Connected Intelligence: Securing the user experience from start to finish.
When it comes to online security, the insights you gain throughout the customer journey can make or break your digital experience.
Is your fraud intelligence connected?
Fraudsters have learned how to outsmart traditional cybersecurity measures by developing sophisticated techniques that uncover your weakest links.
The good news? By linking insights across the customer journey, you can stop fraud in its tracks and provide a more frictionless experience. At Mastercard, we call this approach: Connected Intelligence.
Explore how the different security tools you may be using could be bolstered by connecting insights along the way.
Used to detect mass-scale automated attacks such as credential stuffing, credential cracking, inventory denial, account takeover or credit card cycling, among others.
Bot detection
Using the insight
Active biometrics
If previous indicators do not have sufficient data to verify a customer, active biometrics is a customer-friendly solution that can verify an individual easily using a fingerprint or face.
Using the insight
Network tokenization
Replaces the 16-digit card number with a set of unique virtual numbers that are encrypted during the transaction so fraudsters aren’t able to intercept it during a transaction or decrypt it.
Using the insight
Fraud rule engine
Uses static rules, network monitoring solutions, and machine learning to make fraud decisions, leveraging various sets of data around an event or transaction.
Using the insight
Passive Biometrics
Looks at user behavior such as mouse movements, typing cadence, swipe patterns or device orientation. This technology compares those data points to expected ones from each user for a seamless verification.
Using the insight
Device Intelligence
Uses data such as device ID and device fingerprint to recognize a returning user, whether fraudulent or trusted, to make a preemptive decision.
Using the insight
Payment authentication
Adding EMV 3D-Secure not only removes the potential risk of static passwords thereby reducing friction during the purchase experience, but the ~150 data elements deliver more confidence to the issuer to make an approval decision.
Using the insight
Chargeback mitigation & alerts
Facilitates the exchange of information between merchants and banks after a transaction to resolve a potential chargeback quickly, and limit friendly fraud.
Using the insight
As a first layer of protection against fraud, bot detection can ensure that real people move on while bots are stopped in their tracks. By integrating a machine learning algorithm, each prevented attack offers an opportunity to adapt and adjust to future bot attacks continuously.
The best active biometric solutions leverage insights from passive biometrics to determine whether an authentication challenge is really needed.
Biometric insights indicate whether or not the user is trusted, but payment security doesn’t stop there. A fraudster could potentially still intercept the card number during a transaction. However, with a network token, the encrypted card number can only be used once and includes data about the transaction.
The rules engine takes all the prior insights and decisions into account, including the additional data from the 3D-Secure protocol and network tokens, to come to a final approval decision based on a set confidence threshold.
Replicating human patterns is virtually impossible, which is what makes passive biometrics a valuable security layer in any strategy. When preceded by a layer of bot detection, a passive biometrics tool can accurately recognize human interactions in order to verify good users from bad ones.
Device intelligence ensures that the data captured is secure on the device being used for the transaction or session. Solutions that are built on APIs and SDKs to bind the data to the device offer the most secure experiences.
EMV 3D-Secure protocols work most effectively when leveraged with the additional insights gained from prior security layers, such as passive biometrics and network tokens. This can be especially powerful when an AI-based solution looks at the insights to make more dynamic decisions.
A collaborative dispute process enables an issuer and merchant to use the insights gained during the consumer journey, such as the type of device used for the purchase, to reduce fraudulent activity and ensure that a cardholder does not mistakenly dispute a legitimate transaction.
Connected Intelligence
Each of these layers are necessary for any fraud prevention strategy, but linking the insights across each layer and tying them together with insights, machine learning, and AI, your strategy becomes connected. This Connected Intelligence approach creates a more secure online environment and a more seamless experience for the customer.
Dive deeper into how you can start building your Connected Intelligence approach today.
Learn more
Bot detection
Active biometrics
Third-party data sources
Fraud rule engine
Passive Biometrics
Device Intelligence
Event/
transaction monitoring
Chargeback mitigation & alerts
