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Technology companies have been emphasizing fraud detection for decades. Internet fraud first began appearing in 1994, with the introduction of e-commerce businesses. Since then, companies have taken large strides in fraud detection, but with these advancements also come improved tactics from the cyber criminals themselves. Today we will be discussing how companies traditionally implement fraud detection systems, the difference that machine learning makes on these efforts, and the effects that these improvements have on your customer base.

Traditional methods of fraud detection

Before machine learning became the most effective way of detecting fraudulent activity, organizations would rely on rules. Rules provide a semi-reliable means of mitigating fraud risk, and can be used in a variety of ways. Some of these rules might include parameters such as not allowing purchases from “at risk” zip codes, flagging transactions from locations that are not near the billing address, or not allowing multiple purchases from the same credit card in a short period of time. But these rules come with their own limitations, especially when aiming for big data fraud detection.

ML rules come with limitations in fraud detection

Limitations of rules-based models

Fixed thresholds

Each fraud detection rule has a corresponding threshold. For example, if a company doesn’t allow more than three purchases in a half hour window, then that is the rule’s threshold. Although these thresholds are great for general parameters, they are not capable of adapting to individual situations.

Rules are absolute

This goes hand-in-hand with fixed thresholds. Rules are absolute, meaning that they can only be effective when responding to “yes or no” questions. Such questions would include: Is the purchase location within range of the billing address? Is the billing address located in a risky zip code? Has this user made more than three purchases in the past thirty minutes?

Rules are inefficient when used alone

Because rules cannot adapt to unique circumstances, they prove to be inefficient when acting alone to filter fraudulent transactions. Machine learning is used to help make up for these inefficiencies. 

Fraud detection + machine learning

Machine learning helps make fraud detection easier and more efficient. By implementing machine learning into your detection model, you can flag suspicious activity more frequently, and with far greater accuracy than with traditional rule-based methods alone. This allows for better pattern recognition among large amounts of data, instead of relying solely on “yes/no” factors to determine fraudulent users or transactions.

For machine learning to be effective in preventing fraud, it relies on classification. Classification is the process of grouping data together according to certain criteria. Common uses of classification in detecting fraudulent transactions includes spam detection, predicting loan defaults, and implementing recommendation systems, among others. The goal of these methods is to distinguish legitimate transactions from fraudulent ones based on classifications such as which merchant a customer is buying from, the location of both the merchant and buyer, time of day/year of the transaction, and the amount spent.

Methods to improve fraud detection

There are several ways that you can group together customer data to improve fraud detection efforts. Some of these grouping methods include: 


Age of the customer’s account, amount of characters in their email address, fraud rate of their IP address, number of devices they’ve accessed your site on, etc.

Order history

How many orders were placed when the account was created, the dollar amount spent on each transaction, and how many failed orders were attempted.


The billing address matches the shipping address, the country of the customer’s IP address matches the shipping country, customer’s country, city, or zip code is not known for having fraudulent activity.

Method of payment

Credit card and shipping address are from the same country, matching names between the customer and shipping information, credit card is not issued from a bank with a reputation of fraudulent transactions by its customers.

The effect on customers

Machine learning is not only beneficial to the companies who implement these models, but also for the subsequent customers who visit your site. By having a machine learning model in place, you can reduce the number of falsely flagged transactions, streamlining the purchase process for legitimate users. This system also helps to detect fraud that might otherwise be missed with rules-based models alone, improving inventory management and ensuring that available stock is always accurate and available for those who are ready to buy.

Algorithmia gets machine learning

Implementing machine learning into your fraud detection system might seem like a no-brainer, but Algorithmia understands that such a task can be easier said than done. Our expertise in machine learning allows you to feel confident in your ML implementation, while simultaneously solving your fraud detection problem along the way. We host a serverless microservices architecture that allows enterprises to easily deploy and manage machine learning models at scale, making the entire process simple and effortless for your organization. See how Algorithmia can help you build better software for your organization in our video demo.

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