Tag: fraud prevention

With the rise of digital interactions, identity fraud has become an unassuming threat that impacts individuals, businesses, and institutions worldwide. According to the Federal Trade Commission (FTC), 5.4 million consumer reports regarding fraud and consumer protection were filed in 2023. Identity fraud, which is characterized as when an individual's personal information is stolen and used without their consent for fraudulent purposes, has devastating consequences for consumers, including financial losses, damaged credit scores, legal issues, and emotional distress. Financial institutions face damaging consequences beyond financial losses, including reputational damage, operational disruption, and regulatory scrutiny. As technology advances, so do fraudsters' tactics, making it increasingly challenging to detect and prevent identity-related crimes. So, what are financial institutions to do? Industry-leading institutions apply a layered approach to solving fraud that starts with a fraud risk assessment. What is a fraud risk assessment? When opening a new account, banks typically conduct a fraud risk assessment to verify the identity of the individual or entity applying for the account and to assess the likelihood of fraudulent activity. Banks also assess the applicant's credit history, financial background, and transaction patterns to identify red flags or suspicious activity. Advanced fraud detection tools and technologies are employed to monitor account opening activities in real-time and detect signs of fraudulent behavior. This assessment is crucial for ensuring compliance with regulatory requirements, mitigating the risk of financial loss, and safeguarding against identity theft. Understanding the importance of fraud risk assessments A fraud risk assessment is crucial for banks during account opening as it helps verify the identity of applicants and mitigate the risk of fraudulent activity. By assessing the likelihood and potential impact of identity fraud, banks can implement measures to protect customers' assets and protect against losses in their portfolio. Additionally, conducting thorough risk assessments enables banks to comply with regulatory requirements, which mandate the verification of customer identities to prevent money laundering and terrorist financing. By adhering to these regulations and implementing effective fraud detection measures, banks can enhance trust and confidence among customers, regulators, and stakeholders, reinforcing the integrity and stability of the financial system. 10 tools to consider when building an effective fraud risk assessment Several key factors should be carefully considered in an identity fraud risk assessment to ensure thorough evaluation and effective mitigation of identity fraud risks. Financial institutions should consider emerging threats and trends such as synthetic identity fraud, account takeover attacks, and social engineering scams when conducting a risk assessment. By staying abreast of evolving tactics used by fraudsters, organizations can proactively adapt their fraud prevention strategies and controls. Here are 10 tools that can help catch red flags for fraud prevention: Identity verification: Identity verification is the first line of defense against identity theft, account takeover, and other fraudulent activities. By verifying the identities of individuals before granting access to services or accounts, organizations can ensure that only legitimate users are granted access. Effective identity verification methods, such as biometric authentication, document verification, and knowledge-based authentication, help mitigate the risk of unauthorized access and fraudulent transactions. Implementing robust identity verification measures protects organizations from financial losses and reputational damage and enhances trust and confidence among customers and stakeholders. Device intelligence: Device intelligence provides insights into the devices used in online transactions, enabling organizations to identify and mitigate fraudulent activities. Organizations can detect suspicious behavior indicative of fraudulent activity by analyzing device-related data such as IP addresses, geolocation, device fingerprints, and behavioral patterns. Device intelligence allows organizations to differentiate between legitimate users and fraudsters, enabling them to implement appropriate security measures, such as device authentication or transaction monitoring. Phone data: Phone and Mobile Network Operator (MNO) data offers valuable insights into the mobile devices and phone numbers used in transactions. By analyzing MNO data such as subscriber information, call records, and location data, organizations can verify the authenticity of users and detect suspicious activities. MNO data enables organizations to confirm the legitimacy of phone numbers, detect SIM swapping or account takeover attempts, and identify fraudulent transactions. Leveraging MNO data allows organizations to strengthen their fraud prevention measures, enhance customer authentication processes, and effectively mitigate the risk of fraudulent activities in an increasingly mobile-driven environment. Email attributes: Email addresses serve as a primary identifier and communication channel for users in digital transactions. Organizations can authenticate user identities, confirm account ownership, and detect suspicious activities such as phishing attempts or identity theft by verifying email addresses. Analyzing email addresses enables organizations