6 Ways Technology Could Stop Scams Before They Happen

How we’ll stop scams before they occur.

Maria Konnikova

Humans are startlingly bad at detecting fraud. Even when we’re on the lookout for signs of deception, studies show, our accuracy is hardly better than chance. Technology has opened the door to new and more pervasive forms of fraud: Americans lose an estimated $50 billion a year to con artists around the world, according to the Financial Fraud Research Center at Stanford University. But because computers aren’t subject to the foibles of emotion and what we like to call “intuition,” they can also help protect us. Here’s how leading fraud researchers, neuroscientists, psychiatrists, and computer scientists think technology can be put to work to fight fraud however it occurs—in person, online, or over the phone.

1. Suspicious Story Lines

Spam filters are supposed to block e-mail scams from ever reaching us, but criminals have learned to circumvent them by personalizing their notes with information gleaned from the Internet and by grooming victims over time. In response, a company called ZapFraud is turning to natural-language analytics: Instead of flagging key words, it looks for narrative patterns symptomatic of fraud. For instance, a message could contain a statement of surprise, the mention of a sum of money, and a call to action. “Those are the hallmark expressions of one particular fraud e-mail,” Markus Jakobs son, the company’s founder, told me. “There’s a tremendous number of [spam] e-mails, but a small number of story lines.”

In the future, this technology could go beyond e-mail filtering to also flag text messages, interactions on social media, messages on dating sites, even years-long “friendships.” Aaron Emigh, ZapFraud’s interim CEO, told me he’d stopped a woman from wiring money to a “fellow widow” she’d met on a Christian site for grieving people. He hopes that as natural- language analytics evolves, such warnings can be wholly automated.

2. Truth Filters

A similar approach could help combat fraud by flagging false statements on social media. (Disinformation creates opportunities for con artists to profit. In 2015, for instance, a scammer posted a fake Bloomberg article with news of a Twitter buyout off er— moving markets and making a little cash in the process.)

Kalina Bontcheva, a computer scientist who researches natural-language processing at the University of Sheffield, in England, is leading a project that examines streams of social data to identify rumors and estimate their veracity by analyzing the semantics, cross- referencing information with trusted sources (such as PubMed, for medical information), identifying the point of origin and pattern of dissemination, and the like. Bontcheva is part of a research collaboration called Pheme, which plans to fl ag misleading tweets and posts and classify them by severity: speculation, controversy, misinformation, or dis information.

3. In-the-Moment Warnings