Fraud management is the practice of detecting, preventing and mitigating fraud. It encompasses a range of activities, including monitoring transactions, analysing patterns for suspicious behaviour, implementing verification systems, and educating staff and customers on how to recognise and report attempted fraud. Global losses from card fraud in 2021 were estimated at over US$30 billion, according to Statista. Any business that is susceptible to fraud – from identity theft to financial fraud or cybersecurity breaches – needs to have a fraud-management system in place. And some businesses, such as financial institutions and e-commerce platforms, have a particularly strong need for fraud-management systems.

Below, we'll cover how businesses can begin carving out their fraud-management strategies and operations – from general best practices to industry-specific considerations.

What's in this article?

• Fraud-management best practices
  
• How to choose the right fraud-management solution
  
• How Stripe can help
  

Fraud-management best practices

Businesses have a range of options when it comes to fraud management. Each business should choose its approach carefully and craft a solution that addresses its specific priorities and vulnerabilities. However, there are some fraud-management best practices that will benefit almost all businesses:

• Layered defence: adopt a multi-layered defence strategy that not only includes firewalls and antivirus software, but also real-time behavioural tracking and data encryption. Together, these layers create an intricate web that is difficult for intruders to penetrate.

• Real-time analytics: employ real-time analytics to monitor all data transactions within your organisation. Use machine-learning algorithms that can adapt and learn from new types of fraudulent activities. The faster you can identify something out of the ordinary, the faster you can act.

• Manual audits: even the best automated systems can miss some anomalies. Conduct manual audits of financial and data transactions periodically.

• Customer verification measures: use two-factor authentication (2FA), biometrics and other advanced verification methods to double-check the identities of individuals involved in transactions.

• Staff training: educate employees on the importance of fraud prevention, common tactics to watch out for and internal protocols for handling suspected incidents. Improperly trained or under-trained employees, even those with the best of intentions, can often be the weakest link in a security system. Meanwhile, a well-educated workforce can act as an effective deterrent.

• Collaboration with financial entities: work closely with banks and payment processors to identify potential red flags. Usually, these organisations have specialised knowledge and tools for spotting unusual activities before they escalate.

• Role-based access control: limit who has access to sensitive data within your organisation. Limiting data access to roles that genuinely require it can reduce the likelihood of internal fraud.

• Vendor risk management: vet your suppliers and third-party vendors carefully. Require that they adhere to your security policies and review these partnerships for potential risks on a regular basis.

• Incident response plan: maintain a thorough incident response plan that you update regularly. Every employee should know what steps to take if they suspect fraud.

• Regular updates and patches: cybercriminals are always on the lookout for software vulnerabilities to exploit. Make it more difficult for them to take advantage of outdated systems by ensuring that all of your software is up to date.

These best practices enable businesses to construct a comprehensive – and adaptable – fraud-management system. Adaptability is key, because fraudulent actors are constantly developing new methods to bypass existing security measures.

How to choose the right fraud-management solution

Consider the following important factors as you choose a fraud-management solution.

• Objectives and key performance indicators (KPIs): before you begin researching solutions, decide what you want your fraud-management solution to achieve. Whether it's reducing false positives and chargebacks or identifying new fraud patterns, setting these goals up front will help to guide your selection.

• Data integration and compatibility: evaluate how easily fraud-management systems can be integrated with your existing tech stack. Look for platforms that support API integrations or software development kits (SDKs) for an easier incorporation into your existing workflows. Understand the types of databases that these systems can work with and check if they support data migration.

• Real-time processing: fraud moves at breakneck speeds, so your chosen solution must be capable of real-time data processing and alerting. Removing latency in reporting can make a significant difference in preventing financial loss.

• Scalability: your fraud-management solution must be able to grow with your business. Look for systems that can scale easily in terms of data volume, transaction speed and geographic expansion.

• Machine learning and adaptability: a good fraud-management system should use advanced machine-learning algorithms that adapt to new fraud patterns. Research the machine-learning capabilities of any solution that you're considering. This includes the algorithms that it uses, how often the models are updated and what feedback loops are in place for ongoing optimisation.

• User interface and usability: choose a fraud-management solution with an intuitive interface to reduce training time and cut down on the learning curve.

• Cost analysis: while choosing the most budget-friendly option can be tempting, low-cost solutions might not offer all the features that you need. Factor in the up-front cost and also the long-term return on investment – including the cost of potential fraudulent activities that a cheaper, less effective system might miss.

• Customisation and flexibility: look for fraud-management systems that offer you the flexibility to adjust settings, parameters and even visual dashboards to fit your needs.

• Legal compliance and data privacy: ensure that the system complies with all relevant local and international laws (especially the General Data Protection Regulation, or GDPR, if you operate or serve customers in the European Union). Check how the system stores, processes and transmits data and whether it undergoes regular compliance audits.

