Types of Fraud Threatening Ride-Hailing apps
To ensure customer safety, guarantee a fair and trustworthy ecosystem for both drivers and passengers and safeguard the app’s revenue, ride-hailing companies must stay vigilant about the various types of fraud that can occur on their platforms.
Driver Fraud
Fake rides
Ride-hailing companies often give drivers incentives if they complete a certain number of rides daily. Fraudulent drivers exploit incentive systems by:
- Creating multiple fake accounts and manipulating GPS locations to deceive the ride-hailing service into thinking that legitimate trips have taken place.;
- Cloning their apps to create both driver and passenger accounts on the same device accepting their own rides to meet quotas.
Artificial Surge Pricing
Fraudulent drivers can collaborate to inflate ride prices artificially. They achieve this by creating numerous fake passenger accounts and using GPS spoofers to simulate high demand in a particular area. This causes ride-hailing systems to detect high demand and increase trip prices, charging genuine passengers higher rates.
GPS Spoofing
Fraudulent drivers use GPS spoofers to spoof a device location, falsifying its geographic coordinates. Their goal is to appear in more lucrative areas outside their current range and seem closer to potential passengers, disadvantaging honest drivers and increasing passenger wait times.
Ride Request Hogging
Fraud rings employ devices with autoclickers or tampered ride-hailing apps to programmatically accept ride requests, monopolizing passengers and limiting genuine drivers’ access to opportunities.
Fake Reviews
Fraudsters create multiple fake passenger accounts to falsify ratings and boost their chances of getting rides, allowing poor-performing drivers to maintain top ratings despite genuine negative reviews.
Passenger Fraud
Promo abuse
Ride-hailing companies often offer one-time promotions such as first-ride-free, or seasonal discounts. Passengers can abuse these to get free rides by creating lots of fake accounts and riding with a different account each time, thereby re-using the same promotion multiple times.
Indicators of fraud on ride-hailing apps
Fraud prevention for ride-hailing apps involves a combination of device identification, behavioral analysis, and continuous monitoring, all provided by advanced technology. Here are some common indicators of ride-hailing fraud:
Multiple Accounts from One Device: Detection of several accounts linked to the same device and/or location can indicate attempts to conduct malicious activities.
Unusual Surge in Ride Requests: An unexpected spike in ride requests, particularly in a specific area, could indicate the use of fake accounts or GPS spoofing.
Inconsistent GPS Data: Irregular or implausible GPS movements, such as a car appearing to jump from one location to another, may point to GPS spoofing.
Inconsistent User Behavior: Users who display drastic changes in behavior, such as suddenly taking numerous short trips after a period of inactivity, could be engaging in fraudulent activities.
Repetitive Ride Routes: Multiple rides following the same unusual route pattern, which could be an attempt to generate fake ride completions or exploit ride incentives.
How to protect ride-hailing apps from fraud attacks
Fraud typically starts with a device, whether used to create fake accounts or employ malicious tools to launch attacks on ride-hailing apps. Addressing fraud at its root is crucial, and taking a proactive stance and deploying real-time fraud detection & prevention software and risk analysis are essential steps. These approaches blend various strategies, including:
Device Fingerprinting
Use device fingerprinting technology to identify fraudsters across multiple devices.By combining device attributes with behavioral, network, and location data, device fingerprinting solutions can be tailored to prevent multiple accounting and block fraudsters from accessing the platform.
Real-time monitoring
Implement a platform that manages risks in real-time, continuously profiling device sessions and returning real-time actionable risk signals. This helps platforms stay proactive against fraud threats and identify the exact moment a good user turns bad. In the ride-hailing context, it’s very important to flag when a user starts using GPS spoofers, app cloners, autoclickers, and more.
Artificial intelligence and machine learning algorithms
Unlike humans, AI can continuously analyze vast amounts of data in real-time. This allows them to identify suspicious patterns and flag potential fraud attempts as they happen, minimizing losses.
Machine learning algorithms can learn and adapt over time, becoming adept at recognizing complex patterns in fraudulent behavior. These patterns might be subtle but AI can identify seemingly unconnected data points that indicate a high risk of fraud.
By continuously analyzing data and identifying emerging fraud trends, solutions with AI and ML algorithms can help businesses stay ahead of fraudsters. This allows them to proactively implement new security measures and adapt their fraud detection strategies before new methods are widely used.
How the ride-hailing unicorn inDrive has been protecting their ecosystem and users from fraud with SHIELD
inDrive, the second largest ride-hailing app in the world, partnered with SHIELD to proactively combat fraud and boost trust and fairness in their ecosystem. The app achieved over 99.77% genuine user rate with SHIELD's Device-First Risk AI platform.
Here’s how they leveraged our technology to stop fraud:
inDrive tapped on the SHIELD Device ID, the global standard for device identification that stops fraud at the root. The SHIELD Device ID empowered inDrive to identify instances when many driver or passenger accounts were being operated from the same device.
Simultaneously, SHIELD’s AI technology pinpointed clusters of accounts tapping on the same IP address, location, and subnet, allowing proactive action against fraudulent syndicates from fake accounts.
SHIELD’s Risk Intelligence detected all malicious tools and techniques used on the platform, like GPS spoofers, tampered apps, and app cloners. It continuously profiles the device session, returning real-time actionable risk signals to help inDrive identify the exact moment users engage in fraudulent activity, enabling instant countermeasures.
"inDrive is dedicated to fighting injustice and upholding transparency and fairness in the mobility and transportation space. Our partnership with SHIELD empowers us to stay true to our mission of helping people, ensuring the highest standards of trust and fairness for all while maintaining our rapid pace of growth.", Arsen Tomsky, CEO & Founder, inDrive.
Read the case study here.
