Credit card payments are the most widely adopted payment method in Japan. On ecommerce sites, their usage rate accounts for 80% of all payments. This indicates their popularity with Japanese customers.

While credit card payments are expected to see further growth, cases of payment fraud have also become a serious problem. As a result, many customers have concerns about the security of credit card payments.

Fraud schemes and techniques have become increasingly sophisticated and difficult to detect using conventional systems. Thus, countermeasures using AI systems equipped with advanced machine learning (ML) capabilities are attracting attention.

In this article, we explain why AI is useful in detecting credit card fraud and how fraud detection AI works. We also explore the benefits of implementing AI and some key considerations.

Key takeaways

• Credit card fraud becomes more sophisticated every year. Measures to combat it have become increasingly difficult to implement. Therefore, fraud detection systems that employ AI have become attractive to businesses.
  
• AI-enhanced fraud detection occurs through the automatic analysis of transaction patterns and scoring of fraud risks.
  
• The benefits of AI for fraud detection include the ability to detect fraud in real time.
  
• To enhance the security of a business’s ecommerce site, it is important to carry out employee training on security and establish a robust internal framework, in addition to relying on AI technology.
  
• Stripe Radar allows businesses to implement advanced security measures using machine learning (ML) that adapts to ever-changing fraud patterns.
  

Why is AI important for credit card fraud detection?

Many cashless payment options are available in Japan, such as credit card payments, quick-response (QR) code payments, and bank transfers. Among these options, credit card payments are highly versatile and very popular.

As credit card fraud continues to rise around the world, the Japanese government has developed various guidelines, including the mandatory adoption of 3D Secure 2.0. The implementation of AI-enhanced fraud detection systems is another measure under consideration. Below, we outline the main reasons for the growing interest in them.

Credit card fraud in Japan

According to the Japan Consumer Credit Association (JCA), the total financial loss resulting from credit card fraud in 2025 exceeded ¥‎51 billion. This was more than double the 2020 figure of ¥25.3 billion. The deployment of high-precision fraud detection systems that use AI is important to address this situation.

The most significant finding in this data is that the total losses for the period from October–December 2025 amounted to ¥9.39 billion. This is a significant decrease compared to the same period in the previous year—¥16.23 billion.

This difference could be due to a synergistic effect that resulted from the mandatory implementation of 3D Secure 2.0, which was scheduled for the end of March 2025. Combine this with the widespread adoption of AI for fraud detection, and we can see the results of these efforts in the data.

Credit card fraud using AI

Fraudulent schemes perpetrated by third parties have become increasingly complex and sophisticated due to the misuse of AI. This makes it difficult to ensure security using conventional, rule-based systems alone.

For example, phishing is one type of fraud scheme that uses AI. Fraudulent actors generate large numbers of fake emails using AI. Then, they use the emails to lure recipients to fake websites to steal account passwords and credit card information.

With phishing, fraudulent actors use the names of real organizations and official logos to trick recipients into believing the emails are legitimate. They instantly generate and send large amounts of fake emails written in natural language. Therefore, it is extremely difficult for conventional systems to accurately identify these scams.

It is important to use AI fraud detection to counter schemes that use AI to commit fraud. AI technology can learn about ever-changing fraud patterns in real time and adapt immediately.

Increase in cardholders

Credit card payments have become an indispensable part of everyday life in Japan. Credit cards allow customers to earn points and miles, and they also offer installment payments, revolving payments, and bonus payments. In addition, credit cards are gaining popularity due to their convenience.

However, as the frequency of online card transactions increases, so does the risk of that information being compromised. Once information leaks, there is a risk that it will be misused in a chain reaction across countless ecommerce sites and elsewhere.

Credit card fraud detection using AI is attracting significant interest among ecommerce businesses. Because it is a defensive measure capable of detecting anomalies in real time, it can even defend against schemes that are difficult to prevent due to structural reasons.

How AI detects credit card fraud

Below, we discuss the different mechanisms AI uses to detect credit card fraud.

Pattern recognition

Fraud detection AI uses ML to analyze vast amounts of payment data and automatically identify fraudulent patterns. This enables it to detect suspicious transactions quickly and accurately. It also adapts to the latest fraud patterns, allowing it to detect new tactics that conventional, rule-based systems tend to miss.

By analyzing certain data from multiple perspectives, AI can identify fraudulent schemes. The main data used for training includes the following:

• Transaction amounts
  
• Transaction dates and times
  
• Transaction locations (e.g., country, region, etc.)
  
• Terminals used
  
• Business information
  
• Past browsing and purchase history
  

Fraud detection AI uses this data to build AI models that detect anomalies, such as unusual or fraudulent purchases.

For example, a credit card that is typically used for small transactions during the day is used late at night on an overseas website that sells expensive items. The AI will instantly recognize this as an abnormal pattern.

Furthermore, accuracy can be improved based on feedback from detection results, and detection performance can be further enhanced through continuous training in response to increasingly sophisticated fraud schemes.

Fraud risk scoring

Fraud detection AI monitors transaction patterns from every angle in real time and evaluates (i.e., scores) each transaction based on those patterns. In other words, AI automatically analyzes various data—such as transaction amounts and locations—every time a payment is processed. Then, it quantifies the risk of fraud.

