How reservation apps can put fraudsters in a to-go box

A restaurant reservation can make or break someone’s evening, even someone’s week if they identify as a “foodie.” Fraudsters, of course, are well aware and have found a way to juke reservation apps and naive consumers who mistakenly believe they’re in for the Michelin Star treatment at that hard-to-get-into restaurant.

Sit-down eateries have long been fertile ground for fraud (dine-and-dashing, running the card twice in the back, hacking users through malicious QR codes), but restaurant reservation fraud—akin to ticket scalping—uniquely impacts reservation platforms, restaurants, and their would-be patrons.

Let’s take a closer look at reservation fraud and how to stop bad actors from feasting on reservation apps.

A maître d’s worst nightmare

After scammers land a restaurant reservation—typically at a ritzier establishment—they attempt to sell it on Craigslist, Facebook Marketplace, and other classifieds. The poster withholds vital information from unsuspecting buyers: other folks have purchased the same reservation.

Imagine showing up for your anniversary dinner only to find four other parties have the same reservation—a party of ten with no table and no backup plan.

Craigslist ad for restaurant reservation
A recent Craigslist ad for a restaurant reservation

Duped customers are left with hurt feelings and empty stomachs, but restaurants, many still finding their footing amid the pandemic, suffer lost revenue and the likelihood that neither of these customers will return in the future. In turn, the restaurant blames the reservation app and may seek out a similar platform that isn’t so easily manipulated.

Does such a platform exist? How can reservation apps (and restaurants) turn the tables on bad actors?

Tonight’s special: preemptive fraud detection

Deduce recently partnered with a global restaurant reservation platform to solve its reservation fraud issue. The intelligence, scalability, and preemptive nature of Deduce’s solution was precisely what the app needed to put fraudsters in a doggy bag.

Malleability played a crucial role as well. Deduce created custom risk signals and provided a continuous authentication solution for the app, Improbable Travel, to neutralize phony reservation bookers. If someone in Kansas makes reservations at restaurants in New York, Los Angeles, and New Orleans for the same day, chances are it’s a ruse, doubly so for new accounts. By flagging such cases based on geolocation and account status, and referring them to the reservation app for manual review, Deduce’s tailor-made approach reserved restaurant tables for legitimate customers only.

A recipe for success

Deduce owes its flexibility and real-time fraud detection to its Identity Network comprising 500 million anonymized user profiles, gleaned from over 150,000 websites and apps, and over 1.4 billion daily activities. In fact, Deduce sees the majority of the transactional online U.S. population multiple times per week. It verifies users through risk signals, like Impossible Travel, and trust signals such as familiar device, familiar network, familiar activity, and familiar time of day.

These real-time risk and trust signals work in tandem to spot bad actors long before any malicious behavior can take place. In the case of a restaurant reservation platform, preemptively intercepting fraud is the way to a restaurant and app user’s heart: full tables, satiated appetites, less churn.

Are you starving for an effective first line of defense against account creation fraud and to prevent ATO attacks while reducing friction for legitimate customers? Contact us today and get set up in just a few hours.

What does the Deduce Identity Network look like in action?

In our previous blog posts we’ve discussed the value of identity intelligence, how data poverty can mix up risk signals, and shown how the Deduce Identity Network can enable a trusted user experience.

But what does our network of 500 million user profiles and 1.4 billion daily online activities actually look like in action?

To help illustrate how Deduce’s trust signals can significantly improve the user experience—and prevent the churn that CEOs loathe—here is a day in the life of a trusted user identity on our network.

Uber—8:17 a.m.

Meet Tom. Tom is a Deduce trusted identity. We don’t know his name is Tom (Deduce defines a profile via email, device, geo, and activity), but we know he won’t be launching a credential stuffing attack any time soon.

It’s a typical work day for Tom, and Deduce’s Familiar Time of Day signal is already pointing to trust. Tom is waiting for an Uber, standing on the curb in front of his house—a new house he and his wife moved into a few weeks ago. The Uber arrives. Tom buttons his blazer, tightens his half-Windsor knot, and heads to the office.

