Individuals or groups commit first-party fraud when they misrepresent themselves or manipulate their own accounts for financial gain. They might do so by creating false identities, inflating income on loan applications, or deliberately not reporting a change in financial status to continue receiving credit or benefits. Unlike in third-party fraud, where the perpetrator exploits another person’s information, account holders commit first-party fraud themselves against businesses or financial institutions.

First-party fraud can create major losses and operational costs for businesses, both from the fraud itself and from the efforts required to detect and prevent this type of fraud. It’s also widespread: a 2023 Visa survey found that 9 in 10 small businesses in the UK reported having been a victim of first-party fraud over a 12-month period, for example. In this guide, we’ll explain how first-party fraud works, how it can impact businesses, and how to implement effective fraud prevention measures.

What’s in this article?

• How first-party fraud works
  
• How first-party fraud impacts businesses
  
• Strategies for mitigating first-party fraud
  
• How to implement effective fraud prevention measures
  
• Challenges in identifying and combating first-party fraud
  

How first-party fraud works

With first-party fraud, a fraudulent actor misrepresents their identity or provides false information to gain an unfair or illegal advantage. First-party fraud can take these forms:

• Application fraud: The fraudulent actor provides false or inaccurate information on applications for financial products such as loans, credit cards, and mortgages. This information might include exaggerated income, fabricated employment history, or a misrepresentation of the actor’s credit history.

• Chargeback fraud: A customer makes a legitimate purchase but then falsely claims to their credit card company that the transaction was fraudulent to get a refund while keeping the product or service. This is also known as friendly fraud.

• Account takeover (ATO) fraud: Fraudulent actors manipulate or coerce account holders into sharing their account information for use in illicit activities.

• Synthetic identity fraud: Fraudulent actors create fake identities by combining real and fictitious information to open accounts and obtain credit. This complex fraud can be difficult to detect.

• Bust-out schemes: A customer establishes a good credit history with an institution by making regular purchases and payments. Once they build trust with the institution and it increases their credit limit, they quickly max out their available credit and vanish without paying the debt.

• Fraudulent claims: Fraudulent actors file false claims for refunds or insurance payouts. For example, a person might deliberately damage their property or pretend it was stolen to claim insurance money.

How first-party fraud impacts businesses

First-party fraud poses substantial financial and reputational risks. This affects immediate profits and can impact the business’s long-term viability and strategic market position. Here’s how first-party fraud negatively impacts businesses:

• Financial losses: Monetary loss is the most direct impact of first-party fraud. When individuals commit fraud by not repaying loans, by maxing out credit lines without intent to repay, or by filing false insurance claims, businesses lose money.

• Chargebacks and refunds: When customers dispute legitimate transactions or claim non-receipt of goods, businesses lose the value of the sale and incur chargeback fees.

• Higher operational costs: Businesses often need to invest in advanced fraud detection and prevention systems to identify and mitigate first-party fraud. This investment includes spending on technology, employee training, and sometimes third-party services.

• Diverted resources: Dealing with fraud can be a drain on resources. Managing fraud cases and implementing anti-fraud measures require staff time and company effort that could be used to improve services or develop the business.

• Higher customer costs: Customers often bear the financial burden of fraud through higher fees, interest rates, or premiums. Businesses can become less competitive if potential customers choose lower-cost alternatives.

• Reputational damage: Successful instances of fraud, especially if they occur frequently or get a lot of publicity, can damage a business’s reputation. Customers might lose trust in the business’s ability to protect their information and manage their accounts securely.

• Regulatory and legal consequences: Depending on the jurisdiction, businesses might face regulatory scrutiny if they fail to prevent or manage fraud effectively. This could result in fines, sanctions, and legal fees.

• Insurance premiums: Businesses with more fraud claims might need to pay higher insurance premiums.

• Merchandise loss: In cases where fraudulent actors receive goods but falsely claim otherwise, businesses lose both the product and revenue.

• Customer satisfaction: Delayed shipments or increased scrutiny from fraud prevention measures can frustrate or inconvenience legitimate customers.

Strategies for mitigating first-party fraud

Here are some strategies businesses can use to mitigate the risks of first-party fraud.

• Behavioural biometrics: Use behavioural biometrics to monitor how users interact with devices (e.g., keystroke dynamics, mouse movements, navigation patterns). This technology can detect anomalies in user behaviour that might indicate fraud, even when the user’s identity appears legitimate.

• Artificial intelligence (AI) and deep learning: Use advanced AI and deep learning models that can learn from a vast array of data points and continually improve over time. These models can detect subtle patterns and correlations that humans and simpler algorithms might miss. For example, an AI model could identify possible fraud by analysing discrepancies between a user’s current application details and their historical financial behaviour across databases.

• Network analytics: Apply network analytics to examine the relationships and interactions between different accounts, devices, and IP addresses. This can uncover hidden networks of fraudulent activity and synthetic identity rings that might not be apparent through traditional analysis.

• Sentiment analysis: Use natural language processing and sentiment analysis to examine communications and social media posts related to financial transactions. This can help clarify the context of transactions and identify inconsistencies or deceptive behaviours in textual data.

