If not, we then check the payment method to evaluate the risk (e.g. We first check if the device used by the passenger is a high-risk one. Now imagine we added more rules as shown in the example below.

Maintaining these complex rules was getting harder and harder. Though all rules follow a similar pattern, there are subtle differences in the logic and they enable different decisions. In the example above, if a credit card has been declined more than twice in the last 3-months, the passenger will not be allowed to book even though he has a good booking history. The system scans through the rules, one by one, and if it determines that any rule is tripped it will check the other rules. THEN we would still not allow this booking IF a credit card has been declined(but paid off) more than twice in the last 3-months These rules were very effective in mitigating fraud risk, and we used to create them manually in the code. Rules are like boolean conditions that determines if the result will be true or false. In our early days when Grab was smaller, we used a rules-based approach to block potentially fraudulent transactions. How We Prevented Fraudulent Transactions in the Earlier Days The TIS team’s scope covers not just transport, but also our food, deliver and other Grab verticals. Manage user identities to securely login to the Grab app.to keep fraudulent users away from our app or services.Some of the things we watch for include promotion abuse, or passenger safety on late-night ride allocations. To overcome these issues, the TIS (Trust/Identity/Safety) task force was formed with a group of AI developers dedicated to fraud detection and prevention. Social Media Data, IP Address Geolocation & Proxy Information, Domain Attributes and Phone Ownership & Carrier DataĪICPA SOC 2, The EU-US and Swiss-US Privacy Shield Framework, International Association of Privacy Professionalsĭigital identity verification, address demographics, real-time risk profiling, Single Sign On (SSO), deep learning framework, enhance unsupervised learning for anomaly detection, deploy cost-sensitive machine learning model, Portal 3.At Grab, the scale and fast-moving nature of our business means we need to be vigilant about potential risks to our consumers and to our business. This approach can prevent mass attacks and reduce the ability of fraudsters to scale. So instead of the basic transaction risk assessment, email risk assessment can verify who is behind each online transaction, providing a holistic risk assessment and adding stronger controls against fraudsters while helping to approve good customers. TECHNOLOGY: anti-fraud detection tools availableĮmailage provides merchants the ability to verify the digital identity of the consumers for every transaction, making it harder for fraudsters to penetrate. Emailage provides a frictionless layer of protection that will supercharge your risk engine.Īccertify, CyberSource, Experian, Equifax This approach offers merchants the ability to mitigate fraud with negative signals, while using positive signals to approve good customers.Įmail address + global network + machine learning algorithms = online predictive fraud risk score. The company utilises a predictive risk score based on machine learning algorithms combined with a cross-industry and cross-sector consortium database. Since 2012, Emailage has offered fraud risk assessment built around the email address.

Merchant Risk Council, NORA Network (Australia) Member of industry association and or initiatives Online fraud prevention and digital identity intelligence provider Customers realise significant savings from identifying and stopping fraudulent transactions.Įcommerce merchants, payment processors, financial institutions, airlines, OTA, ticketing brokers, money transfer companies, credit card issuers, marketplace lenders, personal computer manufacturers, fraud platforms, gaming and gambling, other online presence Through key partnerships, proprietary data, and machine learning technology, Emailage builds a multi-dimensional profile associated with a customer’s email address and renders a predictive risk score. Emailage, founded in 2012 and with offices across the globe, is a leader in helping companies significantly reduce online fraud.

Credit card transaction risk engine