in automatic case creation, and commit to a monthly review. Your fraud team stops repeating itself and starts compounding what it learns. Want richer signals feeding your log? Explore the pricing and give every case the context it deserves.