Windows 10: Fraud account in my name taking money from my bank

GraysMedic1 · Feb 4, 2022

Dear SirsI have written several times now regarding payments of £12.99 being taken from my bank account since March 2021. I stopped the payments in November 2021.You keep sending me reminders to pay. This is an xbox account. When i click in, it indeed takes me to my Microsoft account which is clearly inactive. During my investigation of this, I found an account registered as HyacinthCafe270This account has my name and bank details, as is the account where the active Xbox subscription is operating from. This is NOT my account. I require the total of £87.92 taken from my bank to b

:)

Brink · Feb 4, 2022

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 · Feb 4, 2022

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

Taffy087 · Feb 4, 2022

bank account (UK) was hacked five days ago. I was a victim of this (UK) TV Licence fraud email.

PS One of the hackers set up a bank loan for hundreds of pounds - which he/she/they have just transferred into my bank account! I'm taking legal advice on whether I can keep it.

What has been shocking is that one major UK provider of loans/credit cards aren't members of a major UK anti-fraud register called CIFAS, or don't bother to check it for every application. Had they done so,
they would have seen that there is a flag against my name so they should have checked with me directly on an agreed secure line before proceeding.

Presumably details of my bank account and home address are now for sale on the 'dark web' and some have paid for those details - the info won't help them as my bank account has been closed and a new one opened with new account/sort details. And another
fraudster stupidly put his real address on four fraudulent applications so the police now have him in their sights.

I have suffered no financial loss - instead of taking small amounts, which is the usual trick as they're often overlooked by account holders, the fraudsters tried to take four huge amounts, more than I had in this particular account, so these were
blocked.

Anyone in the UK hit by fraud should notify Action Fraud.

Windows 10: Fraud account in my name taking money from my bank

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