New Azure Architecture - Detect mobile bank fraud

August 25, 2022

In a typical case of online fraud, the thief makes multiple transactions, leading to a loss of thousands of dollars. That's why fraud detection must happen in near real-time.

This article presents a solution that uses Azure technology to predict a fraudulent mobile bank transaction within two seconds. We've built it with customers.

Read the article here:

Detect mobile bank fraud - Azure Architecture Center

Let's dig into the architecture:

An event-driven pipeline ingests and processes log data, creates and maintains behavioral account profiles, incorporates a fraud classification model, and produces a predictive score. Most steps in this pipeline start with an Azure function. A model training workstream combines on-premises historical fraud data and ingested log data. Azure Data Factory orchestrates the processing steps. We use Azure Logic Apps to connect and synchronize to an on-premises system to create a fraud management case, suspend account access, and to generate a phone contact.

In the article you'll find:

Information about the top challenges: Rare instances of fraud and rigid rules.
Operational context: The key questions we asked and how fraud is committed in the operational environment.
Compromise matrix: See the methods used, data taken, and patterns for several types of fraud, including Credential, Device, Financial, and Non-Transactional compromises.
A detailed dataflow of the above architecture.
Data pipeline and automation: What happens in the two seconds, in order to catch the compromise.
Event processing: Architecture and dataflow that explains in detail the fundamental interactions for an Azure function within this infrastructure.
Data pre-processing and JSON transformation.
Near real-time data processing and featurization with SQL Database.
Event schema management.
Feature engineering for machine learning.
AutoML: It automates the time-consuming, iterative tasks of machine learning model development.
Data imbalance: In a fraud dataset, there are many more non-fraudulent transactions than fraudulent transactions.
Model training with a code sample!
Model evaluation: The account-level metrics are described in a table.
Model operationalization and retraining.
Components: Direct links to all the Azure services used in this solution.
Technical considerations: Skill sets and Hybrid operational environment.
Security considerations: Includes a Networking Security Architecture and a security baseline recommendations matrix.
Scalability considerations.