A few minutes later, when Tom decides to check emails on his phone, he realizes the email client logged him out and he can’t remember the right password combination. Given his change of residence, Tom’s new commute path to the office could trigger an MFA (multi-factor authentication) challenge; fortunately, Deduce’s IP Address and Time of Day trust signals identify Tom as a non-malicious user and increase his allotted number of password attempts. He’s in!

Uber—8:50 a.m.

Roughly 30 minutes after boarding the Uber, Tom remembers that his wife asked him to buy plane tickets for a spontaneous Vegas trip next weekend.

Tim is only a few minutes from his office and the ticketing app with the best deal isn’t installed on his phone. Even with just a few weeks of data, Deduce’s Time of Day/Day of Week trust signals—coupled with intel from multiple cell towers—recognize Tom is commuting and expedite the account creation and verification process.

Tom acquires the last-minute tickets with time to spare.

Office—9:27 a.m.

Tom grabs his morning joe and walks to his desk. After texting his wife that they’ll soon be swimming in daiquiris and poker chips, he logs into his office computer and checks his calendar.

Uh oh. A video meeting in three minutes AND it’s on a video conferencing platform he’s never heard of?

No worries. Tom downloads and installs the software then quickly creates an account without having to verify via OTP (one-time passcode). Deduce’s recognition that Tom is actually Tom—it recognizes the IP address and device ID of his work computer at the right time of day—allows him to enter the meeting right on the dot.

Home—6:48 p.m.

Tom and his wife get home from work. They’ve hardly unpacked since moving and navigating the labyrinth of boxes in the kitchen to use the stovetop is unrealistic. Pizza it is.

Tom’s phone is dead, so he grabs his wife’s tablet. He downloads a food delivery app—the same one installed on his phone—and logs in to order their favorite: a medium Hawaiian with extra pineapple.

A user logging in on a new device might trigger an MFA under normal circumstances, but Deduce knows Trusted Tom is accessing the app from his residence on a new, albeit still familiar, network. Deduce also identifies the device ID of the tablet, as Tom’s wife has used it on the network before.

The pineapple-on-pizza debate is contentious, but we can all agree that friction has no place in the user experience.


Want to steer clear of friction and churn? Contact us today to find out how you can treat your customers like trusted users, not bad actors

Hint: One is more reliable than the other

Identity fraud, including account takeover attacks, affects 15 million Americans each year. In response, companies are looking for fraud prevention solutions that are easy to deploy, frictionless, and unlikely to trigger false positives.

Two popular methods of detecting fraud are behavioral biometrics and identity intelligence. In simple terms, the former analyzes how a user acts while the latter analyzes who a user is. Most behavioral biometrics and identity-based solutions can be deployed without impeding the user experience—a key prerequisite in the digital age—but they share little else in common.

Before breaking down the key differences between behavioral biometrics and identity intelligence, let’s look closer at each approach and why an identity-centric model is more reliable.

Behavioral biometrics

Behavioral biometrics measures a user’s physical and cognitive traits to differentiate between fraudsters and real customers. Unlike physical biometrics, behavioral biometrics doesn’t scan fingerprints or eyes; instead, it looks for patterns in how a user interacts online. For example, it might invoke keystroke dynamics to determine if someone (or something) is copy-and-pasting into a text form or typing.

Here are some other ways in which behavioral biometrics can examine a user:

  • Signature analysis
  • Gait analysis
  • Voice recognition
  • Lip movement

While behavioral biometrics is easy to integrate and improves the accuracy of fraud identification systems, it has its drawbacks. Being a nascent technology, assimilating it into your existing technology stack can be expensive. Once it is activated, stockpiling enough personal data to successfully analyze a user’s behavior will take some time. The aforementioned accuracy can also take a hit if a user strays from their typical behavioral patterns—a drunk or sick user might speak or type differently, an injured user might suddenly walk with a limp. Even a user’s setup can elicit false positives: consider someone who gets flagged erroneously, via keystroke analysis, because they use different keyboards at home, at work, and on the go.

The increased likelihood of false positives outlined above makes behavioral biometrics more suitable as a complementary fraud defense rather than a core solution.

