Fraud is expected to increase and evolve in complexity in 2025, fueled by advanced technologies like AI and Fraud-as-a-Service (FaaS). Insights from industry surveys reveal emerging fraud trends, major prevention challenges, and strategies for implementing AI-driven anti-fraud solutions. Future fraud prevention will hinge on effectively combining machine learning with human oversight to ensure secure, real-time detection and heightened protection.
What Is Fraud Detection?
Fraud detection is the process of identifying and preventing attempts by criminals to gain financially through deceit, especially in online business where fraud and scams pose significant risks. It combines detection software, RiskOps tools, and manual oversight by fraud teams to manage risks effectively. A robust fraud detection program includes policies that define acceptable risk levels, training to raise company-wide awareness, and regular monitoring and reporting for compliance. Continuous updates and third-party evaluations ensure these measures remain effective and compliant, helping businesses protect themselves from fraudulent activities.
Why Is Fraud Detection Important
Fraud detection is an essential component for any online business, especially in today’s digital landscape where cyber threats are increasingly sophisticated and pervasive. With the rise of ecommerce and digital transactions, the need for effective fraud detection and prevention strategies has never been more critical.
Pervasiveness of Fraud
Fraudulent activities can manifest in various forms, impacting businesses across all sectors. A 2024 study by PwC highlights that over 60% of organizations experienced fraud-related financial losses, with nearly 30% reporting losses exceeding $1 million. Additionally, 25% of respondents indicated that fraud disrupted their business operations, while 20% noted a decrease in employee morale due to fraud incidents. These figures illustrate that fraud is not just a financial issue but also affects organizational culture and operational efficiency.
Evolving Threat Landscape
The nature of fraud is constantly evolving, with a diverse range of perpetrators. In 2024, hackers accounted for 32% of fraud incidents, while customers were responsible for 28%. This shift necessitates a robust response from businesses, as traditional methods of fraud detection may no longer suffice against such dynamic threats. Without real-time fraud detection technologies, businesses risk falling victim to increasingly sophisticated attacks.
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Evaluating the Complete Impact of Fraud Costs
In today’s interconnected digital landscape, fraud has become an urgent and complex challenge that impacts multiple business functions, extending beyond legal and compliance to include areas like marketing and customer experience. Global fraud costs have soared to $5.13 trillion annually, with advanced technologies like AI enabling fraudsters to exploit digital vulnerabilities, while indirect effects—such as operational inefficiencies, customer churn, and compliance costs—intensify fraud’s financial impact. As businesses scale, the need for integrated, AI-powered fraud prevention solutions becomes essential to mitigate fraud’s total cost, protect customer trust, and support sustainable growth.
Learn how fraud impacts your business’s growth, efficiency, and customer trust—and why tackling its hidden costs is essential.
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What Are the Common Types of Fraud?
Fraud takes on many forms, and it adapts to every business model. However, there are a few recurrent attack vectors worth knowing about. These include:
- Credit card fraud: Criminals steal credit card numbers and use them to buy services or products from your company. A chargeback is then submitted, for which you must cover the administrative fees.
- Account takeover fraud: more sophisticated attacks, which use identity theft (often through phishing) to steal credentials of an existing account. The end goal, however, is still the same: steal money or personal data from the original user.
- Fake accounts: Fraudsters falsify information or use stolen IDs to create a new account. A lax signup policy may allow easier onboarding for traction, but it also opens the door to bad agents. It’s one area we’ve seen a boom during the pandemic – for example in the FX trading world.
- Bonus abuse: Fraudsters use linked accounts to abuse merchant terms, whether it is to benefit from signup promotions or loyalty rewards.
- Friendly fraud: This fraud happens when the legitimate cardholder contests a payment. This is either because they forgot, regret their purchase, or maliciously anticipated a chargeback request.
- Affiliate fraud: A marketing partnership can quickly turn sour if your affiliates send bad traffic to your site on purpose. This is particularly prevalent in the iGaming industry, where unscrupulous affiliate fraudsters target PPC (pay-per-click) and PPL (pay-per-lead) acquisition models.
- Return fraud: Another attack vector, growing in popularity due to changing return policies across the ecommerce landscape. Fraudsters purchase items on your site and take advantage of your return policy to get free items, or intentionally deplete your inventory.
Today, we see bad behaviors amplified in scale and speed. As cybercriminals adapt to technological advancements, more than traditional methods is needed.
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The Best Fraud Detection and Prevention Methods
Fraud detection and prevention requires a three-pronged approach, combining education about fraud risks, anti-fraud technology and an elaborate risk strategy.
Education and Awareness
Educating employees and customers on fraud risks is crucial. Awareness can significantly reduce account takeover incidents, phishing, social engineering, and CEO fraud by teaching your staff how to recognize suspicious activities.
Anti-Fraud Features and Transaction Monitoring
A robust anti-fraud strategy involves detailed user fingerprinting through digital footprinting, social media lookups, device fingerprinting, and AML checks to identify risky users. Adding transaction monitoring enriches this approach by analyzing real-time payment data, utilizing tools like card BIN lookups to validate credit card details and identify high-risk transactions. This comprehensive data use helps spot discrepancies and prevent fraud at the payment stage.
