AI security and fraud prevention

In this blog article, we show you how you can use artificial intelligence to detect fraud at an early stage and what measures you can take.

AI security and fraud prevention

Artificial intelligence (AI) has revolutionized many areas of life in recent years. From voice assistance and personalized advertising to self-driving cars, the possibilities seem limitless. But despite all the benefits, we must not forget the downsides: As digital processes increase, so does the risk of fraud.

Online store operators must protect themselves against fraud and cybercrime. Fraudulent transactions, account takeovers and data leaks can not only damage your store's reputation, but also cause significant financial losses. To prevent these threats, more and more companies are turning to artificial intelligence (AI). This guide shows you how to use AI to increase the security of your online store and effectively prevent fraud.

Possible uses of AI

Until now, however, many companies have failed to implement a successful recommendation strategy because maintaining the necessary data involved an enormous amount of manual effort. This is where artificial intelligence comes into play: modern AI solutions open up completely new possibilities that make this functionality - which was previously often reserved for large online stores due to the manual effort involved - now also accessible to small and medium-sized companies. Thanks to AI, product recommendations can be created largely automatically and in real time, based on the individual preferences and behavior of each customer. This enables a highly personalized and efficient customer approach without the need for a large amount of human resources.

Segmenting customer groups

There are many possible applications for AI in fraud detection. A classic example is the financial sector. Banks and credit card companies use AI to detect suspicious transactions in real time. The system analyzes the customer's behavior: When and where do they normally use their card? What is the range of amounts? If there is a sudden transaction in a foreign country or unusually high debits, the system sounds the alarm. A similar approach can also be found with online payment services such as PayPal or marketplaces such as Amazon.

AI also plays an important role in the insurance industry. Insurance fraud is a major problem that costs the industry billions every year. AI can help here by recognizing irregularities in claims reports. For example, the system can check whether a similar claim has already been submitted in the past or whether the data provided matches publicly available information. In the event of discrepancies, the claim can be automatically flagged for closer examination.

How does AI identify suspicious activity?

Manual checks and traditional rule-based systems quickly reach their limits when it comes to fraudulent activity in online retail. AI offers clear advantages here: it is constantly learning, adapts to new fraud patterns and can react in real time. This makes it an indispensable tool for online store operators who want to update their security measures.
AI systems use machine learning and data analysis to detect suspicious activity. This is done by monitoring customer behavior in real time. Here are some concrete steps on how you can use AI for fraud detection:

• Analyzing customer behavior: AI can analyze a customer's typical behavior, such as which products they look at, how often they buy and which payment methods they prefer. If the behavior deviates greatly from the norm, this can indicate fraud. An example would be a customer who suddenly buys several expensive items in a short period of time and uses a new, unsecured payment method.
• Pattern recognition in transactions: AI recognizes recurring patterns that could indicate fraudulent activity, such as repeated failed login attempts or orders that are very different from a customer's usual shopping habit. By using neural networks, AI can recognize complex patterns that would be difficult for humans to understand.
• Anomaly detection: AI can learn what is “normal” for your store and what is not. The system can immediately identify conspicuous order patterns, unusual delivery addresses or suspicious IP addresses and react accordingly, for example by blocking the checkout process or initiating additional verification steps.

Concrete steps for implementing AI

The following steps are necessary for you as an online store operator to benefit from the advantages of AI:

• a) Choosing the right AI tools: There are numerous AI tools on the market specifically designed for e-commerce fraud prevention. Make sure to choose a solution that is easy to integrate and meets the requirements of your store. Some of the best-known tools include solutions such as Sift, Kount and Riskified, which are specifically designed for fraud prevention.
• b) Integration and training: Implementing AI requires a certain level of technical expertise. Work closely with your IT team or an external service provider to ensure that the AI is properly integrated and configured. At the same time, you should train your team so they understand how the AI works and how to respond to alerts.
• c) Testing and optimization: After implementation, it is important to continuously test and optimize the system. Fraud patterns are constantly changing, so the AI should be regularly fed with new data and adapted. Focus on continuous improvement processes and use the AI's feedback loops to increase its accuracy and efficiency.

Examples and best practices

A particularly impressive example of the successful use of AI for fraud prevention is FICO, a company specializing in credit risk management. FICO uses machine learning to detect credit card fraud in real time. The algorithms analyze millions of transactions per second and look for anomalies that could indicate fraudulent activity. By using AI, FICO was able to significantly reduce the fraud rate without compromising the customer experience.

Another example is the telecommunications sector. Telecommunications companies use AI to prevent SIM card fraud, where fraudsters try to take control of a victim's phone number. By analyzing a user's behavioral patterns, AI can detect conspicuous activity, such as frequent SIM card changes or unusual calling patterns. This makes it more difficult for fraudsters to cause damage without being noticed.

A best practice in the implementation of AI for fraud prevention is the combination of different approaches. For example, some companies rely on hybrid systems that use both rule-based and machine-learning methods. While rule-based systems recognize clear, predetermined patterns, AI is constantly learning and can also identify new, as yet unknown fraud methods. This combination maximizes the detection rate while minimizing the number of false alarms.

Data security and data protection

In addition to fraud detection and prevention, there is another major issue: data security and data protection. The challenge is to protect sensitive customer data from unauthorized access while ensuring that AI systems can work effectively. Here, companies face a fine line between security and data protection.

One of the biggest sticking points in the use of AI is the handling of the underlying data. AI systems rely on large amounts of data in order to learn and make accurate predictions. However, this also means that sensitive information such as credit card details, personal addresses or health information is processed. The security of this data is a top priority, as a data leak can not only massively damage customer trust, but also lead to significant financial losses.

Another problem is the so-called “bias” in the data. Bias means that the AI's training data can be biased, leading to incorrect or discriminatory decisions. A classic example would be when an AI system makes discriminatory decisions when granting loans because the underlying data systematically disadvantages certain groups. To avoid this, it is important to carefully curate the data and regularly check the AI systems for possible biases.

Regulatory requirements and compliance

In many industries, especially in finance and healthcare, there are strict regulatory requirements that govern the handling of data. One well-known example is the European Union's General Data Protection Regulation (GDPR), which sets out precise requirements for how personal data must be processed and protected. Companies that use AI for fraud prevention must ensure that their systems comply with these regulations.

Another important aspect is the explainability of the AI models. Regulatory requirements often demand that companies must be able to explain the decisions made by their AI systems. This is particularly relevant when it comes to decisions that have a major impact on the lives of those affected, such as the approval of a loan or an insurance policy. Explainable AI means that decision-making must be transparent and understandable so that both customers and regulators understand how a particular decision was arrived at.

Conclusion: Finding the balance between security and data protection

AI offers enormous potential in fraud detection and prevention. It can quickly and efficiently analyze large volumes of data, identify unusual patterns and thus stop fraud attempts at an early stage. However, these opportunities also come with challenges, particularly in the areas of data security and data protection. Companies must ensure that their AI systems are not only efficient, but also secure and compliant. Finding the balance between security and data protection is one of the biggest challenges of the coming years.

As a company or even as an individual, you should always keep a watchful eye on how AI is used and what data is processed. Find out about the security precautions that companies are taking and make sure that your personal data is protected in the best possible way. AI can protect us from fraud - but only if it is used responsibly and with all security and data protection aspects in mind.

With the right measures and a clear strategy, AI can make a decisive contribution to security in the digital world and help us to detect and prevent fraud more efficiently. It is up to us to use these technologies responsibly and take full advantage of the benefits without losing sight of the risks.

Published on 16.09.2024 | AI security and fraud prevention | DW