AI & Machine Learning In Fraud Detection
Fraud is unfortunate—but it happens. According to the Association of Certified Fraud Executives (ACFE), organizations worldwide lose five percent of their annual revenues thanks to fraud, and small businesses are more susceptible than large corporations. Applying this estimate to the 2016 World Gross Product would translate this figure to global fraud losses of approximately $4 trillion.
Consumers, not just companies, are vulnerable to fraud as well. Now that we live in the digital age, people stand to lose more than money—they might lose their information. Fraud detection and prevention techniques exist, of course, but traditional methods are falling short as technology progresses and attacks become more sophisticated.
Caveats of Traditional Fraud Detection Methods
Current approaches to fraud detection and prevention often depend on sets of “rules” that programmers determine on behalf of a business. If a transaction does not follow those rules, the exchange is marked as suspicious. These rules are, obviously, subject to human error and can only account for so much.
Rules often deal in absolutes—yes and no—which means that technological applications relying on them might let a fraudulent transaction slide if it is cleverly executed, or even flag a legitimate transaction as a scam and affect an innocent customer.
Concerns regarding fraud are understandably growing. Javelin Strategy & Research reports that in 2017, fraud affected eight percent more victims compared to the previous year in the United States, bringing the total to approximately 16.7 million individuals.
The organization notes that a consumer’s digital persona influences their level of fraud risk: offline customers, for instance (such as people shopping at brick-and-mortar enterprises) are exposed to less fraud potential, but they suffer higher losses if they are victimized—and it can take as many as forty days or more to detect fraud.
Online customers, on the other hand, are at higher risk, but 78 percent of victims detected fraud within a week of it occurring—though this is detection, not prevention, so they still had to deal with the aftermath. Traditional digital techniques are better than nothing, but their proclivity for false positives makes them frustrating for both risk managers and affected customers.
Likewise, criminals that steal a consumer’s card information and other details might complete what appears to be a perfectly legitimate transaction from a risk manager’s point of view, but was in fact made without the card owner’s consent.
Many banks and payment gateways are still using outdated fraud detection techniques. Criminals are adapting, which means preventive measures need to keep pace at the very least. Are there any emerging technologies that can keep both consumers and businesses safe from attacks? Fortunately, there is—artificial intelligence and machine learning present a viable solution.
What Makes Machine Learning Better Than Traditional Fraud Prevention Methods?
Artificial intelligence might seem like a recent buzzword, but it has actually been around for decades.
While AI, as people imagine it from science fiction movies, implies machines thinking for themselves, modern applications of basic AI are still dependent on human programmers informing it what to data to look for and what to do with it.
Machine learning, however, is a subdivision of AI that is more on par with what non-computer scientists might imagine it to be; in which a machine can analyze changes in data and improvise accordingly—and thus improve its own performance.
“A human analyst or a human reviewer can only look at a handful of signals at a time and make a determination. But there is enough data out there, and that’s really when machine learning comes into play—because it’s literally able to crunch thousands of signals and look at probabilities or fraud. That’s really where the industry is going from a machine learning viewpoint.”
So, in a business context, machine learning technology would be able to identify potentially fraudulent transactions or attacks with a much higher degree of accuracy than humans or previously employed applications.
Non-ML technologies can be, in a way, overexcited about identifying fraud, which is what causes perfectly legitimate transactions to be rejected.
For instance, it is not uncommon for current fraud prevention strategies to flag attempted high-cost purchases, such as when a consumer wishes to buy many big-ticket items from the same retailer. An identity thief would attempt to steal as much as possible at once, right?
It makes sense, but without proper techniques to double-check, the customer who is genuinely trying to make the purchase grows understandably frustrated (and may even take their business elsewhere; in which case, the company is at a loss).
Machine learning algorithms would not simply think big transaction = fraud. Instead, the technology would analyze thousands of pieces of data to discern whether or not the purchase is legitimate, some of which might mean nothing to a human supervisor. The algorithm could search for details regarding the purchaser’s hardware, their behavior, location, requested shipping address, and more.
If the system notices something suspicious, it can either reject the transaction or send it to a human monitor for further investigation.
