Fraud detection data analytics: A guide to leveraging data for fraud management

Online fraud poses significant challenges for businesses. In 2024, U.S. retailers faced a staggering $103 billion loss due to fraudulent returns and claims.¹ Preventing, detecting, and investigating fraud is critical to protecting businesses from financial and reputational damage.

Fraud prevention relies increasingly on data analytics. Fraud detection data analytics help stakeholders review anomalies and address online fraud.

The market for fraud detection and prevention is experiencing significant growth. In 2024, the fraud detection data analytics market was valued at approximately $28.8 billion and is projected to reach $63.2 billion by 2029.²

In this article, we’ll uncover how your business can leverage data for fraud management.

Understanding fraud in small businesses

In 2024, nearly 90% of U.S. businesses reported experiencing online fraud, with losses reaching up to 9% of their annual revenue.³

Some of the most common attack types include the following⁴:

• Phishing: A deceptive technique where attackers impersonate legitimate institutions via email, text, or fake websites to trick employees or customers into revealing sensitive information such as passwords, credit card numbers, or login credentials. This is one of the leading causes of data breaches and financial theft.

• Denial of service: In a denial of service (DoS) attack, cybercriminals flood a company’s server or network with traffic, rendering it inoperable and disrupting business operations. For small business fraud cases, even short periods of downtime can result in significant revenue loss and damage to customer trust.

• Malware: Malicious software such as viruses, worms, or trojans that infiltrate systems to steal data, monitor activity, or cause destruction. Malware is often introduced through suspicious downloads, emails, or unsecured websites.

• Ransomware: A type of malware that encrypts a company’s data and demands a ransom in exchange for the decryption key. Ransomware attacks can cripple business operations and lead to devastating financial consequences, especially for small businesses.

• Chargeback fraud: This occurs when a customer makes a legitimate purchase but later disputes the transaction to receive a refund while keeping the product or service. Chargeback fraud is especially prevalent in ecommerce and can be difficult to prove or recover from.

• Card testing: Fraudsters use automated tools to test stolen credit card numbers by making small online purchases. Once a card is confirmed to work, it’s used for larger fraudulent transactions.

• Identity theft: Criminals use stolen personal information to open fraudulent accounts, apply for loans, or make unauthorized purchases in someone else’s name. This not only affects customers but also undermines a business’s reputation and trustworthiness.

Effective ways to conduct data audits

To combat these threats, businesses should consider taking a proactive approach to fraud prevention. This includes steps to protect customer data, monitor systems for irregular activity, and prepare regular data audits. Effective data audits may include:

• Reviewing access controls and user permissions

• Verifying the integrity and accuracy of transactional data

• Assessing the security of payment systems and third-party integrations

• Checking for outdated software or unpatched vulnerabilities

• Identifying anomalies in login patterns or location data

Understanding fraud is key for successful fraud prevention. Businesses need to proactively protect customer data, identify early warnings, and prepare thorough data audits. Without proper safeguards and audits, small business fraud can result in regulatory fines, loss of customer trust, and lasting reputational harm.

The role of data analytics in fraud management

Data analytics tools allow businesses to automate fraud detection methods, giving teams the power to identify fraud patterns and mitigate risk in large data sets. These tools can analyze vast amounts of structured and unstructured information from multiple sources in real time with fast and accurate responses to suspicious activity.

Machine learning is a type of artificial intelligence that enables systems to learn from data and improve over time without being explicitly programmed. In the context of fraud prevention, machine learning models can recognize patterns of suspicious behavior and adjust their algorithms as new fraud tactics emerge—helping businesses stay ahead of threats.

Automated fraud prevention strategies use technologies like machine learning, rule-based engines, and behavioral analytics to detect and block suspicious transactions without the need for human intervention. For example, an automated system might flag a purchase made in a foreign country minutes after a local transaction, based on historical spending behavior.

Manual fraud prevention strategies, on the other hand, involve human oversight and intervention. These include internal audits, transaction reviews, employee reporting mechanisms, and visual inspection of flagged activities. While slower, they can catch context-specific fraud that machines might miss, especially in cases involving social engineering or nuanced business knowledge.

