title	Fraud Detection
description	Find transfers that may be fraudulent by looking at payment details and the flow of money between accounts. Then prioritize cases for investigators by both the chance of fraud and the amount at stake.
featured	true
experience_level	advanced
industry	Financial Services
reasoning_types	Graph Rules-based Predictive Prescriptive
tags	GNN Fraud Predict-then-Optimize Classification MILP Multi-Reasoner
sidebar	order 2

Full guide: Explore the Fraud Detection model, run the example, and follow the code.

What this template is for

Payments teams often receive more fraud alerts than investigators can review, and transaction attributes alone can miss suspicious account-network behavior. This template combines network signals with a fraud classifier, then selects the investigations that maximize expected loss averted within a fixed investigator-hours budget. Adapt its transaction data, fraud signals, predictive model, and review constraints for your workflow.

Quickstart

Before you start, install Python 3.10 or later and get access to a Snowflake account with the RAI Native App. Graph, Predictive, and Prescriptive reasoning are in Public Preview; ask your RelationalAI support representative to enable Prescriptive reasoning. Preview features are for evaluation and testing, not production applications. The local demo uses bundled CSVs and runs on CPU without an external dataset or GPU. The template pins relationalai[gnn]==1.27.1 in pyproject.toml.

Ask an administrator to run this SQL before you start. It creates the writable experiment schema used by fraud_detection_local.py and grants the RAI Native App access:

CREATE DATABASE IF NOT EXISTS FRAUD_DETECTION;
CREATE SCHEMA IF NOT EXISTS FRAUD_DETECTION.EXPERIMENTS;

GRANT USAGE ON DATABASE FRAUD_DETECTION TO APPLICATION RELATIONALAI;
GRANT USAGE ON SCHEMA FRAUD_DETECTION.EXPERIMENTS TO APPLICATION RELATIONALAI;
GRANT CREATE EXPERIMENT ON SCHEMA FRAUD_DETECTION.EXPERIMENTS TO APPLICATION RELATIONALAI;
GRANT CREATE MODEL ON SCHEMA FRAUD_DETECTION.EXPERIMENTS TO APPLICATION RELATIONALAI;

Use this sequence to run the bundled example:

1. Download the template

   Download the ZIP, unzip it, and enter the template directory:

   unzip fraud-detection.zip
   cd fraud-detection

2. Create a Python environment

   Create and activate a virtual environment, then update pip:

   python -m venv .venv
   source .venv/bin/activate
   python -m pip install --upgrade pip

3. Install the template

   Install the dependencies pinned in pyproject.toml:

   python -m pip install .

4. Configure your project

   Use the configuration builder in Start building with PyRel to create raiconfig.yaml in the template directory and verify your connection.

   Add this setting to raiconfig.yaml before running the template:

   data:
       ensure_change_tracking: true

5. Run the template

   Run the bundled script from the template directory:

   python fraud_detection_local.py

   The run reports the classifier's ROC-AUC score, ranked alerts, and the audit schedule that fits the investigator budget. The bundled PaySim sample overrepresents fraud for CPU training, so its scores don't estimate real-world detection accuracy. The sample comes from Edgar Lopez-Rojas's PaySim synthetic mobile-money dataset under CC BY-SA 4.0. See data/paysim_mini/LICENSE.txt for attribution and citation details.