We introduce a new, consolidated fraud prevention solution custom-built for platforms. You now have access to a more powerful way to stop fraud in real time and reduce revenue loss from payment fraud, account takeover, and many other risks.
Marketplaces and platforms, with their sizeable customer bases, are an attractive target for fraudsters looking to break into new accounts and make unauthorized transactions. These risks are further increased by the substantial volumes of money moving within these platforms that draw the attention of fraudsters even more.
That’s one side of the problem - fraud is a constant threat to the online business environment. And while you’re trying to keep your business away from this threat, you learn that the journey towards fraud protection is paved with challenges such as:
- Increased false positive rates
- Time-consuming integrations
- Lack of control over rules and little transparency in decision logic
- Keeping up with fraudsters' tactics to stay ahead of potential threats
Fraud Prevention is here to tackle the issues of this journey. Powered by in-depth user data, AI expertise, the most up-to-date fraud insights, and minimum integration efforts, this anti-fraud tool can offer marketplaces and platforms the opportunity to fight fraud on their own terms and accept more genuine transactions.
Customized solutions for specific fraud issues
Thanks to a flexible approach, we can address specific fraud trends at every step of the user journey. If, for instance, you observe fraud attempts at the account registration stage, you might think of creating rules and models that address account takeover to detect fraud when the users sign up on your website or app. Or, spotting users who abuse your loyalty program indicates promo and policy abuse - this situation requires a strategy different from one designed for account takeovers.
You can choose what step from your user lifecycle you want to protect - account registration, checkout, payouts, and post-payment disputes - and build your anti-fraud strategy. Two key features enable this customized approach: a flexible rules engine and modularity.
Flexible rules creation
You can fully customize your decision logic to fit your needs by creating rules and models and finely specifying the data you want to include for fraud assessment. Whether you need to combine multiple conditions to trigger fraud alerts or fine-tune your screening process, you can create in-depth rule sets that align precisely with your fraud detection requirements. What’s more, you can use your historical data to test the rules before they go live and impact final decisions. By back-testing against past data, you reduce the risk of blocking or flagging legitimate transactions.
Modular setup
A modular setup provides the flexibility to select modules and create and edit rules in various combinations. Platforms function across diverse industries and business models, and solutions that are effective for retail, for instance, may vastly differ from those suitable for on-demand platforms. You also have the flexibility to choose between various levels of integration, such as:
- Time- and cost-effective options that require no integration work whatsoever
- Fully integrated systems designed for advanced protection against complex fraud scenarios.
Understand users’ (good or bad) intentions with profiling
User profiling involves collecting all possible information - about how users behave on your website or app - from device, software, device and network environments, and behavioral data. This helps identify who's likely to be a genuine customer and a potential fraudster. For this, we use several tech approaches to:
- collect key details about a user's device, like the model and browser they're using
- monitor how they behave while visiting — what they click on, how long they stay, and their navigation patterns
- understand their operating system and any unique characteristics of how they use it
Fraudsters lie about all these details and try to spoof their device fingerprinting, IP address, or other elements that can give them away. They use special software, such as "antidetect" browsers, to bypass anti-fraud systems in several cases. These fraud tools can prevent, for example, from recognizing one customer with one device purchasing from multiple accounts using multiple credit cards. The goal is to catch signs like these to prevent fraudulent transactions on your platform.
Understand the tools and techniques fraudsters may use against you
With our proprietary research across Darknet and Clearnet, we get a better understanding of ways fraudsters approach legal business. This gives you a leg up in identifying and preventing fraud on your platform. Firsthand fraud intelligence helps us reverse-engineer fraudsters' tactics and adapt our models to existing and emerging fraud trends, thus enabling you to stay ahead of fraud.
Get visibility and control over decisions
You can benefit from a flexible rules engine, but it’s also essential to have the full context around why certain transactions are flagged as fraudulent. By getting clear insights into the fraud prevention strategy performance, you can determine the appropriate actions to take. Visibility and control within our fraud prevention system mean you can create custom rules and learn the rationale behind every flagged transaction - everything through a user-friendly interface that requires no integration effort from your side.
Rely on AI experts for complex fraud cases
Machine learning is undoubtedly a valuable tool, frequently used for real-time fraud detection and prevention. However, the involvement of data scientists - who can fine-tune the algorithms and create complex rules - is a true differentiator.
Our Data Science team can:
- analyze your platform’s collected data,
- identify the key features of fraudulent transactions,
- assess the importance of various fraud predictors,
- choose the most relevant analytical method that aligns with your company's business model
- develop models capable of predicting whether a specific transaction is potentially fraudulent.
Reduce bad rates and make room for the good ones
We use in-depth data insights and machine learning to look for fraud patterns and remove fraud threats. This intelligence informs recommendations to accept or refuse a transaction to minimize fraud rates and false positives. By continuously adapting to new fraud tactics and refining the decision process, businesses can keep the balance between blocking fraudulent activities and allowing genuine transactions to flow in, thus optimizing the customer experience while protecting revenue.
To learn more about how Mangopay can help you select and connect the products you need to succeed, get in touch with us.
