Fraud has become a cost of doing business online. In 2025, global companies lost an average 7.7% of annual revenue to fraud, and that number keeps climbing as payments, accounts, and customer interactions become fully digital.

Below, we’ll explain what financial fraud looks like today, the common fraud types faced by businesses, and the fraud prevention strategies that help organizations protect revenue, maintain customer trust, and securely scale.

What’s in this article?

• What are fraud prevention strategies?
  
• What are the common types of fraud that businesses face?
  
• How does fraud impact revenue, operations, and customer trust?
  
• How do fraud detection systems work in real time?
  
• How does machine learning help identify fraudulent transactions?
  
• Why is customer authentication critical to preventing fraud?
  
• How do businesses build a scalable fraud prevention strategy?
  
• How Stripe Radar can help
  

What are fraud prevention strategies?

Fraud prevention strategies are layered approaches to combating financial fraud, which is defined as any deliberate attempt to move money, value, or access by deception. Fraud rarely looks like a single, suspicious transaction.

Modern fraudulent actors use scripts and bots to test thousands of stolen cards in minutes, create waves of fake accounts, or probe checkout and login flows for weak points. The same technologies that help businesses grow faster, such as application programming interfaces (APIs), automation, and global payments, also increase opportunities for fraud. Fraud is a constant pressure on businesses.

What are the common types of fraud that businesses face?

Fraud has many different forms. Businesses typically face a mix of schemes that vary by industry, geography, and customer behavior.

Here are the main culprits:

• Payment card fraud: These are unauthorized purchases made with stolen card details, especially in card-not-present transactions. Businesses lose the sale, the product or service, and pay chargeback and processing fees when the transaction is disputed.

• Account takeover (ATO): Account takeover fraud involves malicious actors gaining access to legitimate customer accounts using stolen credentials, phishing, or malware. Once inside, they can make purchases, transfer funds, change account details, or lock out the real user.

• Phishing and social engineering: These methods include deceptive messages that manipulate customers or employees into sharing credentials, verification codes, or money. The attacks succeed by exploiting trust, urgency, or authority rather than technical weaknesses.

• Friendly fraud: Legitimate customers dispute valid transactions and often claim they don’t recognize the charge or were dissatisfied. Friendly fraud is a common source of chargebacks and one of the hardest to distinguish.

• Identity fraud: Someone uses stolen personal information to open accounts, access services, or pass verification checks. This ranges from simple identity theft to more coordinated misuse of real data.

• Synthetic identity fraud: Fabricated identities are built from a combination of real and invented information. These identities can pass basic checks and remain active for long periods before losses become visible.

• Insider fraud: Employers or partners abuse legitimate access, including financial manipulation, data theft, or bypassing internal controls.

How does fraud affect revenue, operations, and customer trust?

Fraud rarely stays contained. Once fraud enters a business, the impact spreads across teams, customer experience, and long-term growth. These effects can continue unchecked for a while because it can take up to 12 months for many fraud cases to be detected.

Here’s what businesses need to look for:

• Direct revenue loss: Each disputed transaction often costs far more than its face value once fees, penalties, and processing costs are included.

• Rising business costs: Manual reviews, dispute handling, and customer support consume time and resources that don’t scale as transaction volumes increase.

• Increased payment issues: When fraud rises, businesses often tighten controls and risk management. If those controls are too broad, legitimate transactions are declined, which leads to cart abandonment and lost revenue through false positives.

• Erosion of customer trust: Customers generally expect their money and data to be kept safe. Fraud incidents, account takeovers, or repeated verification failures quickly undermine confidence.

• Higher churn: Customers affected by fraud or falsely blocked are more likely to disengage permanently, even after a single bad experience.

• Regulatory and partner risk: Persistent fraud issues can trigger scrutiny from regulators, payment networks, and banking partners. In severe cases, this can lead to fines, increased monitoring, or processing restrictions.

How do fraud detection systems work in real time?

Real-time fraud detection is designed to stop fraud before funds move without impeding legitimate customers. It requires fast decisions made with partial information.

Here’s how it works:

• Signal collection: As a transaction or account action occurs, the system gathers contextual data, including device details, internet protocol (IP) and location data, transaction history, timing patterns, and behavioral cues.

• Risk assessment: The system analyzes those signals against known fraud patterns and expected customer behavior to estimate the risk of the activity.

• Instant decision-making: Based on the risk level, the system approves, blocks, or requires additional verification in milliseconds.

• Dynamic thresholds: Risk tolerance varies by transaction size, customer history, region, and product. Adaptive thresholds help prevent overcorrecting and declining good users.

• Feedback loops: The system learns from confirmed fraud, chargebacks, and disputes, which improves future decisions as fraud patterns evolve.

• Human oversight: Fraud teams focus on edge cases, investigations, and tuning strategies rather than reviewing every transaction.

How does machine learning help identify fraudulent transactions?

Machine learning strengthens fraud prevention by identifying patterns that are too complex for static rules. Models evaluate thousands of signals simultaneously, from behavior and device traits to transaction timing and historical outcomes. Every confirmed fraud case, dispute, or legitimate transaction improves the model’s ability to separate risky behavior from customer activity. Trained models deliver real-time risk scores in milliseconds, even at massive transaction volumes.

Machine learning both learns patterns from real behavior at scale and adapts as those patterns shift. As criminals change strategies, models learn from new data instead of relying on fixed assumptions. By understanding nuance and context, machine learning can approve transactions that simple rules would unnecessarily block.

Human involvement is essential. Machine learning surfaces risk, then fraud teams use those insights to guide policy, investigate anomalies, and respond to emerging threats.

Why is customer authentication critical to preventing fraud?

Fraudulent actors typically succeed by pretending to be someone else. Strong authentication will stop that impersonation early.

Here’s how customer authentication closes that door:

• No unauthorized access: Authentication blocks fraudulent actors even when credentials are compromised.

• Less payment fraud exposure: Step-up verification during checkout makes stolen payment details far less useful.

• Account protection: Proof-of-possession or customer identity requirements raise the cost of account abuse.

Many regions mandate stronger customer verification for certain transactions or account actions. Clear, well-designed authentication reassures customers that their accounts and payments are actively protected.

How do businesses build a scalable fraud prevention strategy?

Fraud prevention only works long-term if it scales alongside the business. That requires systems designed for constant change.

Here are some best practices:

• Think in systems, not tools: Effective prevention connects fraud detection, authentication, review, and response. Isolated point solutions fail as complexity grows.

• Use layered defenses across the customer route: Risks differ at account creation, login, checkout, and post-transaction. Layered controls prevent single points of failure.

• Automate by default and involve humans strategically: Automation handles the majority of decisions at speed. Human expertise is reserved for edge cases, investigations, and strategy refinement.

• Always close the loop: Outcomes such as fraud losses, false positives, and customer disputes should constantly inform future decisions.

• Balance security and experience: Scalable programs measure fraud reduction and conversion together, not in isolation.

• Design for evolution: The strongest strategies assume fraud tactics will change and build processes to test, adjust, and deploy updates without disruption.

• Ensure teams have shared metrics: Clear ownership and common goals help teams respond faster and avoid working at cross-purposes.

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 trillion in payments annually. This scale uniquely enables Radar to accurately 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.