The detection of possible fraud in financial systems has been one of the significant challenges of several organizations worldwide. In this sense, enabling the development of robust solutions that allow real-time actions is increasingly important for companies that seek to ensure greater security for their customers in carrying out financial transactions.
This repository presents a reference architecture that allows the development of Machine Learning models to be integrated into a real-time fraud detection platform.
In addition, the architecture also allows a series of asynchronous processes to be triggered near-real-time, aiming to feed monitoring dashboards and execute data enrichment routines that can support the fraud detection process.
We demonstrate routines that perform the calculation of Benford's Law and the construction of graphs to identify Fraud Rings to enable advances in fraud prevention.
For this, several Azure resources are used. Below the Reference Architecture:
Please check the prerequisites and step-by-step instructions for configuring the resources used in the items below.
To use this repository, you need access to an Azure subscription. Below we will show the steps to deploy and configure the services.
While it's not required, a basic understanding of some services used will be helpful for understanding the solution. The following resources can help introduce you to them:
- Azure Machine Learning Overview
- Azure Functions
- Azure Event Hub
- Azure Stream Analytics
- Azure Synapse
- Azure Cosmos DB
Start by deploying the resources to Azure. The button below will deploy All the services and its related resources:
Next you'll need to configure your development environment for Azure Machine Learning. We recommend using an Azure ML Workspace as it's the fastest way to get up and running.
Now you can use the AML environment. Let's train and deploy the Machine Learning model.
We will configure a Stream Analytics Job to consume the inputs from Event Hub and persist the outputs to an Azure Synapse SQL Pool and to a Cosmos DB SQL API. In this way we can use the outputs to feed some other processes.
Configure Stream Analytics Job
We need to load some data to our Cosmos DB SQL API account. The link below provide a process to do that.
Finally, we have to deploy the Functions. Follow the link below to proceed with this task. How to Deploy the Functions
- Develop Profile Analytics Function
- Calculate scores from Graph db, Benford Law, and Profile Analytics
- Integrate scores into Orquestrator function
- Develop new supervised ML models
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