Automation library · Content

Detect fraud in user activity with PostgreSQL and OpenAI

Started by another system calling it, and works across Postgres and Slack. It hands the judgement calls to OpenAI. 11 steps, 1 branch: a working afternoon to understand properly.

MediumCalls a model: OpenAI

11 stepsMedium: a working afternoon
1Branch: places the flow splits
0Loops or waits
2Services it connects to

The flow

Starts it 1Model 1Logic 6Service 3

The steps in the order they run, from what starts it.

  1. 1User Activity WebhookWebhookStarts it
  2. 2Request ValidatorCodeLogic
  3. 3Fetch Recent User LogsPostgresService
  4. 4Context BuilderCodeLogic
  5. 5Fraud Rules EngineFunctionLogic
  6. 6AI Fraud InterpreterOpen AiModel
  7. 7AI Response CleanerCodeLogic
  8. 8Risk Decision CombinerCodeLogic
  9. 9Database LoggerPostgresService
  10. 10High Risk FilterIfLogic
  11. 11Slack Alert DispatcherSlackService

How it's built

Starts on
Another system calling it
Steps
11, with 1 branch
Size
Medium: a working afternoon
Connects to
Postgres, Slack
Model
OpenAI

Questions

What does it need to run?

An n8n account, plus your own Postgres and Slack accounts, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on another system calling it.

Does it use AI?

Yes. It calls OpenAI. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Plan an afternoon. 11 steps is enough that you should read each one before running it.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

Before you run it

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 11 steps before you point it at anything that matters.