Another flaw of behavioral biometrics is bias. Some solutions rely upon training data that skews toward one demographic. For instance, a 2018 study from MIT and Stanford discovered that the facial data used in at least one system was more than 77% white and more than 83% male.

Identity intelligence

Sophisticated anti-fraud tactics such as behavioral biometrics can be effective. But, in the era of synthetic identities, it’s not enough.

Detecting fake identities consisting of stolen passwords and other personal info requires robust security checks at point of entry, post-authentication tools that can zero in on inconsistent behaviors and preempt fraudulent transactions. Identity intelligence achieves precisely this.

Identity intelligence leverages massive datasets rife with insights on how legitimate users interact online. This knowledge helps neutralize fraudsters even if they possess a user’s login details. Contrary to behavioral biometrics’ need to ramp up its behavioral data for a given user, identity intelligence pulls from data that is ready to go from day one and, thanks to machine learning, constantly growing and up to date.

Identity intelligence hones in on both the person and their device. If George logs in, it finds out if it’s really George, and if the device in question belongs to him. Device usage offers identity-based solutions a plethora of behavioral insights: the types of mobile apps George uses during his morning commute, the wifi network he uses at work, the VPN he accesses on his home computer. Identity intelligence is the actionable, real-time, dynamic fraud prevention approach that closes the gaps left behind by behavioral biometrics.

Identity intelligence that can’t be faked

If a company needs identity intelligence to overcome the blindspots of their existing behavioral biometrics solution, or to remove the need for behavioral biometrics altogether, they’ll need as much identity data as they can muster. No one has more of this data than Deduce.

Deduce boasts the largest real-time identity graph for online fraud in the US. The brain behind our identity intelligence, the Deduce Identity Network, comprises more than 500 million anonymized user profiles and over 1.4 billion daily activities. This sizable (and fully compliant) data stack prevents the false positives that would hinder a behavioral biometrics solution.

Furthermore, given fraudsters’ proclivity with learning to hack new technologies (like behavioral biometrics), businesses can be assured that Deduce’s identity intelligence cannot be bamboozled. Fraudsters are too cheap to outwit our network. Circumventing such a vast arsenal of user profiles, website and activity data—over an extended period of time—requires money, time and effort they can’t afford.

Want to give our identity intelligence a spin? Even if you’ve already implemented a behavioral biometrics tool, Deduce can be layered right on top. Contact us today and get started in just a few hours.

New device ID and its pesky false positive problem

Every day, supermarket and liquor store cashiers reject wannabe McLovins attempting to buy six-packs with a fake ID. Likewise, every hour—perhaps every minute—fraud prevention solutions reject online logins and transactions due to a new, unfamiliar device ID.

The problem? Only 2% of fraud is perpetrated by a new device. The new device ID risk signal, one of the most widely used by authentication platforms, is guaranteed to trigger a false positive fraud risk for the 98% of good customers—and trigger a deluge of rage along with it. Per PWC, one in three consumers ditches a brand following a negative user experience; it’s hard to get more negative than erroneous multi-factor authentication (MFA) or a wrongfully canceled purchase.

False positives cost US e-commerce merchants $2 billion per year. That’s nearly 3% of their revenue, not far behind fraud-related costs (7.6%)—a possible death knell for e-tailers with razor-thin margins. 

Part Two of our “Mixed Signals” series explores the flaws of the new device risk signal, and how to combine new device ID with real-time data to keep users (and bottom lines) intact.

False positives aren’t the only problem

Device-based authentication leads to a flurry of false positives, including a 30-50% false positive rate associated with geolocation sensitivity. But it doesn’t end there. To avoid flagging legitimate customers, solutions need to track a variety of real-time risk and trust signals.

Outside of false positives, here are other downsides of counting on the new device risk signal alone:

Device spoofing. Spoofing a user’s device is a cinch and ubiquitous enough to render device ID, by itself, unsuitable for verification.

Advanced attacks. Solutions reliant upon device ID won’t detect complex attacks involving social engineering and automation (man in the middle, remote access tool attacks, etc.).

