The majority of fraud is now committed online. Be it for financial gain or to steal sensitive information, it continues to be a significant threat to businesses and their customers. Most banks & financial firms traditionally have separate fraud and cybersecurity teams. However, to effectively combat the rise of cyber fraud, the key is to unite the teams, creating a ‘fusion centre’, enabling the teams to share systems, processes, and data. Whereas separate teams would view incidents from their own viewpoints, using their own data, fusion teams offer a more holistic and collaborative approach which produces superior fraud prevention solutions.
Whether a business uses the cloud or relies on its own data centre, it is still vulnerable to fraudulent attacks. However, with cloud adoption and data streaming companies can unlock more advanced protection capabilities. Historically, businesses focused on identifying fraud through anomalies in transaction patterns, but this method is limited in success, and often reactive, meaning money has already been taken by the time the crime has been flagged. With the introduction of cloud capabilities, AI and machine learning (ML) technology can be integrated into fraud prevention systems, drastically improving their effectiveness.
Real time data streaming enables a new level of data analysis, giving businesses real time access to previously unexplored contextual information, such as the number of failed attempts before a successful login, an unusual login location, or unrecognised device used. By streaming these events through a data streaming platform, it is possible for AI and ML to gain enough context to detect a compromised account in milliseconds, suspending it before a fraudulent transaction can take place. To be the best in their class, it’s crucial that organisations utilise AI, ML and contextual information to drive their fraud prevention strategy. This is the key to stopping fraud before a single penny is lost, giving businesses real time, effective and automated protection.