to identify patterns of fraudulent behavior, block unauthorized access attempts, and enhance security measures. Furthermore, email address validation helps prevent fraudulent transactions, safeguard sensitive information, and protect against financial losses and reputational damage. Leveraging email addresses as part of fraud prevention strategies enhances trustworthiness in digital interactions. Address verification: Address verification provides essential information for authenticating user identities and detecting suspicious activities. By verifying addresses, organizations can confirm the legitimacy of user accounts, prevent identity theft, and detect fraudulent transactions. Address validation enables organizations to ensure that the provided address matches the user's identity and reduces the risk of fraudulent activities such as account takeover or shipping fraud. Behavioral analytics: Behavioral analytics enables organizations to detect anomalies and patterns indicative of fraudulent activity. By analyzing user behavior, such as transaction history, navigation patterns, and interaction frequency, organizations can identify deviations from normal behavior and flag suspicious activities for further investigation. Behavioral analytics allows organizations to create profiles of typical user behavior and detect deviations that may signal fraud, such as unusual login times or transaction amounts. Consortia: Consortia facilitate collaboration and information sharing among organizations to combat fraudulent activities collectively. By joining forces through consortia, organizations can leverage shared data, insights, and resources to more effectively identify emerging fraud trends, patterns, and threats. Consortia enables participating organizations to benefit from a broader and more comprehensive view of fraudulent activities, enhancing their ability to detect and prevent fraud. Risk engines: Risk engines enable real-time analysis of transaction data and user behavior to detect and mitigate fraudulent activities. By leveraging advanced algorithms and machine learning techniques, risk engines assess the risk associated with each transaction and user interaction, flagging suspicious activities for further investigation or intervention. Risk engines help organizations identify anomalies, patterns, and trends indicative of fraudulent behavior, allowing for timely detection and prevention of fraud. Additionally, risk engines can adapt and evolve over time to stay ahead of emerging threats, enhancing their effectiveness in mitigating fraud. Orchestration streamlines and coordinates the various components of a fraud detection and prevention strategy. By orchestrating different fraud prevention tools, technologies, and processes, organizations can optimize their efforts to combat fraud effectively. Orchestration allows for seamless integration and automation of workflows, enabling real-time data analysis and rapid response to emerging threats. Step-up authentication: Step-up authentication provides an additional layer of security to verify users' identities during high-risk transactions or suspicious activities. By requiring users to provide additional credentials or undergo further authentication steps, such as biometric verification or one-time passcodes, organizations can mitigate the risk of unauthorized access and fraudulent transactions. Step-up authentication allows organizations to dynamically adjust security measures based on the perceived risk level, ensuring that stronger authentication methods are employed when necessary. By layering these tools effectively businesses remove gaps that fraudsters would typically exploit. Learn more

As we step into 2025, the convergence of credit and fraud risk has become more pronounced than ever. With fraudsters leveraging emerging technologies and adapting rapidly to new defenses, risk managers need to adopt forward-thinking strategies to protect their organizations and customers. Here are the top fraud trends and actionable resolutions to help you stay ahead of the curve this year. 1. Combat synthetic identity fraud with advanced AI models The trend: Synthetic identity fraud is surging, fueled by data breaches and advanced AI tooling. Fraudsters are combining genuine credentials with fabricated details, creating identities that evade traditional detection methods. Resolution: Invest in sophisticated identity validation tools that leverage advanced AI models. These tools can differentiate between legitimate and fraudulent identities, ensuring faster and more accurate creditworthiness assessments. Focus on integrating these solutions seamlessly into your customer onboarding process to enhance both security and user experience. 2. Strengthen authentication against deepfakes The trend: Deepfake technology is putting immense pressure on existing authentication systems, particularly in high-value transactions and account takeovers. Resolution: Adopt a multilayered authentication strategy that combines voice and facial biometrics with ongoing transaction monitoring. Dynamic authentication methods that evolve based on user behavior and fraud patterns can effectively counter these advanced threats. Invest in solutions that ensure digital interactions remain secure without compromising convenience. 3. Enhance detection of payment scams and APP fraud The trend: Authorized Push Payment (APP) fraud and scams are increasingly difficult to detect because they exploit legitimate customer behaviors. Resolution: Collaborate with industry peers and explore centralized consortia to share insights and develop robust detection strategies. Focus on monitoring both inbound and outbound transactions to identify anomalies, particularly payments to mule accounts. 