• Third-party reviews and references: seek out authentic reviews and ask for client references. Sometimes, insights from businesses in a similar industry can reveal important information about a system's performance that you can't glean from a datasheet.

• Aftersales support: support after the system has been implemented can be as important as the product itself. Examine the level of support that the vendor provides and whether it is available through online resources, a dedicated support team or periodic performance check-ins.

• Red flags: beware of solutions that seem to promise too much and lack documented success stories, as well as platforms that don't provide transparent pricing or are not forthcoming about their technology's limitations.

By examining these factors systematically, you'll be able to choose a fraud-management solution that is closely aligned with your operational needs and long-term business objectives.

How Stripe can help

In addition to Radar – Stripe's flagship fraud-prevention solution – Stripe offers a comprehensive suite of solutions to help businesses address fraud risks. Here are some ways that Stripe's tools and features assist in fraud prevention and mitigation.

• Stripe Elements: Elements is a set of pre-built UI components and widgets that helps to reduce fraud by collecting sensitive payment information directly from customers.

• 3D Secure: 3D Secure offers an additional layer of security for online credit and debit card transactions. When 3D Secure is activated, cardholders are required to enter additional authentication details to make a purchase, adding an extra layer of security and reducing the likelihood of fraudulent transactions.

• Dynamic 3D Secure: Dynamic 3D Secure determines when to apply 3D Secure based on the level of risk associated with a transaction. This lets businesses benefit from the feature's added security without inconveniencing every customer with extra verification steps.

• Tokenisation: Stripe's tokenisation process ensures that sensitive data, such as card numbers, is replaced with unique identifiers (tokens). This minimises the risk that fraudulent actors will intercept and steal payment data because the card details aren't stored on the business's servers.

• Webhooks: webhooks provide real-time notifications about events in a business's Stripe account, including potentially fraudulent activity. This helps businesses to respond promptly to suspicious activities.

• Stripe Connect: for platforms that manage payments on behalf of other businesses, Stripe Connect provides monitoring to detect abnormal patterns in transactions. This helps platforms to identify and prevent potential fraud.

• Manual reviews: while automation is extremely important, Stripe allows businesses to review suspicious transactions manually. Stripe also retains detailed records of customer data and transaction histories for review.

• Dispute handling: if a customer files a chargeback or otherwise disputes a charge, Stripe can provide information about the dispute and suggestions for how the business can respond. This reduces the chance that the dispute is resolved against the business.

• Data and reporting: Stripe's Dashboard and API provide detailed transaction data that businesses can use to analyse transaction patterns, spot anomalies and refine fraud-detection strategies over time.

• Integration with external tools: Stripe is designed to work seamlessly with a range of third-party fraud-prevention and detection tools. This allows businesses to augment Stripe's built-in capabilities with specialised solutions, if needed.

Together, these tools and features form a holistic fraud prevention and mitigation framework, enabling businesses to protect themselves and their customers against a wide range of potential threats. In particular, Stripe Radar uses machine-learning tools trained using data from major global companies to detect potential fraud. Radar's machine-learning algorithm has evaluated transactions from 197 countries, giving it a comprehensive view of different regional activities and a wider scope of expertise with which to detect potential fraud.

And Stripe Radar boasts better accuracy than some fraud-management alternatives: out of billions of legitimate payments, Radar incorrectly blocks only 0.1%. It absorbs data from the entire financial stack, including payment details and information from financial giants, such as Visa, Mastercard and American Express.

Key features of Stripe Radar include:

• Use of checkout flow data: this enables Stripe's checkout tools to identify patterns automatically, such as detailed customer paths that might suggest fraudulent intent.
  
• Periodic adjustments: this feature employs dynamic adjustments to new fraud threats and the consistent refinement of algorithms to determine the most relevant attributes for fraud detection.
  
• Evaluation of red flags: Radar evaluates many signals to create a detailed device profile, helping to pinpoint suspicious devices and unusual behaviours.
  
• Use of historical data: Radar uses historical data to recognise recurring patterns, speeding up the identification of known fraudulent tactics or sources.
  
• Detection techniques: this feature uses techniques including proxy detection to spot IP spoofing or abnormal proxy usage –both of which are potential red flags for fraud.
  
• Use of a multi-signal method: this increases the accuracy of its fraud prediction.
  
• Tools designed specifically for tackling fraud: this includes features that surface the most pertinent data for reviews and insights into fraud performance and dispute rates.
  
• Customisation: this feature allows businesses to set custom rules to highlight, block or demand additional verification for particular transactions. This tailors protection to individual business requirements.
  
• Features for block and allow lists: these features allow businesses to keep tabs on trusted or blocked customers based on past actions.
  
• Integration of a unified fraud model: this allows businesses to merge their fraud data with Radar's expansive dataset.
  

Stripe Radar – with its combination of machine learning, extensive data sources and adaptive algorithms – is highly effective at detecting and blocking fraudulent transactions.

Learn more about Stripe Radar.