AI vs. Fraud
Simple, predictive models can no longer keep up with the complexity and scale of fraud. Cybercrime currently costs the global economy over $600 billion a year with most of that being credit card fraud.
There is huge potential for cybercrime to get worse as cybercriminals now can capitalize on unregulated cryptocurrencies to cash out on their criminal online activities. Hence, it becomes imperative for artificial intelligence to reduce fraud. Fortunately, AI machines are becoming powerful enough to thwart complex fraud attempts.
The future of AI-based fraud prevention lies in the combination of supervised and unsupervised machine learning. Supervised machine learning uses a ground truth (having prior knowledge of what the output values should be). The goal of supervised learning is to learn a function that best approximates the relationship between given inputs and outputs in a data set. It is best used for examining events and trends from the past, and hence uses past trends as predictive analysis for the future.
Meanwhile, unsupervised machine learning uses no ground truth, or labeled outputs. Instead, its goal is to infer a structure present within data points. Thus, it is best used for determining anomalies and links between variables. Combining both types of learning allows AI to use the past as a template as well as identify new outliers among data points in real time. With this functionality, AI can stop fraud attempts.
The blend of supervised and unsupervised learning is the way to combat fraud attempts. This new technology can detect credit card fraud in seconds rather than over a six to eight-week period. Chargebacks can be detrimental for businesses so getting ahead of them with this technology is critical.
Additionally, because of the combination of supervised and unsupervised learning, Ai makes it possible to stop newer and more sophisticated attacks such as promotion abuse or collusion among sellers. IBM Watson's Lead Security Architect Harold Moss adds, "Examples of the newer anti-fraud based solutions can be found in forms of Bot Detection, Currently fraudsters leverage automation and AI to maximize their fraudulent activities. As a result the Bot detection industry has been growing to identify non-human actors and reduce fraudsters abilities. We should expect AI to be applied to other fraud attempts like ACH fraud, where organizations struggle to understand when valid activities transition to fraudulent ones." Finally, AI helps reduce false positives, which leads to a better user experience as people’s transactions will not be wrongly stopped or flagged.
Fraud has been a constant throughout human history, and it has only gotten more difficult to stop. Luckily, we are finally in a position to leverage technology to reduce or maybe even eliminate fraud. I wonder if banks will reduce fees or interest rates because they will no longer have to deal with the same level of fraud. Nevertheless, getting rid of fraud is a win for everybody.
Written by Willie Turchetta, Edited by Alexander Fleiss & Sonakshi Dua