4. Optimize Your Fraud Stack for Efficiency and Effectiveness The trend: Outdated device and network solutions are no match for GenAI-enhanced fraud tactics. Resolution: Deploy a layered fraud stack with persistent device ID technology, behavioral analytics, and GenAI-driven anomaly detection. Begin with frictionless first-tier tools to filter out low-hanging fraud vectors, reserving more advanced and costly tools for sophisticated threats. Regularly review and refine your stack to ensure it adapts to evolving fraud patterns. 5. Build collaborative relationships with fraud solution vendors The trend: Vendors offer unparalleled industry insights and long-tail data to help organizations prepare for emerging fraud trends. Resolution: Engage in reciprocal knowledge-sharing with your vendors. Leverage advisory boards and industry insights to stay informed about the latest attack vectors. Choose vendors who provide transparency and are invested in your fraud mitigation goals, turning product relationships into strategic partnerships. Turning resolutions into reality Fraudsters are becoming more ingenious, leveraging GenAI and other technologies to exploit vulnerabilities. To stay ahead of fraud in 2025, let us make fraud prevention not just a resolution but a commitment to safeguarding trust and security in a rapidly evolving landscape. Learn more

Four capabilities to consider for improved coverage and customer experience. Identity verification during account opening is the foundation for building trust between consumers and businesses. Consumers expect a seamless and convenient experience, and with the ease and optionality of online banking, are willing to look for alternatives that offer less friction. According to Experian research, 92% of consumers feel it's important for the businesses they deal with online to identify or recognize them on a repeated basis accurately, but only 16% have high confidence that this is happening. The disconnect between consumers’ expectations for online identity verification and the digital experiences they encounter is leading to reduced satisfaction and increased abandonment during new account opening processes. According to recent research by Experian, 38% of consumers surveyed considered ending a new account opening mid-way through the process due to poor experience. In addition, the same research found that nearly one-fifth of consumers had moved their business elsewhere because of this. Amidst the quest for convenience lies a pressing concern: ensuring the integrity of accounts being opened and protecting against fraud. Businesses continue to experience increasing fraud losses, Juniper Research forecasts that merchant losses from online payment fraud will exceed $362 billion globally between 2023 and 2028, with losses of $91 billion alone in 2028. Identity verification serves as the first line of defense in protecting both financial institutions and consumers. By verifying the identities of individuals before granting them access to services, businesses can mitigate the risk of identity theft, account takeover, and other forms of fraud. Four capabilities to consider when building out an identity verification strategy Personally Identifiable Information (PII) dataComparing consumer input data to a comprehensive data set helps effectively validate the consumer without disrupting customer experience. Details like name, address, date of birth, and social security number provide valuable identity information to verify identities quickly and accurately. Identity graphUsing an identity graph leveraging advanced analytics and data linking techniques helps prevent synthetic IDs from getting through. By mapping relationships between identity attributes, you can easily identify patterns and connections within the data and detect anomalies or inaccuracies in the information provided. Alternative data“Thin file” consumers are often rejected due to a lack of traditional data. Using alternative data like phone ownership and email data helps not only verify that the identity is real but also improves coverage, so you are not rejecting good customers. Document verificationHaving a document verification provider that seamlessly integrates into your identity verification workflow is essential for robust identity verification. Validating good users early in the account opening process helps keep fraudsters out so good users are not subject to stringent identity checks later on during onboarding. Next steps A strong identity verification process builds trust by demonstrating a commitment to protecting and safeguarding consumer data. A proper identity verification workflow would minimize the impact of friction for consumers and help organizations manage fraud and regulatory compliance by examining specific business needs on a case-by-case basis. Identifying the right mix of capabilities through analytics and feedback while utilizing the best data reduces the cost of manual verification and helps onboard good customers faster. Learn more Research conducted in March 2024 by Experian in North America

