Cybersecurity continues to be a major concern for businesses of all kinds. This is no surprise, considering that it’s on the rise: According to a recent report by Accenture, security breaches have increased by 11 percent since 2018 and by 67 percent since 2014. And it is also no surprise that increasingly, artificial intelligence (AI) and machine learning (ML) are being leveraged to bolster cybersecurity in general, as well as to shore it up on the payment security front.
What are AI and ML Anyway?
Artificial intelligence lets computers and machines mimic the perception, learning, problem-solving, and decision-making capabilities of the human mind, according to IBM Cloud Education. In “computer science-speak,” AI refers to any human-like intelligence exhibited by a computer, robot, or other machines. In “popular usage, it refers to the ability of a machine or computer to mimic the capabilities of the human mind via algorithms—learning from examples and experience, recognizing objects, understanding and responding to language, making decisions, solving problems, and combining these and other capabilities to perform functions a human might perform,” like identifying patterns in data.
ML, meanwhile, is a subset of machine learning application that learns by itself. As ML algorithms digest more data, they reprogram themselves to perform the specific tasks they are designed to perform, with increased accuracy. There are two types of ML algorithms: unsupervised and supervised. Unsupervised algorithms learn patterns from untagged data and work on their own to “discover” information, while supervised algorithms allow data to be collected, or data output to be produced, from previous experience.
AI, ML, and Cybersecurity in General
All organizations transmit vast amounts of data—personal and financial information, intellectual property, and more—through networks and online systems. Traditional security software and database protection algorithms have a limited scope and, as a result, can neither predict potential threats to data nor spot them in real-time.
On the flip side, ML algorithms based on AI are designed to intelligently detect and sometimes predict any suspicious activity, preventing data compromises and preserving cybersecurity. As mentioned above, the algorithms are configured to learn continuously, which improves their performance.
AI, ML, and Payment Fraud
In payments, experts say AI is well suited to reduce false positives, decrease and prevent attempts at fraud, and decrease the need for manual reviews of potential payment fraud events. This is because it can, through a combination of supervised and unsupervised ML, discern whether a particular transaction or series of financial activities are fraudulent, and, if so, immediately alert the appropriate parties. It can also address the problem(s) through predefined workflows.
There are other reasons AI is a top weapon against payments fraud. One such reason: the digital footprint or pattern, sequence, and structure of fraud-based cyberattacks make it impossible to detect them using only rules-based logic and predictive models. This was not always the case, but payment fraud schemes have become so much more complex and nuanced than in the past when rules and simple predictive models could pinpoint most types of fraud.
AI also gives end-users the advantage of scale and speed. They have lightning-quick response rates, with one platform said to offer a 250-millisecond response rate for calculating risk scores based on several decades worth of data in a universal data network, according to Forbes magazine. The combination of supervised and unsupervised ML algorithms yields fraud scores that are “twice as predictive as previous methods,” states a recent article in the publication.
Further, the myriad of predictive analytics and ML techniques that fall under the AI umbrella does a highly effective job of finding anomalies in large-scale data sets in a matter of seconds. The larger the quantity of data on which an ML model can train, the more accurate its predictive value.
What’s more, the breadth and depth of data from which an individual ML learns means more than how advanced or complex it happens to be, especially in payments fraud detection, where ML algorithms learn just what legitimate and fraudulent transactions from a contextual perspective. Analyzing historical account data from a universal data network allows supervised ML algorithms to build up a greater and greater level of accuracy and predictability.
A study by Brighterion, a Mastercard-owned provider of AI solutions, demonstrates the growing acceptance of the role AI can play in thwarting the payments fraud category of cybercrime. Of fraud specialists queried for the study, 80 percent whose organizations that use AI platforms said they believe the technology helps to put a lid on payments fraud. Fraud specialists also unanimously agreed that AI-based fraud prevention is extremely effective at decreasing chargebacks.
Additionally, a whopping 80 percent of fraud specialists who participated in the study reported that they have seen AI-based platforms reduce false positives and payments fraud, as well as prevent attempts at such fraud. Nearly two-thirds (63.6 percent) of executives representing financial institutions that have implemented AI tools said they consider it capable of preventing fraud before it occurs.
Payment fraud is not easy to stop, but AI can be an effective pre-emptive strategy to minimize it—and its effects.