• Blockchain technology: Use the blockchain for identity verification processes. By using decentralised systems, businesses can create and verify customer identities based on a universally accessible and immutable record. This reduces the chances of identity manipulation and fraud.

• Advanced anomaly detection: Use advanced statistical techniques and anomaly detection algorithms that focus on non-linear and complex fraudulent activities, which are often overlooked by conventional models. These activities might include sudden changes in financial behaviour that do not match the customer’s profile or historical patterns.

• Integrated data environments: Create an integrated data environment that consolidates data from sources like external and internal databases. This holistic view enables more comprehensive risk assessments and can identify sophisticated fraud attempts that rely on inconsistencies across different datasets.

• Real-time analysis and intervention: Deploy systems capable of analysing transactions in real time and automatically intervening to halt potentially fraudulent activities. This includes setting up automatic triggers that activate when certain thresholds are reached or anomalies are detected.

• Collaborative fraud information networks: Participate in industry-wide fraud information networks where companies share insights about fraud trends and offender tactics. This collective intelligence can improve individual predictive capabilities and response strategies.

• Regulatory technology (RegTech): Use RegTech solutions that comply with existing regulations and can also quickly adapt to new regulatory challenges. These technologies can help ensure that anti-fraud measures are effective and comply with global standards.

How to implement effective fraud prevention measures

You need to be proactive, thorough, and cover all aspects of the fraud life cycle to implement effective fraud prevention measures. Here’s a step-by-step guide to get you started.

Conduct a fraud risk assessment

• Fraud risks: Analyse your business processes, systems, and customer interactions to identify vulnerabilities that fraudulent actors could exploit.

• Fraud likelihood and impact: Evaluate the probability of different types of fraud occurring and the potential financial and reputational damage they could cause.

• Risk priorities: Focus your resources on addressing the biggest risks first.

Establish a fraud prevention strategy

• Fraud prevention plan: Outline the specific measures you will take to prevent, detect, and respond to fraud.

• Roles and responsibilities: Clearly define who is responsible for implementing and monitoring different aspects of the plan.

• Measurable goals: Establish measurable goals for reducing fraud losses and improving detection rates.

• Fraud prevention specialists: Seek guidance from experts or specialised companies who can help assess your risks, develop effective strategies, and implement appropriate measures.

Implement fraud prevention measures

• Identity verification: Strengthen your customer identification processes by using multi-factor authentication, document verification, address verification, and knowledge-based authentication.

• Transaction monitoring: Implement real-time monitoring systems to detect suspicious transactions like those involving large amounts, multiple purchases in a short period of time, or those from high-risk locations.

• Behavioural analytics: Use behavioural analytics to identify unusual customer behaviour patterns that could indicate fraud.

• Device fingerprinting: Track devices used to access accounts and flag any suspicious activity, such as using the same device to access multiple accounts.

• Data analysis: Analyse data across sources to identify patterns and trends. Use this information to refine your fraud prevention strategies.

Educate employees and customers

• Employee training: Train employees on how to identify and report suspicious activity. Raise awareness about common fraud tactics and the importance of fraud prevention.

• Customer education: Educate customers about first-party fraud and how to protect themselves. Provide tips on account security and encourage them to report any suspicious activity.

Monitor and review fraud prevention efforts

• Data review: Analyse fraud data to identify trends, patterns, and vulnerabilities.

• Performance checks: Evaluate the effectiveness of your existing strategies and identify areas for improvement.

• Updates: Make necessary adjustments to your plan based on your findings and any changes in fraud trends.

Challenges in identifying and combating first-party fraud

Because first-party fraud often involves seemingly legitimate interactions, it can be particularly difficult to detect and prevent. Here are the primary challenges businesses typically encounter when handling this type of fraud:

• Sophisticated fraud techniques: Fraudulent actors continually change their methods to avoid detection. Techniques like creating synthetic identities or manipulating personal financial information can imitate legitimate transactions and evade initial fraud screenings. These techniques are difficult to identify without advanced analytical capabilities.

• Data integration: Effective fraud detection requires a holistic view of customer activities, but integrating data across numerous platforms, systems, and data formats can be technically challenging.

• Customer experience: Overly strict fraud prevention measures can negatively affect the customer experience, such as by flagging legitimate transactions as fraudulent.

• Compliance: Laws and regulations govern fraud detection practices. Ensuring compliance with the relevant regulations requires careful planning and consideration. For example, a business must ensure aggressive anti-fraud measures don’t infringe on customer privacy.

• Cost: Effective fraud prevention requires substantial resources, including investing in technology and specialised personnel.

• Developing technology: Fraud tools and tactics change with technology, and businesses must continually update their fraud detection and prevention systems to address new trends and threats.

• Data quality and accessibility: Effective fraud detection depends on the quality and completeness of the available data. Incomplete, outdated, or inaccurate data can make it difficult to accurately detect fraudulent patterns.

• Company culture: A company culture that promotes ethical behaviour and risk awareness is essential to fraud detection. Changing the culture and employee behaviour can be challenging and requires continuous, top-down effort and commitment.