Rules Creation and Machine Learning
Implementing custom risk rules and fraud scores is essential for adapting to evolving fraud patterns. Machine learning enhances this by analyzing large volumes of data to uncover patterns and suggest risk rules, improving detection accuracy and efficiency over time. Combining rule-based systems with machine learning allows for the dynamic and effective prevention of complex fraud attacks.
What Are the Main Challenges of Fraud Detection and Prevention?
Fraud is an inevitable byproduct of conducting business, but after identifying the most pervasive issues for your business, you can develop the resources to address them and then implement the best course of action, all while staying compliant with regulatory frameworks.
Detecting Fraud Pain Points
Key challenges in fraud prevention include:
- False positives: Fraud detection must distinguish genuine customers from fraudsters, as excessive false positives harm customer experience and block checkouts. Balancing risk thresholds is vital for business optimization.
- Evolving risks: Fraudsters adapt quickly, requiring businesses to stay proactive about potential threats and regulatory changes to avoid fines and protect reputation.
- Customer journey optimization: Collecting necessary personal data for fraud prevention can create friction, so balancing this with a company’s risk tolerance helps streamline the experience.
- Data hygiene: AI-driven fraud prevention relies on clean, well-labeled data to identify risk markers efficiently and address related challenges effectively.
Developing the Best Fraud Prevention Framework
The next step involves selecting suitable fraud prevention software, building a skilled team, and establishing a system to maintain protections. Key actions include:
- Choosing fraud detection software that matches your risk profile, supports digital ID verification, multi-channel coverage, transaction monitoring, legal compliance, and real-time data use.
- Building a dedicated fraud team, essential for regulated sectors, with specialists skilled in the chosen platform and legally required roles like a Money Laundering Reporting Officer (MLRO).
- Prioritizing education on threats like BEC and APP fraud, ensuring all staff with access are trained to recognize and prevent phishing and similar scams.
Integrating the Best Fraud Prevention System
Implementing any new system will always have hiccups as it’s ingested into your infrastructure. In fraud prevention, though, these can and should be planned for so the execution of your chosen framework doesn’t allow major mishaps. As you move forward with your fraud solution, your workflows should include:
- Regular fraud reviews: All businesses should regularly assess the effectiveness of both software and internal procedures to ensure they are optimized to fight fraud. These assessments should be carried out by both internal and external teams.
- Compliance: As the goalposts for maintaining regulatory compliance are constantly moving, all companies should proactively learn what laws their ongoing business is responsible for. At a certain scale, all companies, regardless of vertical, should at least have a designated compliance and reporting officer that monitors adherence to mandates like Customer Due Diligence (CDD), Anti-Money Laundering (AML), and data privacy laws like GDPR.
- Establish clear appetites and practices: For the most complete fraud prevention coverage, your company needs to establish a single risk appetite and assess how much risk to fraud and noncompliance you are willing to take on, balanced against potential profit. Additionally, incidences of successful fraud should have explicit workflows attached, as time and transparency can come into play during the fraud management lifecycle, especially when it comes to reporting to authorities. Of course, having an established foundation of processes also helps avoid panic in the face of cybercrime.
How Do You Choose a Fraud Detection & Prevention System?
After evaluating how you can integrate the solution, there are key differences between the systems you should consider.
- Customizable Rule Systems: Fraud platforms like SEON offer adaptable rule systems and machine learning (ML) solutions that tailor risk rules specifically for your business.
- Whitebox and Blackbox ML: ML refines risk rules over time using your business data. Blackbox ML identifies highly granular points beyond human interpretation, while Whitebox ML provides clear, decision-tree explanations for why a transaction was flagged or approved.
- Data Protection and Compliance: Fraud prevention relies on data collection, subject to strict regulations like GDPR, PSD2, AML, and KYC. Solutions like SEON provide compliance tools to support regulatory needs.
- User Experience: Prioritize solutions with intuitive interfaces to ensure ease of use, as not all software prioritizes user-friendly design.
- Monitoring KPIs and KRIs: Robust fraud tools should offer detailed reports and analytics, helping teams track metrics like detection accuracy and ROI.
- Integration and Support: Choose a solution with clear integration support, minimizing technical issues and offering help with rule customization.
- Pricing Model: With high spending on fraud prevention and tight business margins, a reasonable pricing model is as essential as the software features.
To Sum Up Fraud Identification
With a growing number of fraud-prevention tools available on the market, it can be easy for merchants to be confused. It is bad enough that companies have to deal with relentless attacks, on top of that they must now face the challenge of vetting the right solution as an important business decision.
Hopefully, this guide will serve as a good primer. By now, you should have a clearer idea of which tools make sense for your company. And remember that remaining informed, whether it’s about the latest attack techniques or cybersecurity tools, is always the best way to stay one step ahead of the fraudsters – and your competitors.
SEON Fraud APIs are highly configurable for various business use-cases to match your unique business needs
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Frequently Asked Questions
Fraud solutions can help you reduce chargeback rates, account takeover attempts (account hacking), and registration with fake IDs, amongst others. It can also help with compliance for KYC and AML checks.
The fraud detection process consists of gathering user and transaction data, feeding it to risk rules, and automatically approving or declining actions based on the results. For instance, an IP address is a data point. You can detect if it is risky or not based on whether it points to certain countries or VPNs.
For fraud detection to be effective, you need as much data as possible. That includes user data (device, IP address), payment data (card BIN, shipping address), and even behavior data (login attempts, password resets, etc.).
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