Both automated and manual anti-fraud strategies are useful. Analysis conducted by internal and external auditors can uncover hidden risks and anomalies, which can then be incorporated into detection models. Additionally, fraud detection data analytics tools can inform staff training, showing team members what phishing scams, payment fraud, and other threats look like in practice—empowering them to mitigate risk and respond quickly.

Payment fraud analytics

Payment fraud analytics refers to the use of data analysis, machine learning, and automated systems to detect, prevent, and respond to fraudulent activities in financial transactions. These fraud detection data analytics tools examine patterns in payment behavior, flag anomalies, and help businesses identify threats in real time before they cause financial damage.

Businesses can use payment fraud analytics to:

• Monitor transaction trends and flag unusual behavior, such as high-value purchases from new locations or rapid-fire transactions using different cards.

• Detect account takeovers by analyzing changes in login behavior or device use.

• Identify chargeback fraud by reviewing patterns of frequent disputes or refund claims.

• Prioritize threats by using scoring models to assess the risk level of each transaction.

By integrating payment fraud analytics into their operations, businesses can reduce losses, protect customer trust, and make informed decisions on when to block, flag, or review a transaction. Customer profiles and payment information, transactions, device information, and geolocations can all be analyzed to identify suspicious behavior, detect relationships between accounts and transactions, and compare this across channels.

These tools are especially valuable for ecommerce, fintech, and retail businesses where high transaction volumes can make manual fraud detection nearly impossible.

Implementing data analytics tools

There are a variety of fraud detection data analytics tools available. Below are brief explanations of some leading platforms:

• SEON: A flexible fraud prevention software solution that uses real-time digital footprint analysis and social signals to verify identities and detect fraud. SEON is popular with fintech and ecommerce platforms for its speed and customizability.

• Fingerprint: Specializes in device intelligence and browser fingerprinting to uniquely identify users across sessions. This fraud detection tool helps businesses detect account sharing, bot attacks, and suspicious user behavior.

• Fraud.net: A cloud-based fraud detection tool offering AI-driven insights, collaborative intelligence, and case management features. It’s ideal for enterprise-level businesses seeking end-to-end fraud management.

• Ravelin: Focuses on fraud prevention software for ecommerce and delivery-based businesses. Ravelin uses machine learning to detect fraud while reducing false positives and improving customer experience.

• Kount: An identity trust platform that leverages AI and machine learning to analyze the risk behind each interaction. Kount helps prevent payment fraud, account takeovers, and loyalty program abuse.

• Riskified: Tailored for ecommerce merchants, Riskified uses behavioral analytics and machine learning to approve more orders with fewer chargebacks. It’s a powerful fraud prevention software solution that also improves conversion rates.

Before choosing a solution, consider your business’s priorities and the features offered . An ecommerce business may achieve greater accuracy with fraud detection data analytics software designed specifically for online merchants. Other businesses might benefit more from fraud detection data analytics tools that also optimize customer conversions or access open data from social media.

A high-quality data analytics tool can also integrate seamlessly with your existing environment and databases.

Data analysis for fraud detection

As fraud techniques become more sophisticated, businesses must turn to data analysis for fraud detection to stay ahead of cybercriminals. Advanced analytics powered by machine learning allow companies to interpret vast amounts of behavioral and transactional data in real time. Below are three powerful techniques businesses can implement using data analysis for fraud detection.

Analyze user behavior

Fraud detection automation will monitor how legitimate customers interact with a business’s app or website and compares those patterns to suspicious users and bots. Real-time monitoring will consider transaction type, volume, and frequency, along with personal details pulled from user behavior such as device fingerprints, IP addresses, and even registered social media profiles. This helps spot inconsistencies that may indicate fraud attempts.

Detect anomalies

Small businesses can identify emerging fraud patterns using machine learning and constant monitoring. These systems analyze not only individual transactions but also broader data like website traffic, payment trends, and customer geolocation. For example, if a user with a billing address in one country attempts a purchase from a distant location, data analysis for fraud detection tools can intervene before the transaction becomes card not present fraud. Card not present fraud is currently projected to reach $49 billion globally by 2030.⁵

Provide seamless customer experience

Machine learning for fraud prevention can enable business reduce friction at checkout with fast approvals and secure two-factor authentication. This can increase conversion rates and reduce costly chargeback fraud, which is expected to cost businesses $28.1 billion in annual losses by 2026.⁶

Benefits of data analytics in fraud management

As fraud threats evolve, more businesses are recognizing the benefits of fraud detection data analytics in strengthening their overall fraud management strategies. In fact, investment in data analytics increased by 54% in 2024, while 79% of organizations who do so have experienced increased profits.⁷

Improved precision in detection leads to earlier identification of suspicious activity, preventing fraud before it affects customer trust or financial performance. Cost-effective fraud management can also help businesses avoid regulatory fines and audits, reputational damage, and lost revenue.

