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Combining Machines and Humans to fight against FRAUD

Published on Mar 07, 2022 by Haingomanitra H. F.

Online retailers and any payment system operation must be protected against credit card fraud to avoid losses and serious financial risks. Several techniques exist to detect and stop credit card fraud, but some are less effective, and others are highly-priced.

So what would be the most effective fraud detection technique to adopt for your business? Well by the end of this article, you will be able to answer this question.

Using human review to detect fraud

Manual review relies primarily on the knowledge of qualified analysts to identify and prevent fraud. It can be done in-house or outsourced to a third-party provider. 

Relying solely on this method is expensive, as companies need to hire qualified and trained staff to review each transaction. It is also challenging to ensure that they have the right level of staff as sales can vary at any time. 

The need to manually verify all transactions also poses risks to e-commerce, as it is time-consuming and can leave an order in the queue for hours or even days, leading to customer frustration and a poor customer experience.

Despite these drawbacks, manual review is the last and most reliable defense against fraud, as it reduces the risk of false rejections and approved fraudulent transactions through fraud scoring machines. However, the human-machine combination is only effective if the human only has to review the hard-to-decide orders. In other words, the fraud score generated should be as accurate as possible and allow humans to identify a legitimate transaction from a fraudulent one with confidence. The aim is to reduce both chargebacks and the number of genuine customers rejected.

So, what type of machine technique should be combined with human examination to make your fraud detection system as effective as possible?

Rule-based techniques fall short

The rule-based scoring method is a traditional method of data analysis that requires complex and time-consuming investigations into different knowledge areas such as finance, economics, business practices, and behavior. Its implementation requires time and ongoing expertise, and it is not very scalable. Indeed, the rules must be constantly updated to cope with peak periods and new types of fraud. It is therefore challenging to use this method to make the human review effective. 

Machine learning is the most effective solution

Machine learning is a method that uses algorithms able to analyze and learn patterns from data by itself to detect whether a transaction is fraudulent or not. It removes the heavy work of data analysis from your fraud detection team. 

One of the attractive machine learning features is its ability to learn from new data and continuously detect anomalies. So the new tactics of fraudsters are most likely to be identified by machine learning. This solution will not replace humans 100%, but it is the most efficient machine-based technique that has the potential to dramatically reduce the time your team spends on reviews and analysis.

When you're looking for the best and most affordable protection against fraud and business loss, consider running SmartPredict's out-of-the-box Credit Card Fraud Detection AI use case. Our approach makes it easy for anyone to use and integrate AI-based solutions into businesses with just a few clicks.

You don't need to hire data scientists because the SmartPredict data scientists team and experts have implemented this solution as a ready-to-use white box AI project where reverse engineering is possible. 

Your team has to present your historical data to this AI solution and run the entire automation process in a few clicks. At the end of the process, they can retrieve the generated API to integrate the AI solution into their company's IT environment, whether an e-commerce website or any kind of payment system.

The resulting system tells you in real-time whether a transaction is fraudulent or not, with the probability of the event occurring.

With this indication, your team can set decision thresholds to determine whether a transaction will be authorized, reviewed, or prevented, depending on their sense of risk, to minimize the number of transactions that are unnecessarily reviewed.  

The result of the SmartPredict credit card fraud detection test.

If you wish to know more about SmartPredict Credit Card Fraud Detection out-of-the-box AI use case, do visit our site and watch the tutorial on our YouTube channel: