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How to watch TV using the Xfinity Stream app on Roku, Fire TV, or Apple TV?


If you need to add another Xfinity TV to your home but don’t want to order another set-top box, you have a few options. The Xfinity Stream app is available in beta on Roku devices, as well as some Samsung and LG smart TVs, though you must be connected to your home Xfinity Internet to use it. The app isn’t available on Apple TV or Fire TV, but if you’re using a PC, you can use screen sharing to get Xfinity on your TV in a pinch. Here’s how to set it up. xfinity.com/authorize is helpful for activate xfinity on TV and other Devices.

Products used in this guide

  1. Entry-Level Roku Streaming: Roku Express HD ($ 24 at Amazon)
  2. Add Streaming and Better Audio: Roku Smart Soundbar ($ 169 at Amazon)

How to watch TV in the Xfinity Stream app.

The Xfinity Stream Beta App is available on Roku devices, as well as many Samsung and LG smart TVs.

  1. Download the app by searching for Xfinity Stream on your device.
  2. Make sure your device is connected to your Xfinity Internet.
  3. Open the app and select Sign up.
  4. Use another device, such as a phone or PC, for activation code at www.xfinity.com/authorize
  5. Use your remote control to select the content you want to watch. Live TV channels can be found in the Live TV section.

One thing to keep in mind is that this app is only designed to be used in your home. If you were planning on using your streaming device at someone else’s house or taking it on vacation, you’re out of luck.

When you’re out and about, you’ll need to stream via a web browser or use a phone or tablet with the Xfinity Stream App (also available on iOS and Amazon).

Fire TV and Apple TV.

The Xfinity Stream app is not available natively on Fire TV or Apple TV.

Some people have been able to activate and use an APK downloaded from the Xfinity Stream app on Fire TV devices. This requires installing unauthorized Android apps and emulating the tablet or phone environment on your device. It is not known how long it will work or if the apps you download from outside of an official app store are completely safe, so it is not recommended.

One of the most reliable ways to bring your Xfinity service to a TV is to use a PC. This can be done by using a direct connection with something like an HDMI cable or by sharing your screen with your device. You will need a Mac to share your screen with your Apple TV. Google Chrome can be used to easily cast a web browser tab to your TV, although in my experience the audio and video quality can be highly variable even with a solid connection.

Sharing your screen from an Android or iOS device will stop your Xfinity streaming and display an error message.

The Xfinity Stream app is designed to bring your Xfinity service to more TVs in your home with Roku hardware or Samsung and LG smart TVs. If you want to take your service on the go, you will need to use a phone, tablet, or PC to watch. Android and iOS apps do not allow the use of an external monitor or TV, but streaming on a PC can still be displayed on a TV.


Stream at home with a Roku device. Watch on your phone or tablet with the Xfinity Stream app.

Using one of the best Fire TV Stick VPNs can also help you access content that is not available in your local area.



How Machine Learning Can Prevent Credit Card Fraud


How Machine Learning Can Prevent Credit Card Fraud

Machine learning can reduce false positive and quickly detect credit card fraud.

Using traditional methods to detect instances of credit card fraud slows down the process of resolving such issues. The application of machine learning in banking promises to find quicker and accurate solutions for all kinds of financial institutions.

The advent of digitization in banking has introduced several cybersecurity-related issues in such finance-based organizations. For example, reported financial fraud had increased by 104% in the first quarter of 2020, compared to Q1 2019. One of the main types of financial fraud is credit card fraud, which involves hackers accessing an individual’s card and illegally using it. While card-owners can call their banks and freeze the card on encountering this issue, preventing the possibility of such fraud must be the priority for banks and credit card companies. A guaranteed way to address this problem is by using machine learning for fraud detection. This application of machine learning in banking is yet another example of AI being used for finance-related purposes in organizations.

Scanning Purchase Patterns to Detect Fraud


AI-based fraud detection systems closely scan the credit card statements of users continually. AI’s advanced pattern and anomaly recognition come into play by detecting buying patterns that cannot be found with human eyes. For example, if a user doesn’t particularly shop a lot online and suddenly—as per their card statement—starts doing so with some frequency, the system raises a red flag. Similarly, if a user suddenly begins making purchases from stores that are miles away from where they stay, a flag is raised. There are several such parameters. Additionally, the tools based on machine learning in banking can differentiate between a one-off purchase and multiple purchases following the same pattern. In this way, false positives are avoided in credit card fraud detection. Once the system raises red flags after scanning real-time purchases, credit card companies or banks can deploy their investigation team to look into the matter and take the appropriate action before any significant activity takes place with the card.

Using Machine Learning for Fraud Detection

The Denmark-based Danske Bank employs AI for credit card fraud detection. After implementing the technology for the purpose, the bank reduced the number of false positives in fraud detection by 60% and reported a 50% increase in the number of detected cases of fraud.

Banks and credit card companies walk a thin line during credit card fraud detection. They do not wish to incorrectly flag every transaction as fraudulent. At the same time, every case of credit card fraud must be caught immediately. Machine learning in banking is necessary to achieve both these objectives seamlessly.  


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