If you own an eCommerce store it’s of prime importance to protect yourself and your customers from any type of financial fraud.
In recent years, especially since the COVID-19 pandemic, there has been a significant increase in credit card fraud. The majority of customers are now shopping online which allows fraudsters to explore various means of financial scams.
For instance, according to UK Finance, in 2020 alone credit card fraud losses have reached £574.2 million.
Where eCommerce websites are concerned each retailer needs to implement strong security measures in order to prevent different types of fraud. Let’s take a closer look at how this all works.
Types of eCommerce Fraud
Some of the most common types eCommerce fraud include:
- Classic credit card fraud – when fraudsters steal a victim’s credit card through the Internet and use it to order items
- Return to Origin (RTO) – users claim to have never received an order, or use it and swap it with a damaged one and request a refund
- Ordering without paying – some users order goods with an empty PayPal account
- Chargeback fraud – instances when a customer makes an online order and after the item is being delivered to them, they request a chargeback on the grounds of a lost or stolen credit card
- Promo-code abuse – one individual creates several different accounts on the eCommerce website to use a single Promo code multiple times.
Rule-based Fraud Detection vs. AI/Machine Learning
To avoid payment scams, most eCommerce retailers use traditional methods of manual fraud detection. Quite often there are cases of multiple large orders on one eCommerce website to one address that occur at the same time – or an attempt to reset the account password 500 times which indicates a non-human endeavour.
For that reason, fraud analysts take various scenarios into account and use them to create a rule-based system of fraud detection.
The disadvantage to this rule-based system is that all these scenarios are manually written and they often need to be adjusted or even supplemented which requires additional manual work. In addition, the multiple verification steps that are incorporated in this system can harm the user experience.
Luckily, the machine learning-based system of fraud detection offers greater advantages – primarily in time and cost savings. It even finds hidden and implicit correlations in data that can’t be easily detected by rule-based systems and also reduces the number of verification measures.
The best part about the machine learning-based fraud detection system is that it functions automatically based on the user behaviour analysis.
Benefits of AI Fraud Detection
So, why should eCommerce retailers opt for AI-based fraud detection? What are the main benefits of implementing it on your website?
- Speedy Verification – when the verification is automated the entire process is faster and improves the user experience
- Behaviour Analytics – all machine learning algorithms will analyse the user behaviour and detect any deviant actions. Based on the analysis it will then prevent any other fraudulent activities
- Real-Time Data Processing – unlike the traditional rule-based systems which act after a fraud has taken place, AI fraud detection prevents such activities and acts before the attack
- Total Accuracy – there is no place for human error when it comes to machine learning, meaning businesses will receive consistent, round the clock security
- Locating Hidden Patterns– AI fraud detection can find different scenarios of fraud that humans can’t predict if they haven’t experienced them.
To sum up, eCommerce retailers can only benefit from implementing AI fraud detection. This will enhance their security, diminish significant losses that could occur from these types of fraud, and offer a better user experience.
If you are interested in enabling machine learning-based fraud detection in your eCommerce website don’t hesitate to contact the team at Cursum for more information and guidance.