Not All Machine Learning Systems Are Created Equal

Introduction

The rise of e-commerce, alternative payment methods, and card, not present transactions have caused the number of fraud losses by retailers to increase greatly. According to a recent Forrester the report, the e-commerce fraud loss rate in North America alone is expected to reach approximately $4.2 billion by 2018.

 Alternative factor attaching to the escalation in fraud losses by traders is the easy access to modern technologies. Fraudsters now have access to cloud services, web servers, APIs, and advanced technologies. This allows them to not only find new and innovative ways of committing fraud but also to commit fraud on a massive scale and with super speed. In this challenging and competitive landscape, online businesses need a fraud prevention solution that can detect many different types of fraud and is effective across multiple channels.

The right fraud prevention system can help businesses reduce fraud losses, lost sales, fraud review costs, and chargebacks. The right system can also help prevent businesses from losing customers due to troll accounts, fake listings, and content spam.

The shortcomings of legacy fraud detection systems

Online businesses are discovering that traditional fraud prevention systems simply can’t keep pace or deliver the results they need. Rules engines, the most common legacy approach to fighting fraud, are not as effective as machine learning-based systems at fighting the many different types of online fraud or keeping up with massive amounts of user-generated data.

Unlike machine learning online training-based systems, rules-based methods are not proficient of automatically learning from large data sets or analyst feedback. Instead, rules are hard-coded and inflexible. That means that as fraudsters adapt their tactics, businesses are left vulnerable to new types of fraud attacks that can easily slip through the cracks. Rules-based systems are also not capable of detecting the subtle nuances of fraud, treating it in “black and white” terms that often cause a greater number of false positives.

Qualities of a powerful machine learning solution

There are quite a few machine learning-based fraud prevention solutions available today. But not all machine learning solutions are formed equal. So, what types of a machine learning training system effective at transmittable fraud?

While the types of machine learning algorithms a fraud prevention system uses are important, access to massive quantities of high-quality data is crucial when it comes to fraud prevention. Algorithms are only as good as the data and training provided to them.

The Sift Science solution

Sift Science uses large-scale machine learning course and a global network of fraud data to provide online businesses real-time, adaptive fraud protection. Sift Science features a vast library of fraud detection and prevention models covering many different types of businesses and industries. Sift Knowledge can also be modified to suit each concern’s specific fraud anticipation needs. Sift Science can help any online business fight fraud, while still providing a great customer experience. Sift Sciences learns from more than 5,000 fraud signals, which can be clustered into two basic categories: behavioral signals and identity signals. Behavioral signals include things like what a user clicks or taps on, the rate at which they buy things over a certain period of time when they signed up for an account, and other actions they take on a website or mobile app. Identity signals include things like email addresses, device information, and billing and shipping addresses.

Beyond these two groupings of fraud signals, the Sift Discipline solution also learns and predicts fraud based on activities occurring across its entire network of customers. In other words, each time a customer gives feedback that a particular user is fraudulent; all other customers will receive updated risk scores immediately. The network effect increases each customer’s ability to prevent fraud exponentially.

What makes Sift Science Different?

Here are just a few of the features that set Sift Science apart when it comes to detecting and preventing online fraud:

Online Learning

 Sift Science uses online learning to update deployed models with new information obtained from customers, third parties, and other real-world sources of data. When the system is notified that a transaction has been deemed to be fraudulent, the system learns what characteristics and attributes the fraudulent transaction contains. That knowledge is relayed across the entire network in milliseconds, allowing customers to receive updates in real-time.

Large-Scale Machine Learning

Large-scale machine learning certification allows Sift Learning to leverage thousands of signals in order to quickly determine new fraud patterns and detect fraudulent performance. Thanks to large-scale machine learning, Sift Science landscapes a fraud library that comprises millions of fraud patterns. Large-scale machine learning also agrees on the platform to discover deception signals specific to each client, automatically and with no additional integration work.

Rescoring

Sift Knowledge features real-time learning and scoring so that new fraud forms and signals can be learned, detected, predicted, and that knowledge is shared through the network in milliseconds. Sift Learning always checks fraud factors, signals, and other data – then rescores the possibility that certain users are conducting fraudulent transactions.

Labels

 A label is the representation of human judgment about a specific user. Customers can apply a label indicating whether specific users are “bad” or “not bad.” If a user is marked as “bad,” the system considers all of the behavior and signals of the user to be associated with bad behavior in the future. Labels help Sift Science achieves a high level of accuracy when it comes to learning and predicting future fraudulent behavior.

An Ensemble of Models and Global Models

Sift Knowledge features a collective of analytical models that detect changed types of fraud based on specific signals and behavior. The platform presently learns from over 5,000 fraud indicators and that number is growing rapidly. Each consumer receives their own machine learning classic and a global model that segments the data collected from each transaction and model across the network. The platform also uses in-depth custom learning to create models that are defined by a small number of signals and can target business-specific data for each customer.

Global models make it possible for customers who do not apply labels to their own data to leverage all of the other models shared across the network, providing them with effective fraud prevention.

Console / Data Visualizations

One of the most important features of Sift Science is a console that tells the complete story about the results through data visualizations, relevant signals, and access to raw data. Many fraud stoppages platforms do not do a good enough job of explaining the results, nor do they provide a means for users to discover the data.

Users need to understand why transactions are scored the way they were and why certain transactions are deemed to be a fraud. Users should be able to discover the data acquisition insights from fraud signals and data conceptions.

N-Gram Analysis

When it comes to detecting fraud, Sift Science does more than just simple correlation. The platform uses n-gram analysis, a type of natural language processing that looks at all of the combinations of adjacent words or letters of length n. This allows for a detailed, nuanced representation of the data.

N-gram analysis is particularly useful when it comes to spam finding and recognizing multiple fake accounts. For example, when a fraudster is blocked, they will often create another account on the same site, and may change a few details (for example, by tweaking johndoe123@gmx. com to johndoe124@gmx.com). Sift Science is one of the few vendors employing n-gram analysis to identify repeat behavior like this, and can typically flag fraudulent users who come back to a website or app – even if they change them device or identifying information.

Conclusion

The quantity, velocity, and variety of transactions and fraud data continue to increase at an extremely rapid pace, and legacy fraud prevention systems simply can’t keep up. Machine learning-based systems like Sift Science are designed for analyzing vast streams of data generated from billions of transactions in real-time. They are also capable of detecting fraud from nuanced combinations of signals buried in data. While best machine learning course is far more effective at fraud protection than traditional methods, it is not a silver bullet. Machine learning alone is not going to solve all of your fraud glitches. Machine learning requires access to massive extents of high-quality data in order for it to be truly operative at detecting and stopping fraud.

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Sift Science features a growing network of thousands of companies that provide a wealth of quality data and models helping the platform accurately predict and prevent fraudulent transactions in real-time. Sift Science can help protect your online business from the rapidly evolving strategies and methods of fraudsters. Learn more about how Sift Science can help your online business fight payment fraud, account abuse, and many other types of fraud.