If the score exceeds the threshold, the AI will determine that there is a high risk of fraud and will either automatically block the transaction or request additional authentication.

Cross-industry data learning with multitenant AI

A major challenge with conventional AI technologies is that they train solely on transaction data held by individual credit card companies. This makes it difficult for them to address fraudulent activity they haven’t previously encountered. Multitenant AI involves cross-industry collaboration to analyze transaction data. It has been considered as a way to overcome this challenge.

Businesses that adopt multitenant AI share fraudulent data with one another and train the AI collaboratively. For example, with multitenant AI, if a business detects a new fraud pattern, other businesses can immediately start using an AI model that has automatically learned the pattern.

By working together as an industry and sharing information, AI systems can counter complex fraudulent schemes that individual businesses would be unable to detect on their own.

Advantages of AI for fraud detection

Adopting fraud detection AI offers many advantages.

Block fraud instantly

The biggest advantage of implementing fraud detection AI is that it uses advanced AI technology to detect fraudulent transactions in real time and automatically blocks them. This makes it possible to prevent fraud before the transaction is completed.

This type of real-time, AI-enhanced instant blocking feature is important for protecting customers and minimizing profit losses caused by chargebacks.

Reduce operational workload

System automation can significantly reduce the workload associated with monitoring and detection. In the past, operators were responsible for monitoring vast amounts of transaction data to detect fraud. The excessive workload often resulted in oversights and created bottlenecks.

Fraud detection AI can operate consistently and monitor all transactions in real time. In addition, if a transaction with a high risk of fraud is detected through scoring, the AI immediately sends an alert. Because of this, operators no longer need to review every single transaction—including legitimate ones—as they have in the past. Instead, they can allocate their resources to verifying cases that are difficult for AI to assess on its own and providing high-quality customer service.

Fraud detection AI that uses ML to continuously improve detection accuracy can also help reduce false positives (i.e., the false identification of legitimate transactions as fraudulent). This can reduce workloads and costs associated with manual verification and prevent legitimate customers from being affected. Therefore, it can also help businesses avoid lost sales opportunities.

Retain customers

Security measures with fraud detection AI ensure customer safety, enhance business reliability, and further strengthen branding.

Providing a payment environment where customers can use their credit cards with confidence reduces their anxiety and stress during the payment process. It also ensures a pleasant shopping experience, which directly contributes to customer retention.

Challenges with AI for fraud detection

AI-enhanced fraud detection systems do present some challenges. Therefore, caution is required when implementing them.

Lost sales opportunities

Even with high-precision fraud detection AI, there are still cases where legitimate transactions are mistakenly flagged as fraudulent. If a payment by a valued customer is flagged as fraudulent, it can discourage the customer from making purchases. This results in lost sales opportunities.

When introducing fraud detection AI, it is important to set a reasonable detection threshold and regularly verify the system’s accuracy.

No comprehensive fraud detection

While fraud detection AI can enhance the security of ecommerce sites, it is important to understand that it cannot perfectly prevent all instances of fraud. There will be cases where it cannot fully counter the sophisticated fraud schemes that continue to develop.

Rather than relying solely on AI systems, it is important to implement security measures—such as 3D Secure 2.0—for safe website operation. Businesses can also focus on employee training and establish a system that allows responsible staff to make final decisions.

Unclear fraud decisions

Even when AI detects fraud, the “AI black box” phenomenon can occur. This means that the basis for the AI’s decision is unclear, and the business’s representatives cannot offer an explanation. If a representative can’t clearly explain to a valued customer why their payment was flagged as fraudulent, there’s a good chance the customer’s satisfaction will decline.

Explainable AI (XAI) is an AI technology that visualizes the basis for decision-making and provides explanations. XAI has been attracting global attention as a solution to this challenge.

In the EU, there is a growing trend toward establishing legal standards specific to AI. This includes the legal requirement under the Artificial Intelligence Act to provide explanations behind AI decisions. It is expected that similar regulations will be introduced in Japan, which could lead to the widespread adoption of XAI.

Therefore, when choosing fraud detection AI, it is important to choose a system that provides logical explanations of how it arrived at its decisions—one that humans can understand.

FAQs

In this section, we answer frequently asked questions about AI for credit card fraud detection in Japan.

How Stripe Radar can help

Stripe Radar uses AI models to detect and prevent fraud, trained on data from Stripe’s global network. It continuously updates these models based on the latest fraud trends, protecting your business as fraud evolves.

Stripe also offers Radar for Fraud Teams, which allows users to add custom rules addressing fraud scenarios specific to their businesses and access advanced fraud insights.

Radar can help your business:

• Prevent fraud losses: Stripe processes over $1.9 trillion in payments annually. This scale uniquely enables Radar to help detect and prevent fraud, saving you money.

• Increase revenue: Radar’s AI models are trained on actual dispute data, customer information, browsing data, and more. This enables Radar to identify risky transactions and reduce false positives, boosting your revenue.

• Save time: Radar is built into Stripe and requires zero lines of code to set up. You can also monitor your fraud performance, write rules, and more in a single platform, increasing efficiency.

Learn more about Stripe Radar, or get started today.