How You Can Improve Fraud Detection with Help of Machine Learning


Tech companies have been emphasizing fraud detection for decades from machine leaning. 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 improvements also come improved tactics from the cyber offenders themselves. Now we will be discussing how businesses traditionally implement fraud detection methods, the gap that machine learning earns about these efforts, and the ramifications that these improvements have in your client base.

How You Can Improve Fraud Detection with Help of Machine Learning
How You Can Improve Fraud Detection with Help of Machine Learning

Conventional methods of fraud detection

Before machine learning became the most effective way of detecting fraudulent action, associations would rely on rules. Rules offer a semi-reliable way of mitigating fraud risk and can be used in a variety of ways. Some of these rules may include parameters such as not allowing purchases from "at-risk" zip codes, flagging transactions from places that aren't near the billing address, or even not allowing many purchases in precisely the same credit card in a quick period of time. But these rules come with their limitations, particularly when planning for large data fraud detection.

Limitations of rules-based models
  • Fixed thresholds

Each fraud detection principle includes a corresponding threshold. For example, if a business does not permit more than three purchases in a half-hour window, then that's the rule's threshold. Even though these thresholds are great for general parameters, they aren't capable of adapting to individual scenarios.
  • Rules are absolute

This goes hand-in-hand with fixed thresholds. Rules are absolute, sense that they can only be operative while responding to “yes or no” questions. Such questions could include: Is the purchase location within range of this billing address? Is your billing address situated in a speculative zip code? Has this consumer made over three purchases in the past thirty minutes?
  • Rules are ineffective when used alone

Since rules cannot adapt to specific conditions, they prove to be ineffective when acting exclusively to filter fraudulent transactions. Machine learning is used to help make up for all these inefficiencies.

Fraud detection + machine learning

Machine learning helps make fraud detection easier and much more efficient. By applying machine learning in your detection model, it is possible to flag suspicious activity more frequently, and with far greater accuracy compared to conventional rule-based approaches alone. This allows for greater pattern recognition among considerable quantities of information, rather than relying solely on "yes/no" variables to determine fraudulent transactions or users.

For machine learning how to succeed in preventing fraud, depends upon classification. Classification is the process of grouping data together according to specific standards. Common uses of classification in detecting fraudulent trades include spam detection, predicting loan defaults, and implementing recommendation systems, amongst others. The goal of these approaches is to differentiate legitimate transactions from fraudulent ones based on classifications like which retailer a customer is buying from, the positioning of both the merchant and buyer, time of day/year of the transaction, and the sum invested.

Approaches to improve Fraud detection

There are lots of ways in which you can group together customer data to improve fraud detection efforts. A number of these grouping methods include:
  • Identity

Age of the customer's accounts, amount of figures inside their email address, fraud rate of their IP address, number of devices they've accessed your site on, etc…
  • Order history

The number of orders was placed when the account was created, the dollar amount spent on each transaction, and how many failed orders were attempted.
  • Location

The billing address must match the shipping address, the country of the client's IP address matches the shipping nation, client's country, city, or zip code is not known for having fraudulent action.
  • Method of payment

Credit card and shipping address are must be from the same country, matching names involving the customer and shipping information, credit card isn't issued by a bank with a standing of fraudulent transactions by its clients.

The effect on clients

Machine learning isn't only beneficial to the companies who implement these versions, but also for the following customers who visit your site. By having a machine learning model set up, you can cut back the number of flagged transactions, streamlining the purchase process for legitimate users. This system also helps detect fraud that might otherwise be overlooked with rules-based models independently, improving stock management, and ensuring available stock is always accurate and available for people who are ready to buy.

Gets machine learning

Implementing machine learning into your fraud detection system might appear like a no-brainer, however, some companies understand that such a task could be easier said than done. Many companies have an expertise team in machine learning (ML) that permits you to feel confident in your ML execution, while simultaneously solving your fraud detection problem on the way. They sponsor a server less micro services architecture that enables enterprises to quickly deploy and manage machine learning models at scale, making the whole process easy and effortless for your organization.

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