Opinions expressed by Digital Journal contributors are their own.
Cybercrime is on the rise. In 2023, the estimated number of cybercrime incidents in the United States rose to 880,000, resulting in $12.5 billion in losses. Such incidents can cause significant financial damage to customers, erode trust in businesses and compromise sensitive data.
As cyber criminals have taken to using increasingly advanced tactics to scam businesses and their customers, enterprises have also turned to AI to combat digital fraud. In fact, while cybercrime may be on the rise, the growth of AI tools focused on addressing fraud provide a meaningful path forward.
1. Anomaly detection
AI has become increasingly advanced in detecting behavioral anomalies linked to digital fraud. Because AI is capable of analyzing large quantities of data in real-time, it is able to quickly evaluate how current activities compare to past trends and behaviors. By proactively monitoring inputs across an enterprise system, AI can be trained to identify common concerns such as traffic surges linked to DDoS attacks or suspicious activity on a customer account.
These capabilities are especially important given the sheer quantity of data that most enterprise businesses now generate. The scale of information and digital interactions has reached a point that is increasingly difficult for older technologies (and impossible for humans) to keep up with. AI ensures that anomalies don’t go unnoticed.
This level of detection is especially valuable on an individual user level. AI can identify unusual activity on user accounts (such as a change in purchasing activity) to identify if an account has been compromised.
2. Real-time threat identification
A recurring issue in digital fraud is the amount of time it typically takes companies to identify and respond to fraud events. Hackers are often able to spend weeks or even months inside an enterprise network completely undetected, resulting in a significant delay before companies recognize and respond to a data breach — and by then, it is often too late. Companies are then subjected to credit card chargebacks, cancelled memberships and lost revenue as customers lose trust in the brand.
AI represents a powerful step forward in its ability to identify and respond to a variety of threats in real time, before customers or businesses suffer serious harm. For example, tools like Memcyco proactively defend against brand impersonation attacks by continually monitoring for site reconnaissance attempts, giving the AI the ability to instantly identify when a fake website goes live and then deploy warning alerts to customers that visit the fake site to prevent them from getting scammed.
The ability to address threats in real time is transformational for enterprises, helping to significantly limit the scope of damage caused by digital fraud.
3. Vulnerability assessments
AI security isn’t just reactionary — it is also helping businesses become more proactive in improving their digital security profile. As enterprises rely on more and more software products to run their business, the vulnerabilities associated with their software expand exponentially. In fact, it is estimated that businesses use an average of 130 SaaS applications, all of which require proactive patch management and security upgrades to avoid becoming a potential liability.
AI vulnerability management functionality directly addresses this by scanning enterprise software tools and networks for potential vulnerabilities and then recommending possible solutions to address discovered issues. This could include the AI helping with patch management to keep software up to date or identifying software that is no longer being updated and therefore should no longer be used by the organization.
Automating basic security tasks based on the findings of a vulnerability assessment can then give digital security professionals greater ability to focus on higher-level tasks and implementations that will protect the enterprise from fraud attempts.
4. Continual improvement through machine learning
Machine learning plays a crucial role in these security-focused AI tools. Thanks to machine learning, AI tools focused on combatting digital fraud are able to undergo continuous improvement as they consume more data. Leveraging an enterprise’s historical data alongside the constant flow of real-time information enables AI to become even better at identifying anomalous behavior, active threats and potential vulnerabilities.
With more data, the AI can become better at understanding the contextual relationships between different actions or behaviors and fraudulent events. This enables the AI to identify and stop more sophisticated fraud attacks and instill a higher level of protection for the business. This also allows the AI to perform increasingly complex security tasks with less human supervision so enterprise leaders can focus more on big picture strategic activities.
As this occurs, businesses with a tighter security profile will be better able to ensure the online safety of their customers while also keeping operational costs related to digital security at a manageable level.
A path forward for combatting digital fraud
While the rise of AI has certainly introduced new challenges in the realm of digital fraud, the growth of AI tools designed to combat these bad actors also gives cause for hope.
With advanced tools that are better at identifying and addressing threats in real time (in addition to learning so they can become even more effective), enterprises can implement an effective system that helps their customers and themselves. Enterprises that use AI to combat digital fraud can build greater trust with their target audience while simultaneously protecting themselves from financial losses.