Actionability. The amount of users logging into new devices at new locations overwhelms device-based anti-fraud solutions. Consequently, good users on unfamiliar devices will be burdened with friction and deemed high-risk.

Why device ID causes false positives

The chief failing of device ID authentication is that it doesn’t account for one simple fact: consumers are constantly toggling between devices or buying new ones altogether.

Cell phones are only one of the devices that users swap because they either dropped it in the toilet or desire the latest and greatest model. It’s also not uncommon for more than one person to use a device, such as a tablet or desktop computer, making the new device risk signal an inadequate means of verifying user identity.

The increasingly remote nature in which we work and interact presents new challenges for device ID authentication—even when paired with geolocation and behavioral biometric data (both can be spoofed). For instance, someone who’s temporarily telecommuting from a family member’s house might use that individual’s computer to buy goods. Or, someone might be in quarantine at a hotel and get flagged for using their mobile device at an unusual location. Sharing login credentials with friends and relatives across households and devices is another sure-fire way to set off the device authentication tripwires.

Silencing the false alarms

Similar to device fingerprinting—a way of positively identifying a device by recognizing its unique software and hardware characteristics—real-time data is the key piece missing from device-based authentication.

The Deduce Identity Network melds the new device risk signal with other data such as device, IP, geolocation, and activity (login, checkout, account creation, password reset, etc.) to generate comprehensive real-time behavioral intelligence that drives a calculated risk or trust signal. This prevents legitimate users from being flagged and the resultant friction that makes them jump ship. 

Deduce’s 500 million anonymized user profiles, 150 thousand websites and apps, and over 1.4 billion daily activities provide a rock-solid determination of user trust—or, conversely, flat-out fraud. Device spoofing is rampant, but the Deduce Identity Network won’t fall for the fakes. Fraudsters can’t afford to create a synthetic identity capable of fooling the largest real-time identity graph in the US.

The Cliff Notes: Don’t sink users in a quagmire of friction when they’re merely transacting from a new phone or shopping for clothes on their parents’ Macbook. Treat legitimate customers like distinguished guests, not criminals.

Ready to tap the collective intelligence of our Identity Network and experience the serenity of avoiding new device false positives? Click here to learn more.

Wipe location spoofers and false positives off the map

Geolocation, geolocation, geolocation. It’s one of the common risk signals tracked by anti-fraud solutions and often the reason legitimate customers are thrown into account verification purgatory.

Geolocation locates users via a GPS signal, IP address mapped to geography, wifi network locations, or web browser location information on their device. Geolocation is helpful in combating fraud, that is, if it’s used properly. However, many fraud prevention companies erroneously depend on IP address alone, and don’t possess the data at scale to differentiate between a fraudster and someone who’s simply transacting in an unfamiliar area. This makes businesses susceptible to location spoofing—when fraudsters falsify their location by using a virtual private network (VPN) or IP spoofing techniques.

Ineffective use of geolocation also contributes to online payment fraud, which is expected to cost businesses 200 billion by 2024. Additionally, verifying location without the right intelligence leads to the much-maligned false positive, and from there metastasizes into a user experience nightmare.

How geolocation impacts users

Account takeover by way of location spoofing isn’t fun for businesses—particularly merchants who bemoan chargebacks—and neither is a false positive credit card decline, which unnecessarily annoys users and causes a churn reaction.

Imagine traveling to another state on vacation and needing to gas up your rental car. Easy enough. But the gas pump declines your credit card transaction, requiring you to call your bank and verify your identity, or, heaven forbid, actually have to go and talk to the member of staff in the office, which in turn throws off the timing of your family’s tightly packed itinerary. Even worse, what if you can’t buy a plane ticket because the airline triggered multi-factor authentication (MFA) and seats filled up by the time you verified your identity?

A recent fraud surge on the Nike SNKRS app exemplified the impact of location spoofing across verticals. The app released a special pair of sneakers only made available to customers within a certain region. Predictably, fraudsters manipulated their IP addresses in order to buy the sneakers, leaving a slew of unhappy SNKRS users in their wake.

