Data Security Firm is Changing the Face of Fraud Protection by Carole VanSickle Ellis Property Shield is, by any definition, “new to the scene” of data security. In fact, the company launched in February of last year. However, said the company’s co-founder and CEO Alex Fahsel, the data security firm hit the ground running by tackling emerging issues around fraud, data security, cyber security, and even artificial intelligence. “As much as we love all this technology, it can be used for all sorts of nefarious things,” Fahsel said. “Property Shield specializes in using this technology for good and to stay ahead of scammers and fraud. Cybersecurity has historically been a ‘cat-and-mouse’ game where the good actors have always been a few steps behind. We started Property Shield to give those good actors the opportunity to stay ahead.” “We are basically the data police,” explained Luke Lind, co-founder and CTO of the company. “Our system is constantly monitoring the web for potential fraudulent listings, and whenever it finds one, we immediately notify whichever client owns the data.” At the same time as the notification is going out, Property Shield tackles the process of having the fraudulent listing removed from consumer access as soon as possible. “The combination of these two workflows helps reduce illegal activity at the physical property level,” Lind continued. “Because our system is able to identify and remove fraud listings within hours, we can effectively cut down on the threat they pose to the property.” That threat is growing exponentially larger every day as cyber criminals, already frighteningly internet savvy, add psychological manipulation to their roster of skills. According to the Boston Division of the Federal Bureau of Investigation (FBI), rental scams increased 64% between 2020 and 2021. Cumulative losses exceeded $350 million in 2021 alone. “The actual losses are most likely much higher,” the bureau added, “because many people are hesitant to report they were scammed.” “These are very sophisticated crime syndicates that push these scams and frauds,” Fahsel said. He explained that large syndicates of cyber criminals based in countries with low or no regulation on this behavior may run hundreds of variations on dozens of distinctly “stamped” scams, from romance scams to employment scams to rental fraud. When one strategy begins to work particularly well, the lessons from that strategy may then be applied across the board. There is a great deal of intense analytics involved. “Cyber criminals look closely for certain criteria to indicate if a certain geographical region or market is ripe for real estate scams,” Fahsel said. “When they home in on a market, they will check net domestic migration patterns, for example, to determine where a large volume of people is moving. Then, they tailor the scam to fit the mindset of the people moving into or out of those areas.” Because an individual moving into a new area may not have a clear idea about the nature of the rental landscape, they are particularly prone to falling for what Fahsel calls “too-good-to-be-true” offers. “Scammers compile data about properties, rental prices, images, and listings, then hire virtual assistants or even college students looking for part-time jobs to put together these offers and facilitate the exchange of security deposits, rent, or other funds,” he explained. This makes the entire process even muddier for law enforcement because there are often innocent parties involved in the middle of the process between the victim and the cybercriminal. “The ‘employees’ get scammed too, because they frequently never get paid,” Fahsel noted. The “salaries” for these positions are also often too good to be true; the Federal Trade Commission estimates in 2022, job seekers lost $68 million in labor, lost income, and fraudulent fees associated with fake jobs. Cutting Off Fraud at the Source Because most real estate scams are carried out by large syndicates running multiple scams and multiple variations on these scams in multiple locations around the country, identifying trends early and acting preemptively to identify and eliminate fraud related to an investment portfolio is one of the few ways to make a significant dent in the potential money, time, and opportunity lost when a real estate scam succeeds. There are multiple victims at every point in the scam, from the property owner to the “employees” and “interns” working unwittingly for the scammer to the renter hoping to establish a household in the property shown by the fraudulent listing. According to Georgia Legal Aid, an organization based in Atlanta, Georgia, which is currently a hotbed of rental fraud, the two primary types of rental listing scams actively deployed at present included renting properties that the scammer is not authorized to rent and creating property listings for properties that do not exist and soliciting deposits or application fees using those listings. However, this simple explanation barely scratches the surface when it comes to the ingenuity and brainpower cybercrime syndicates dedicate to this type of fraud, Fahsel said. “These are teams of people who wake up every day and think, ‘How do we scam these people? How can we hack the system and take advantage of it?’ If they put half the effort they do into pushing these scams into a legitimate business that would help people, they could do really well,” Fahsel explained. “Sadly, that is not the route they take, so Property Shield spends every waking moment thinking about how to stop them.” Lind added, “Our system stands out from other fraud-prevention platforms because we are using the same tools that the scammers are using to fight them. Machine learning is a critical component of our system, and we have been ‘training’ our models for a long time.” To “train” a machine learning model, the algorithm is “fed” data from which it can learn. The more training the model receives, the better it becomes at identifying threats and classifying them as such. Lind noted, “Our model is also multi-modal, meaning it can process multiple mediums of data, including text and images.” This enables Property
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