Hi all this is the first post for me here. I will be writing on different topics on Financial Technology on introductory level and share some of my ideas. :)
Cybersecurity is a key challenge that can threaten the momentum of the rise of Fintech. Professionals have to be prepared for threats such as malware, economic cybercrimes, stealing of privacy, and system intrusion. With the cross-border, open-platform, highly-interconnected nature of this virtual dimension, the impacts of cybercrimes are far-reaching, and would require integral effort from all parties involved to protect stakeholders’ information securely in order to effectively combat cybercrimes.
According to a recent study by F5 and The Asian Banker, 84 percent of financial firms now rank cyber threats as one of their top business risks. We could see the uprising risk of cyberattacks particularly in financial sector.
Financial data is a premium target for hackers, offering clear financial incentive for their malicious attacks. And, as hackers become increasingly sophisticated, banking organizations are more and more at risk.
The increasing dependence of cyberspace in digital information technologies not only increase the convenience and efficiency of the financial industries but also increase the vulnerability of it. It is extremely important to safeguard the financial information and therefore much effort is paid to form the defence line.
Machine learning as a threat and defence
The combination of machine learning and big data has enormous promise in fintech, and has motivated the creation of plenty of startups. The interplay between machine learning and cybersecurity is complex. Machine learning is going to be a key factor in cybersecurity in near future.
http://www.infotechlead.com/security/can-machine-learning-cybersecurity-50357
Bot could go either ways on cybersecurity
Botnets are a group of bots that attackers organize and run together to launch attacks through remote systems. On the other hand, system designers should pay more attention to the biases and can be inadvertently built into machine learning solutions that can end up creating or masking cybersecurity vulnerabilities down the road.