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 cybercriminals themselves. Now we will be discussing how companies traditionally implement fraud detection systems, the gap that machine learning earns on those attempts, and the consequences that these improvements have in 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 offer a semi-reliable way of mitigating fraud risk and can be utilized in a variety of ways. A number of those rules might contain parameters like not allowing purchases from “at-risk” zip codes, flagging transactions from locations that are not close to the billing address, or not letting many purchases from the same credit card in a short time period. However, these rules include their own limitations, particularly when planning for big data fraud detection.
Limitations of Rules-based models
- The fixed thresholds
Every fraud detection principle includes a corresponding threshold. For example, when a company doesn’t allow more than three buys at a half-hour window, then that’s the rule’s brink. Although these thresholds are great for overall parameters, they are not capable of adapting to individual situations.
- Rules are absolute
This goes hand-in-hand using fixed thresholds. Rules are absolute, meaning that they can only be successful when responding to “yes or no” questions. Such questions would include: Is your purchase location within range of the billing address? Is the billing address situated in a risky zip code? Has this consumer made more than three purchases in the previous thirty minutes?
- Rules are ineffective when used alone
Because rules can’t adapt to unique circumstances, they prove to be ineffective when acting alone to filter fraudulent trades. Machine learning is used to make up for these inefficiencies.
Fraud detection + machine learning
Machine learning aids make fraud detection simpler and much more efficient. By applying machine learning in your detection version, you can flag suspicious activity more often, and with much greater accuracy than with traditional rule-based methods alone. This allows for better pattern recognition among considerable amounts of information, rather than relying solely on “yes/no” variables to determine fraudulent transactions or users.
For machine learning to be effective in preventing fraud, it depends upon classification. Classification is the process of grouping data together according to certain criteria. Frequent applications of classification in detecting fraudulent transactions include spam detection, forecasting loan defaults, and implementing recommendation systems, amongst others. The objective of these approaches is to differentiate legitimate transactions from fraudulent ones based on classifications like which retailer a client is buying from, the positioning of both the retailer and buyer, time of day/year of the transaction, and the amount spent.
There are lots of ways that you can group together client data to improve fraud detection efforts. A number of those grouping methods include:
Age of the client’s account, amount of characters inside their email address, fraud rate of their IP address, number of devices they have accessed your site on, etc…
- Order history
The number of orders was placed when the account was created, the dollar amount spent on each individual transaction, and just how many failed orders were tried.
The billing address matches the shipping address, the country of the client’s IP address matches the shipping country, customer’s state, city, or zip code is not known for having fraudulent action.
- Method of payment
Credit card and shipping address are from the same country, matching names involving the customer and shipping information, a credit card isn’t issued from a bank with a reputation of fraudulent transactions by its clients.
The effect on customers
Machine learning is not only beneficial to the businesses who implement these models, but also for the subsequent customers who visit your website. Using 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 readily available for those who are prepared to purchase.
Gets Machine Learning
Implementing machine learning in your fraud detection system may seem like a no-brainer, but we understand that such a task can be easier said than done. Our experience in machine learning permits you to feel confident in your ML implementation, while concurrently solving your Fraud detection problem along the way. We sponsor a serverless microservices architecture that allows enterprises to quickly deploy and manage machine learning models in scale, making the whole process simple and effortless for your organization.