What Kinds of Machine Learning Fraud Detection Are There?
While the details are more complex, there are essentially two kinds of machine learning: supervised and unsupervised.
The most common form of machine learning, supervised models, entail training an application by presenting it a robust set of transactions that are labeled as legitimate or fraudulent. The model analyzes the differences between them and infers a function so that when presented with new information, it can properly identify a transaction’s status.
The more data the system ingests at the beginning, the more accurate it will be.
Unsupervised machine learning models are not given pre-labeled data. Instead, these applications assess the data set’s overall structure and deem anything that does not fit its derived instruction set or function as an anomaly.
Due to their practice in pinpointing outliers, unsupervised models are helpful for distinguishing previously unseen fraud techniques.
The most effective ML systems blend both supervised and unsupervised models. Non-AI fraud prevention strategies depend on humans extracting known and unknown data patterns, but machine learning automates this process and adapts as new information appears.
How Machine Learning Benefits Fraud Prevention
So, in what ways is machine learning more effective than typical fraud prevention methods?
A person might be able to identify a pattern or two by looking at a data set, but growing customer bases, the number of different payment methods, and technological advances in cyber attacks result in so much information that it all becomes noise.
Machine learning algorithms, however, can handle enormous amounts of data at any given time—and not only that, but true to its name, machine learning systems learn and become more accurate when presented with more information.
Establishing and employing rule-based systems is time-consuming because ad-hoc rules necessitate manual supervision. Needless to say, machines can process large datasets much faster than humans can.
Speed is one of the primary benefits AI presents in fraud detection: dozens or hundreds of transactions may simultaneously occur at any point, but algorithms can identify which ones are potentially fraudulent within microseconds.
It’s more efficient
Machines are also more suited for performing repetitive tasks than humans. AI systems would conduct a majority of the tedious work, and would only pass on particularly tricky cases to human risk analysts for further review.
ML is also arguably less likely to result in false positives due to the sheer amount and diversity of information that it takes into account.
Machine learning is not intended to replace risk analysts any time soon—it simply grants them a powerful tool to make their jobs easier. When businesses equip risk managers with ML, they can dramatically reduce the costs required to train employees on comprehending and managing traditional fraud detection practices.
Why AI Fraud Detection Still Isn’t Perfect
While AI and machine learning present the next step in fraud prevention, they are nevertheless imperfect and have room to be improved upon. Small businesses have fewer customers than large corporations, and thus have smaller data sets to train their supervised applications with.
An insufficient amount of data will not make ML algorithms as accurate as they can be, but small businesses and their customers still deserve just as high-quality security that large corporations enjoy—especially because fraudsters target them more often, and thus smaller enterprises stand to lose more.
Artificially intelligent technology itself is also plagued by what is known as the “black box problem.” An ML application can make predictions and discern fraud from legitimate exchanges rapidly, but it cannot explain why or how it arrived at that conclusion.
This issue is particularly relevant regarding unsupervised machine learning; any AI technology is only as good as the humans who develop it, so it is imperative to be able to review a system’s logic for faults. If a machine learning algorithm’s process is uninspectable, then how are the people it is supposedly benefiting supposed to know if it is working as it should?
A solution to the black box challenge is known as Explainable AI. Previous attempts to make an artificial intelligence chronicle its processes have involved attaching explanation capabilities post-development, but this means that the system can only detail its calculations after making them. In a fraud prevention context, an AI application called Similarity leverages the only known algorithm that concurrently flags fraud while providing explanations.
Fraudulent practices represent a small fraction of an organization’s overall activity, but losses can be high if they are successful, and criminals are developing increasingly clever methods of deception.
Human risk analysts can only differentiate legitimate from fraudulent transactions to a certain extent. Artificial intelligence and machine learning systems, however, can identify fraud at an incalculably faster rate and process tremendous amounts of data at one time.
Explainable AI and machine learning technology is, arguably, the best alternative to traditional fraud prevention techniques thanks to its ability to improve upon its performance and communicate its reasoning transparently. As such AI applications glean more experience, ML will soon become the standard in fraud detection and prevention.