Whether users are falsely identified as bad actors or locked out of buying a rare pair of shoes, relying on IP addresses alone to stop fraud is damaging to a brand’s user base and reputation.

IP address isn’t spoof-proof

IP addresses are easily exploited by fraudsters. Tapping a VPN or other proxy to conceal their location requires minimal sophistication. As such, businesses need to supplement IP data with additional location intelligence to accurately identify trustworthy users (and keep bad ones out). Conversely, they need to be able to identify when a legitimate, privacy-concerned user accesses online services and apps via a VPN. 

The question remains, then: What is the best (read: only) way to successfully outwit location spoofers and avoid geolocation-triggered false positives?

Relieving geolocation irritation

If location-spoofing fraudsters have your compass spinning out of control, Deduce’s identity intelligence data can provide a sense of direction. Deduce combines real-time and historical data to eliminate false positives, discerning if a user is fraudulent or if they’re simply a good, tax-paying citizen who’s on the move. 

Powered by the Deduce Identity Network—500 million anonymized user profiles, 150 thousand websites and apps, over 1.4 billion daily activities—Deduce’s algorithms analyze multiple trust signals, in addition to IP, for a given user (device, network, time of day, and a lot more) to determine if a user is legitimate.

By tracking online activity related to time of day, day of week, and specific activities such as logins, account creation, checkout, forgotten password, etc. over time, Deduce is able to discern “normal” behavior from fraudulent behavior. (After all, you’d be suspicious if your neighbor started mowing his lawn at 10:00pm, right?) As an identity’s activity is tracked over the course of two weeks, a month, six months, and so on, a more accurate picture of an identity’s behavior is established and the confidence factor increases. In fact, Deduce is likely the first to know when an ATO has occurred, usually well before the victim themselves.

In rare cases in which Deduce is not able to determine fraud via geolocation, our Customer Alerts feature quickly notifies consumers to confirm their identity and location. This feedback only strengthens Deduce’s algorithms to ensure such verification measures aren’t necessary in the future.

User experience isn’t the only incentive for companies to fortify their geolocation authentication. In some industries, such as online gaming, Deduce’s intelligence layer could prevent enormous fines related to regional gambling laws and/or the collapse of a company altogether. All the more reason to ditch an IP-only approach.

Ready to jump-start geolocation in your fraud prevention efforts? Contact us today and try Deduce for free.

More users are leaving passwords on the tarmac

The rigors of boarding an airplane post-9/11 are well-documented: ID checks; removal of belts, shoes, laptops, decanting your toiletries into three fluid ounce containers; frantically stuffing plastic tubs with personal belongings before the travelers behind you hum the Jeopardy theme.

Over the past two decades, however, frequent and occasional flyers alike have subscribed to expedited customs programs from the Transportation Security Administration (TSA) and ​​U.S. Customs and Border Protection (CBP) that slingshot travelers to their terminals with their clothes and luggage untouched. The friction alleviated by programs such as TSA PreCheck and Global Entry is comparable to the slog of old-school account login — travelers hate waiting in line; modern app users hate keying in username/password combos upon each visit or being asked to verify the email they have just entered in a different application.

Passwordless authentication is the account login equivalent of PreCheck and Global Entry. Here is why passwordless is taking off, and how apps are “boarding” their users expeditiously while creating a fraud-free, Trusted User Experience.

Passwords don’t fly anymore

Just as line-weary travelers have opted for PreCheck and Global Entry, research suggests more and more users are ready to leave passwords on the tarmac.

Earlier this year, Experian’s Global Identity & Fraud Report asked more than 2,700 businesses and 9,000 consumers about their preferred login approach. For the first time since Experian’s ran this annual report, passwords landed outside the top three. Respondents, more security-conscious amid a 20-percent bump in online traffic during the pandemic, felt more comfortable logging in via physical/behavioral biometrics and SMS pin codes.

The data dictates that we are rapidly approaching a passwordless future. Like the airline passengers who get in and out of customs with a simple biometric scan, a growing contingent of app users desire a quick and seamless customer journey. Businesses must answer the call by implementing passwordless login that operates in real time yet still mitigates fraud risk. It’s not just money that’s at stake either — it’s the trust of users.

