AI-powered e-commerce fraud prevention is a modern approach that uses machine learning and artificial intelligence algorithms to solve one of the most critical security problems facing online stores. Global e-commerce fraud losses exceeded $48 billion a year as of 2025; in Turkey, card payment fraud and return fraud are among the fastest-growing fraud categories. In this guide we take a comprehensive look at the types of e-commerce fraud, AI-based detection methods and industry leaders such as Stripe Radar, Signifyd and Kount.
Types of E-Commerce Fraud
To build an effective fraud prevention strategy, you first need to correctly identify the types of threat you face. Each type of fraud requires a different detection method and countermeasure.
- Card Fraud: Unauthorised purchases made with stolen or cloned card details. In card testing attacks, the validity of a card is probed with small-value transactions.
- Account Takeover (ATO): Accessing a customer account with stolen login credentials. Fuelled by credential leaks (credential stuffing), these attacks particularly target accounts holding loyalty points or saved cards.
- Return Fraud: Methods such as returning used, damaged or counterfeit products, or returning a product that was never purchased. The high return rates in Turkey make this type of fraud especially critical.
- Chargeback Fraud (Friendly Fraud): A customer denies receiving a product they did receive and requests a refund through their bank. One of the fraud types small businesses encounter most often.
- Identity Fraud: Opening a new account and making purchases using someone else's identity details.
AI-Based Fraud Scoring
Traditional rule engines (for example: block orders from country X, manually approve transactions above 1000 TL) can quickly be neutralised, because fraudsters discover these rules and easily work around them. AI-based fraud scoring, by contrast, analyses hundreds of signals simultaneously and produces a risk score for every transaction.
Machine Learning Models
The ML models most widely used in fraud detection are as follows: Gradient Boosting classifiers (XGBoost, LightGBM) deliver high accuracy by handling the inherently imbalanced nature of fraud datasets with SMOTE or class weighting. Neural Networks are powerful, particularly for behavioural pattern analysis. Graph Neural Networks stand out in detecting organised fraud rings by modelling the networks of relationships between accounts (same IP, same device, similar order patterns).
Feature Engineering
The success of fraud models depends largely on selecting the right features. The critical features are: transaction time and day, match between IP geography and delivery address, device fingerprint, order history and return rate, number of transactions from the same card or device within a short period, and email and phone verification status.
Stripe Radar, Signifyd and Kount: Platform Comparison
The leading fraud prevention platforms on the market address different needs and budgets.
- Stripe Radar: Built-in ML-based fraud protection for stores using the Stripe payment infrastructure. It is fed by data from billions of transactions across the Stripe network. The basic package is free for Stripe users; the Radar for Fraud Teams add-on is available for advanced features. It should be the first choice for Stripe users in Turkey.
- Signifyd: A platform with a guaranteed fraud protection model that offers a chargeback guarantee. Its per-transaction pricing structure makes it suitable for small and mid-sized stores too. Shopify and WooCommerce integration is straightforward.
- Kount: Enterprise-scale fraud management under the Equifax umbrella. It offers omnichannel fraud detection, identity verification and compliance management in a single platform. Ideal for high-volume e-commerce and fintech companies.
Rule Engine vs Machine Learning: Which One and When?
It is important to remember that rule engines and ML-based approaches complement each other. Rule engines are suitable for rapid deployment, interpretability and clear blocking rules required for legal compliance (OFAC lists, restrictions on specific countries). ML models, on the other hand, excel at detecting complex, multivariate fraud patterns and adapting to fraud tactics that change over time. The most effective system uses the two in layers: first the rule engine blocks clear violations, then the ML model scores the rest.
Managing False Positives
The most critical balance in fraud prevention is the false positive rate: genuine customers being wrongly flagged as suspected fraudsters. A high false positive rate leads to lost sales, customer dissatisfaction and reputational damage. To determine the optimal threshold, the precision-recall trade-off should be analysed and business priorities taken into account. For high-value and loyal customers, additional verification layers (OTP, identity verification) are an effective way to provide security without triggering a fraud block.
Fraud Trends Specific to Turkey
The prominent fraud patterns in the Turkish e-commerce ecosystem are as follows: card testing attacks are concentrated particularly over VPNs and proxies; return fraud is high, especially in the fashion and electronics categories; cases of customer account takeover through social engineering are on the rise; and fraud during courier delivery (the product is received but a return is reported) is also a common scenario. For Turkish e-commerce stores, GSM-based identity verification and integration with the Turkish BIN (Bank Identification Number) database provide an additional layer in fraud scoring.
Frequently Asked Questions
Which fraud prevention solution is best for small e-commerce stores?
For stores using Stripe, Stripe Radar is a free and effective starting point. For those using a different payment infrastructure, Signifyd's chargeback guarantee model is a good option for managing the risk of loss on a limited budget. In every case, basic measures such as a strong password policy, 2FA and shipping address to billing address matching checks should be prioritised.
What should I do in the event of a chargeback?
To dispute a chargeback, you need to submit evidence such as proof of delivery (signed courier document, delivery photo), the order confirmation email, correspondence records with the customer and IP address logs during the bank's dispute process. Platforms that provide a chargeback guarantee, such as Signifyd, take on this risk entirely on your behalf.
How is a fraud prevention ML model trained?
Developing a model with your own data requires labelled fraud and legitimate transaction history, feature engineering and imbalanced data handling steps (SMOTE, class weighting). Using a ready-made platform is generally faster and more cost-effective than developing your own model. If you do not have your own data science team, we recommend starting with Stripe Radar or Signifyd.
Does a fraud prevention system hurt the customer experience?
Poorly calibrated systems can block legitimate customers' purchases through a high false positive rate, which in turn increases cart abandonment. Correct threshold tuning and risk-based dynamic verification (an additional step for high-risk transactions, a smooth pass for low-risk ones) are the key to maintaining this balance. The goal should be frictionless security rather than friction-based security.
Conclusion
AI-powered e-commerce fraud prevention solutions, with the right platform and strategy, significantly reduce both chargeback losses and the operational security burden. Building a system that uses a rule engine and an ML model in layers, respects the false positive balance and tracks fraud trends specific to Turkey is an essential investment for sustainable growth. Contact Toserof Tech. for AI and e-commerce solutions.
