Like most types of crime, fraud has been around for centuries. Forgery, cons, and schemes have been around since long before computers were invented, but the digital age has brought fraud to a new level of sophistication and globality. As worldwide E-commerce escalates into a multi-trillion-dollar industry, cases of identity theft per year are in the millions, and losses from fraud are well into the billions.
Fortunately, creators of Artificial Intelligence (AI) solutions for fraud detection have been developing methods for identifying, investigating, and preventing fraud. One of the most useful approaches is to create algorithms that analyze and learn from patterns across documented cases of fraud. This data is used to identify patterns of fraud detection in a range of settings, drawing investigators’ attention to the possibility of criminal activity.
In its essence, the process is not much different from the way that human analysts and investigators have been conducting research for decades. However, AI computing can multiply the efforts of humans exponentially in terms of speed, accuracy, and sheer volume of data when it comes to fraud detection technology. Some of the more advanced algorithms can recognize and interpret data that a team of analysts could not identify or process even if they had all the time in the world.
One of the crucial advantages of AI and Machine Learning (ML) is speed. While it’s important that these systems can find and process information faster than human analysts, it’s just as important that they can also learn, change, and adapt much faster. ML mimics a sentient being’s ability to take in new information, make new observations and connections, and change its behavior based upon what it has learned.
When it comes to the ever-changing world of digital fraud, ML systems can identify new trends and practices used by fraudsters long before those changes are noted by human investigators. This type of adaptive thinking makes response times to threats extremely quick as the machines effectively think and learn for themselves in order to stay less than a half step behind the criminals.
When applied within an organization’s systems, AI for fraud detection can identify and prevent virtually all types of fraud by recognizing details of communications and transactions that are even slightly outside of the parameters of legal, standard activities. By recognizing facets of activity that would be virtually imperceivable to the average human, AI can flag attempts at Phishing, payment fraud, identity theft, synthetic theft, account takeovers, and even document forgeries, sometimes immediately after they happen or before they are completed.
One of the most important advantages of these applications is that they can be ubiquitous, operating simultaneously throughout an organization’s systems and effectively keeping watch over every user’s each and every interaction. They act in real time and respond immediately, like a gatekeeper that is omnipresent and virtually omniscient.
At this point in time, the use of AI in fraud detection is in its infancy. However, it’s likely to become more sophisticated and widely used in the coming years. As global internet fraud continues to expand, these AI solutions may become a necessity, perhaps even a standard feature on every computer across the world.
