Windows 10: Microsoft Fraud

Adrienne Ross · Aug 8, 2024

Someone used my Microsoft account auto-pay feature with PayPal to order a laptop that was delivered to someone in another state. I have filed a complaint with Microsoft, FedEx who delivered the laptop and PayPal. I cannot get any answers or updates from Microsoft and PayPal is coming after me for the payment. I get no answers or updates from Microsoft, just a continuous loop of people telling me to contact PayPal.

:)

Brink · Aug 8, 2024

Microsoft AI solution to detect mobile bank fraud in less than 2 secs

The future of mobile banking is clear. People love their mobile devices and banks are making big investments to enhance their apps with digital features and capabilities. As mobile banking grows, so does the one aspect about it that can be wrenching for customers and banks, mobile device fraud.

Problem: To implement near real-time fraud detection

Most mobile fraud occurs through a compromise called a SIM swap attack in which a mobile number is hacked. The phone number is cloned and the criminal receives all the text messages and calls sent to the victim’s mobile device. Then login credentials are obtained through social engineering, phishing, vishing, or an infected downloaded app. With this information, the criminal can impersonate a bank customer, register for mobile access, and immediately start to request fund transfers and withdrawals.

Artificial Intelligence (AI) models have the potential to dramatically improve fraud detection rates and detection times. One approach is described in the Mobile bank fraud solution guide. It’s a behavioral-based AI approach and can be much more responsive to changing fraud patterns than rules-based or other approaches.

The solution: A pipeline that detects fraud in less than two seconds

Latency and response times are critical in a fraud detection solution. The time it takes a bank to react to a fraudulent transaction translates directly to how much financial loss can be prevented. The sooner the detection takes place, the less the financial loss.

To be effective, detection needs to occur in less than two seconds. This means less than two seconds to process an incoming mobile activity, build a behavioral profile, evaluate the transaction for fraud, and determine if an action needs to be taken. The approach described in this solution is based on:

Feature engineering to create customer and account profiles.

Azure Machine Learning to create a fraud classification model.

Azure PaaS services for real-time event processing and end-to-end workflow.

The architecture: Azure Functions, Azure SQL, and Azure Machine Learning

Most steps in the event processing pipeline start with a call to Azure Functions because functions are serverless, easily scaled out, and can be scheduled.

The power of data in this solution comes from mobile messages that are standardized, joined, and aggregated with historical data to create behavior profiles. This is done using the in-memory technologies in Azure SQL.

Training of a fraud classifier is done with Azure Machine Learning Studio (AML Studio) and custom R code to create account level metrics.

Recommended next steps

Read the Mobile bank fraud solution guide to learn details on the architecture of the solution. The guide explains the logic and concepts and gets you to the next stage in implementing a mobile bank fraud detection solution. We hope you find this helpful and we welcome your feedback.
Click to expand...

Source: Two seconds to take a bite out of mobile bank fraud with Artificial Intelligence | Blog | Microsoft Azure

Brink · Aug 8, 2024

Microsoft AI solution to detect mobile bank fraud in less than 2 secs

The future of mobile banking is clear. People love their mobile devices and banks are making big investments to enhance their apps with digital features and capabilities. As mobile banking grows, so does the one aspect about it that can be wrenching for customers and banks, mobile device fraud.

Problem: To implement near real-time fraud detection

Most mobile fraud occurs through a compromise called a SIM swap attack in which a mobile number is hacked. The phone number is cloned and the criminal receives all the text messages and calls sent to the victim’s mobile device. Then login credentials are obtained through social engineering, phishing, vishing, or an infected downloaded app. With this information, the criminal can impersonate a bank customer, register for mobile access, and immediately start to request fund transfers and withdrawals.

Artificial Intelligence (AI) models have the potential to dramatically improve fraud detection rates and detection times. One approach is described in the Mobile bank fraud solution guide. It’s a behavioral-based AI approach and can be much more responsive to changing fraud patterns than rules-based or other approaches.

The solution: A pipeline that detects fraud in less than two seconds

Latency and response times are critical in a fraud detection solution. The time it takes a bank to react to a fraudulent transaction translates directly to how much financial loss can be prevented. The sooner the detection takes place, the less the financial loss.

To be effective, detection needs to occur in less than two seconds. This means less than two seconds to process an incoming mobile activity, build a behavioral profile, evaluate the transaction for fraud, and determine if an action needs to be taken. The approach described in this solution is based on:

Feature engineering to create customer and account profiles.

Azure Machine Learning to create a fraud classification model.

Azure PaaS services for real-time event processing and end-to-end workflow.

The architecture: Azure Functions, Azure SQL, and Azure Machine Learning

Most steps in the event processing pipeline start with a call to Azure Functions because functions are serverless, easily scaled out, and can be scheduled.

The power of data in this solution comes from mobile messages that are standardized, joined, and aggregated with historical data to create behavior profiles. This is done using the in-memory technologies in Azure SQL.

Training of a fraud classifier is done with Azure Machine Learning Studio (AML Studio) and custom R code to create account level metrics.

Recommended next steps

Read the Mobile bank fraud solution guide to learn details on the architecture of the solution. The guide explains the logic and concepts and gets you to the next stage in implementing a mobile bank fraud detection solution. We hope you find this helpful and we welcome your feedback.
Click to expand...

Source: Two seconds to take a bite out of mobile bank fraud with Artificial Intelligence | Blog | Microsoft Azure

hurricane51 · Aug 8, 2024

Edge Search Does Not Permit Change of Region

I got these suggestions from the Microsoft Community forum.No. 1 worked for me:

Thank you for posting the query on Microsoft Community. I am glad to assist you on this.
We have reproduce the issue here and we are able to access non-regional search for google using Microsoft Edge.
I would suggest you to try the below steps and check if it helps.
Step 1:
Clear browsing data option of Microsoft Edge and check if you face the issue. To do so perform the steps below.

Click on the More actions icon next to the feedback icon present on top right corner of the Project Spartan homepage.

Select Settings and click on Choose what to clear.

Check the boxes Browsing history, Cookies and saved website data and Cached data and files and click on Clear.

Step 2:
I suggest you to create a new user account and check if the issue occurs.

Go to Settings.

Choose Accounts and then select Family and other users.

Select add someone else on this PC.

Enter a user name and hit next.

Click on Finish.

Sign out from the current Account and Log into the new account.

Step 3:
It could also happen because of network issue. I suggest you to try with different network connection and check if it helps.

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