A trusted user experience attracts frequent flyers

Frustrated as travelers may be with slow-moving lines, they’re unlikely to leave the airport and take Greyhound. They have a plane to catch, and anything short of death will not warrant a ticket refund. App users, on the other hand, inundated with platforms and services, have every reason to seek out a frictionless alternative.

Businesses that don’t adopt a passwordless approach risk losing customers, some of whom will share their sluggish user experience with others and ultimately damage a brand’s reputation. Even worse, companies with lengthy authentication processes at the account signup stage will dissuade people from using the product in the first place. Recently, one QSR company admitted that they lost 10 percent of new app signups due to the email verification step not being completed, rendering the fast food app a no-food app.

In the spirit of PreCheck and Global Entry, apps must expedite the user journey by installing a passwordless login apparatus that is fast as it is safe. This requires an intelligent fraud solution with enough data to authenticate users in real time and remain in lockstep with an ever-shifting cybersecurity landscape. By analyzing multiple factors in real time — device, geography, time of login, account activity etc. — platforms can verify fast and reliably at login, get users in-app in a flash, and create a Trusted User Experience that generates customer loyalty.

Deduce can’t expand the leg room on your next flight, but we can get your user authentication flying in no time. Try us for free today, and build a fraud-free, Trusted User Experience that converts your customers into frequent thumb-tappers and mouse-clickers.

Customer churn can happen early — even before checkout

Central to transforming the user experience is removing the friction involved in account creation verification. It’s the first step of the customer journey — before the customer is actually a customer — and often an overlooked source of churn. The Deduce team has seen cases in which companies signing up tens of thousands, or millions, of new users per month have lost 10 percent of these accounts due to email verification problems alone (verification emails landing in spam, issues with mobile email apps, etc.).

Track the lifetime value of those thousands of customers over time, not to mention the negative brand reputation accrued, and the damage is significant.

A new report from CMO Council, comprising 2,000 consumers from the US, UK, Canada, and Ireland, shows just how fed up customers are with frustrating authentication processes. Here are some notable takeaways:

  • More than 60 percent of consumers surveyed had canceled a transaction due to inefficient authentication
  • 81 percent of respondents indicated they would seek out companies that employed an easy and secure identity verification process
  • 34 percent preferred to use biometrics as a primary means of authentication; 10 percent preferred to use passwords

As for brand reputation, most respondents (53 percent) reported that login problems were a substantial detractor, and an overwhelming majority (85 percent) indicated they look down on a company with identity verification issues. This specifically rang true for banks, credit providers, mobile payment apps, and other types of financial services.

There is a way to eliminate this account creation friction. Deploy an identify fraud solution such as Deduce that can provide trust signals on each new account creation in real time. If the new customer is designated as trustworthy, take them down the Trusted User Experience journey to your application or service. If the solution determines potentially fraudulent account creation activity, route the user down the traditional path.

Here’s an example of a frictionless returning customer experience. Let’s say a fraud prevention solution flags potentially fraudulent activity when a customer, who’s attempting to use their saved credit card info, logs in to a new device to book a dinner reservation. The application, in this case Resy, a restaurant discovery platform, verifies the customer’s identity through the following steps:

  • First page: Enter email
  • Second page: Enter phone number
  • Third Page: Resy sends a text message with a code
  • Customer enters code and accesses the application

This is a brand that really cares about customer experience and wants to minimize steps/friction. At no point is the customer asked for their password.

Like Resy, other brands are upping their game in the user authentication department — Gartner expects more than 60 percent of large enterprises to adopt passwordless login by 2022. ​​But passwordless isn’t perfect: devices can be stolen, biometrics can be spoofed, and hackers will inevitably adapt to new authentication tools by way of SIM swapping, intercepting SMS messages, biometric database leaks, and other methods. To avoid customer churn caused by sluggish account verification, and thwart account takeover fraud, companies must ultimately simplify account verification via identity intelligence: a contextual, data-driven solution that can confirm a user’s identity in real time.

Click here to try Deduce for free and keep your customers moving with real-time identity verification.