Jasmine Birtles
Your money-making expert. Financial journalist, TV and radio personality.

Banks are just about the most interconnected of all organisations. To function, they need to be robust, and to implement measures that defend their systems against online attackers, and every form of fraud. By being proactive, they can put themselves on the right side of regulators and earn the trust of the average consumer. This is often a matter of ensuring that every component of a bank’s internal network can be monitored and maintained.
The right network intelligence allows a bank to observe patterns of traffic in real time. This allows the organisation to recognise abnormalities and act swiftly to address them. If an account has been compromised, it might be that the right platform can flag the situation and thereby limit the damage.
The quality of the monitoring tools can make a big difference, here. Traditional methods might not leverage modern techniques, which often means missing out on many of the advantages that come with them.
Through the right network intelligence, banks can obtain a tighter grip on money laundering, and observe the state of their critical infrastructure. This can help prevent an avoidable malfunction from putting the bank’s online services out of action. Since downtime can be so corrosive to the reputation of a bank, this is essential.
Network intelligence isn’t just a benefit in real time. It can also be used retrospectively, to help a bank identify the root cause of a particular failure. A detailed audit trail can often be beneficial when interacting with regulators, and demonstrating that the right steps were taken in a given situation (if, indeed, they were).
Network intelligence isn’t just something that matters in the here and now. It’s almost certain to evolve considerably, as machine-learning-based strategies for data analysis become more sophisticated, and the use of cloud computing becomes more and more widespread. Through artificial intelligence, banks may become better at anticipating where the threats are likely to emerge, and making better strategic decisions when it comes to thwarting them.
For example, you might consider the problem of fraud detection. A machine-learning algorithm might be able to detect cases of fraud more reliably than a human being. But what really matters is that it can flag up the potential for fraud very quickly, before passing those cases onto a human analyst. This means that in most cases, the security measures employed will be less intrusive, and that the digital ‘friction’ perceived by the end user is slowly driven down. The result might be a customer experience that’s smoother and more pleasant.
Disclaimer: MoneyMagpie is not a licensed financial advisor and therefore information found here including opinions, commentary, suggestions or strategies are for informational, entertainment or educational purposes only. This should not be considered as financial advice. Anyone thinking of investing should conduct their own due diligence.