FIDO WebAuthn allows the use of FIDO Authentication by online services through a standard web API that can be integrated into browsers and associated web platform infrastructure. It is a collaborative project that began with specifications submitted to the W3C by the FIDO Alliance and then refined and completed by the wider FIDO and W3C communities. In March 2019, WebAuthn was recognized as an official web standard. It is presently supported on the Windows 10 and Android operating systems and the web browsers Google Chrome, Mozilla Firefox, Microsoft Edge, and Apple Safari. WebAuthn enables users to access their online accounts through their chosen device. Web services and applications may and should enable this capability to provide their users with a more convenient login experience through biometrics, mobile devices, and or FIDO security keys, all of which provide much more security than passwords alone.
Fraud prevention is the process of developing a plan for identifying fraudulent transactions or banking activities and preventing them from inflicting financial or reputational harm to the consumer or financial institution (FI). As online and mobile banking grows more popular and financial institutions digitize, a robust fraud protection strategy will become even more critical.
Fraud prevention and cybercrime are inextricably linked and constantly evolving. While fraud prevention experts strive to create new authentication and detection technologies, criminals network, monetize, and exchange information on the Dark Web. Nowadays, fraudsters use sophisticated methods and software to carry out their fraudulent operations. While fraud protection technology has advanced significantly and continues to do so, it is critical to be aware of fraudulent techniques and learn how to avoid them.
At present, the rising trend in fraud detection and prevention is a strong emphasis on machine learning. Machine learning is the process of using artificial intelligence to enhance a system without explicitly programming it to do so. Unsupervised machine learning makes use of anomaly detection to ascertain what is normal and odd about a transaction. The model is trained using historical data on fraud using supervised machine learning. As a result, it can determine if a routine or odd transaction is likely to be fraudulent by automatically giving a fraud score in real-time.