Online shopping has become an integral part of people’s daily lives. According to the World Bank, two-thirds of adults worldwide use digital payments. While businesses have much to gain through online transactions, the risk of fraud poses a significant threat. Online shopping holds significant risk for fraudulent transactions, with worldwide credit card losses predicted to reach $43 billion in 2026.
To stay ahead of this growing problem, businesses need proactive solutions that employ cutting-edge technologies like artificial intelligence (AI). AI has numerous applications, and its use in fraud detection is a compelling way to combat fraud at scale. Let’s explore how you can leverage AI fraud detection to protect your business’s bottom line and financial information.
What is AI fraud detection?
AI fraud detection is an artificial intelligence use case that employs algorithms to analyze large datasets containing information about transactions. These fraud detection algorithms are designed to recognize patterns and identify discrepancies that indicate fraudulent activity.
How does AI work in fraud detection?
At its most basic level, AI fraud detection algorithms analyze datasets and flag anomalies. But what actually happens when an AI algorithm processes transaction data? Here are the key aspects:
- Pattern recognition: AI algorithms group similar data points together based on inherent similarities or correlations in the data. By establishing a baseline of normal activity, these algorithms can quickly detect when something is suspicious.
- Anomaly detection: Once a discrepancy is identified, fraud-detecting AI flags transactions or activities that deviate significantly from established patterns of normal behavior. For instance, if an individual suddenly makes an unusually large transaction, AI can detect the deviation and raise alerts for further investigation.
- Real-time monitoring: Identifying anomalies in a data set is one thing, but fraud happens faster than human agents are able to react. AI continuously analyzes incoming data streams and immediately blocks suspected fraudulent activity as it occurs. This real-time monitoring can prevent fraudulent transactions from costing your business revenue.
- Machine learning: Machine learning models are trained by analyzing past instances of fraudulent behavior. This enables algorithms to identify underlying patterns and features indicative of fraud, facilitating the development of predictive models. The iterative nature of machine learning allows AI systems to continuously refine their fraud detection algorithms and adapt to evolving tactics used by fraudsters.
Benefits of using AI fraud detection
Combining AI, machine learning, and fraud detection offers several advantages, resulting in significant time and cost savings for businesses:
- High accuracy rate: AI algorithms possess the capability to analyze vast amounts of data with remarkable precision. Unlike human analysts, AI does not suffer from fatigue and can process thousands of transactions per second, leading to reliable and consistent results. This ensures that fraudulent activities are detected accurately while allowing legitimate transactions to occur without friction.
- Real-time detection: By monitoring transactions and behaviors as they occur, AI can promptly identify suspicious patterns and take immediate action. Real-time detection not only saves businesses from potential losses but also enhances the customer experience by protecting their financial interests, ultimately improving trust and loyalty.
- Adaptability: Fraudsters are constantly evolving their tactics, but fraud detection AI systems are designed to keep pace. AI is capable of self-learning and adapting to new fraud patterns. As novel techniques emerge, AI continuously learns from new data and adjusts its algorithms accordingly, ensuring its effectiveness in detecting emerging threats.
- Customization and scalability: AI solutions can be customized to meet the specific needs of your businesses, regardless of size or budget constraints. Additionally, AI models can be tailored to focus on particular fraud indicators or risk factors relevant to a particular industry, providing a targeted approach to fraud prevention.
What kind of fraud can AI detect?
While there are numerous fraudulent schemes, the following are the most prevalent techniques that plague online businesses and can be spotted by AI, machine learning, and fraud detection algorithms:
- Payment fraud involves unauthorized or fraudulent transactions made using stolen payment information, such as credit card numbers or bank account details.
- Chargebacks occur when a cardholder disputes a transaction with their bank, leading to a reversal of funds from the merchant’s account. This type of fraud was estimated to cost businesses $100 billion in 2023.
- Account takeover (ATO) is unauthorized access to a user’s account, typically gained through stolen credentials or phishing. Sift’s AI-driven solution is highly effective at blocking this type of fraud and managed a 427% jump in the rate of blocked account takeovers in 2023.
- Fake account creation (account origination) involves the creation of accounts or profiles using false or stolen identity information. Fake accounts are used to commit fraudulent activities like identity theft or promotion abuse.
- Content scams & spam are the dissemination of deceptive, unsolicited content aimed at tricking recipients into disclosing personal information or engaging in fraudulent transactions.
- Return fraud, like chargeback fraud, is a type of “friendly fraud.” It occurs when people exploit return policies by returning stolen or counterfeit merchandise for refunds. It can also happen when legitimate items are returned with altered or counterfeit receipts to obtain refunds.
AI fraud detection use cases across industries
Every industry is at risk of fraud, and fraudulent activities are constantly evolving into more sophisticated techniques. The good news is that AI, machine learning, and fraud detection can be applied universally across various sectors:
Digital goods
As digital goods and services have gained popularity, so has fraud in the industry. Many businesses have limited their expansion due to fears of fraud in certain markets. However, AI-driven solutions can help protect user accounts, prevent account takeovers, and offer visibility into attack trends. With AI, businesses can make data-driven decisions about fraud prevention strategies and confidently expand into new markets.
E-commerce and retail
The e-commerce and retail sector is vast, with 70% of Americans shopping online. This large population is exposed to fraud daily, requiring a scalable fraud solution. AI fast-tracks trusted users while leveraging a global data network to stop emerging fraud before it impacts businesses. By drastically lowering dispute rates, safeguarding customers’ accounts, and protecting against the growing threat of first-party fraud, AI enables e-commerce and retail businesses to operate securely at scale.
iGaming
The iGaming market is dynamic, with rapidly changing regulations. AI can help iGaming companies maintain regulatory compliance while attracting and retaining high-value players. By providing a friction-free experience to valuable players, preventing promotional abuse and fake account creation, and avoiding regulatory penalties, AI helps protect the reputation of iGaming organizations in this highly competitive market.
Leverage Sift’s AI-powered fraud detection to help you protect your business
At Sift, transparency is a core component of our solutions. We believe it’s not enough to say that AI fraud detection “just works.” We want businesses to understand the process and see the decisions being made. While some AI models operate in a “black box” that keeps data and decision-making processes inaccessible, Sift uses Clearbox Decisioning. This approach exposes the rules, logic, signals, and insights behind the AI models’ decision-making processes, allowing users to interpret and understand the AI’s actions.
Transparency is crucial wherever AI and ML decision-making impacts critical business outcomes. Clearbox Decisioning builds trust and accountability into our technology, giving you the power to feel confident in the platform. You can make your own assessments of data accuracy and risk, ensuring that the AI’s decisions align with your business objectives.
Learn more about how Sift’s AI-powered fraud prevention can empower your business to detect and prevent fraud in real time. Discover how Sift can improve customer journeys, scale to your needs, and protect your financial bottom line.