With AI fuelling the prevalence and sophistication of online fraud, the need to adopt for businesses to adopt advanced fraud analytics is increasingly pressing—globally, the value of ecommerce fraud is forecast to rise from $44.3 billion in 2024 to $107 billion in 2029.⁸

What to consider when adopting fraud detection tools

Implementing fraud detection data analytics for risk management offers powerful tools to combat fraud, but it also presents challenges that businesses must navigate carefully. Some organizations may lack the technology, time, or internal expertise to manage the volume and complexity of data generated by sophisticated fraud detection tools. In some cases, there may be insufficient historical or contextual data to support accurate AI-driven analysis, limiting the system’s effectiveness. This makes it important for businesses to adopt only those tools they can manage and optimize fully.

Another potential issue arises in how these tools identify suspected fraud. New or evolving systems may rely on inaccurate data or immature detection rules, which can mistakenly flag legitimate customers as suspicious. This not only disrupts customer experience, but also risks losing loyal clientele due to false positives. As consumer behavior shifts rapidly—especially in online environments—machine learning systems need time to adjust and improve accuracy.

Relying on third-party fraud detection tools also introduces risks beyond internal control. A major concern is vulnerability to a data leak, particularly when sharing sensitive customer information. Alarmingly, more than 35% of all breaches in 2024 were third-party related, a figure that is likely conservative due to underreporting.⁹ This may underscore the need for careful vetting, secure integration, and strong oversight of all external fraud detection collaborations as part of a broader risk management strategy.

Fraud prevention best practices

Small and medium-sized businesses (SMBs), typically defined as companies with fewer than 500 employees, face a growing array of fraud threats, from payment scams to account takeovers. To stay competitive and secure, SMBs need to adopt fraud prevention best practices that balance efficiency, accuracy, and scalability. Leveraging technologies such as AI fraud detection and user behavior analysis, businesses can build strong fraud prevention strategies that protect both their customers and bottom line. Below are a few critical steps SMBs can take to strengthen their fraud defenses:

• Implement appropriate data analysis for fraud detection solution. Different tools are designed for different sectors, internal teams, and business sizes. Choosing the right software can protect a business from the most likely causes of fraud and can keep managing long-term risk effectively. For example, an ecommerce business trying to cut down on CNP fraud may want to look for an AI fraud detection tool focused on online payment protection. Businesses requiring real-time user behavior analysis should consider session-based systems that track detailed activity.

• Train staff to utilize data analysis for fraud detection software. Many SMBs continue to use manual review methods alongside automated tools. Training staff helps teams interpret alerts accurately, respond to threats, and help machine learning software become more precise in identifying suspicious users.

• Continually audit and monitor threats. Fraudsters are also using AI to disguise their activities, making it important for businesses to stay proactive. Ongoing audits, software updates, and team awareness are crucial to adapting to new fraud trends. Understanding emerging threats and training fraud detection tools to identify them can significantly reduce disruption.

Using PayPal for fraud protection

PayPal’s fraud protection tools use risk intelligence and machine learning technology to manage risk 24/7.

Informed by data from billions of monthly transactions and a global consumer network, PayPal’s fraud detection technology gets smarter with each transaction. Businesses of all sizes can benefit from low friction dispute resolution, chargeback protection, and reduced false declines – with no extra charges.

By investing in advanced fraud detection data analytics technologies and continuously evolving their prevention strategies, businesses can safeguard their operations and customer relationships. Tools that incorporate risk intelligence and machine learning offer valuable insights into emerging threats, helping companies act quickly and effectively. Whether it’s through real-time monitoring, behavioral analysis, or continuous staff training, building a layered, data-driven fraud prevention approach is important to mitigate fraud risk and stay resilient in today’s digital economy.