New Azure Architecture – Detect mobile bank fraud

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This post has been republished via RSS; it originally appeared at: Microsoft Tech Community - Latest Blogs - .

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:

 

Let's dig into the architecture:

 

MobileBankFraud_HighLevel-Architecture.png

 

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.

 

You can find the article here, on the Azure Architecture Center:

 

 

 

Special thanks to the Engineers who wrote this:
Kate Baroni
Michael Hlobil
Cedric Labuschagne
Frank Garofalo
Shep Sheppard

And thanks also to our editor/tech writer, Mick Alberts.

 

Remember to keep your head in the Cloud!

 

Ed

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