In today’s digital landscape, where data breaches and cyberattacks are rampant, businesses face increasing security challenges. One of the most prevalent threats is credential stuffing—a cyberattack in which malicious actors use stolen username and password combinations to gain unauthorized access to user accounts. As more personal and financial data gets leaked or sold on the dark web, these attacks become more sophisticated, and the consequences for businesses and consumers alike can be devastating.But there are ways to proactively fight credential stuffing attacks and protect your organization and customers. Solutions like our identity protection services and behavioral analytics capabilities powered by NeuroID, a part of Experian, are helping businesses prevent fraud and ensure a safer user experience. What is credential stuffing? Credential stuffing is based on the simple premise that many people reuse the same login credentials across multiple sites and platforms. Once cybercriminals can access a data breach, they can try these stolen usernames and passwords across many other sites, hoping that users have reused the same credentials elsewhere. This form of attack is highly automated, leveraging botnets to test vast numbers of combinations in a short amount of time. If an attacker succeeds, they can steal sensitive information, access financial accounts, or carry out fraudulent activities. While these attacks are not new, they have become more effective with the proliferation of stolen data from breaches and the increased use of automated tools. Traditional security methods—such as requiring complex passwords or multi-factor authentication (MFA)—are useful but not enough to prevent credential stuffing fully. How we can help protect against credential stuffing We offer comprehensive fraud prevention tools and multi-factor authentication solutions to help you identify and mitigate credential stuffing threats. We use advanced identity verification and fraud detection technology to help businesses assess and authenticate user identities in real-time. Our platform integrates with existing authentication and risk management solutions to provide layered protection against credential stuffing, phishing attacks, and other forms of identity-based fraud. Another key element in our offering is behavioral analytics, which goes beyond traditional methods of fraud detection by focusing on users' data entry patterns and interactions. NeuroID and Experian partner to combat credential stuffing We recently acquired NeuroID, a company specializing in behavioral analytics for fraud detection, to take the Experian digital identity and fraud platform to the next level.  Advanced behavioral analytics is a game-changer for preventing credential-stuffing attacks. While biometrics track characteristics, behavioral analytics track distinct actions. For example, with behavioral analytics, every time a person inputs information, clicks in a box, edits a field, and even hovers over something before clicking on it or adding the information to it, those actions are tracked. However, unlike biometrics, this data isn’t used to connect to a single identity. Instead, it’s information businesses can use to learn more about the experience and the intentions of someone on the site. NeuroID and Experian’s paired fraud detection capabilities offer several distinct advantages in preventing credential stuffing attacks: Real-time threat detection: Analyze thousands of behavioral signals in real-time to detect user behavior that suggests bots, fraud rings, credential stuffing attempts, or any number of other cybercriminal attack strategies.  Fraud risk scoring: Based on behavioral patterns, assign a fraud risk score to each user session. High-risk sessions can trigger additional authentication steps, such as CAPTCHA or step-up authentication, helping to stop credential stuffing before it occurs. Invisible to the user: Unlike traditional authentication methods, behavioral analytics work seamlessly in the background. Users do not need to take extra steps—such as answering additional security questions or entering one-time passwords. Adaptive and self-learning: As users interact with your website or app, our system continuously adapts to their unique behavior patterns. Over time, the system becomes even more effective at distinguishing between legitimate and malicious users without collecting any personally identifiable information (PII). Why behavioral data is critical in combating credential stuffing Credential stuffing attacks rely on the ability to mimic legitimate login attempts using stolen credentials. Behavioral analytics, however, can spot the subtle differences between human and bot behavior, even if the attacker has the correct credentials. By integrating behavioral analytics, you can: Prevent automated attacks: Bots often interact with websites in unnatural ways—speeding through form fields, using erratic mouse movements, or attempting logins from unusual or spoofed geographic locations. Behavioral analytics can flag these behaviors before an account is compromised. Detect account takeovers early: If a legitimate user’s account is taken over, behavioral analytics can detect the change in interactions. By monitoring behavior, businesses can detect account takeover attempts much earlier than traditional methods. Lower false positive rates: Traditional fraud prevention tools often rely on rigid rule-based systems that can block legitimate users, especially if their login patterns slightly differ from the norm. On the other hand, behavioral analytics analyzes a user's real-time behavioral data without relying on traditional static data such as passwords or personal information. This minimizes unnecessary flags on legitimate customers (while still detecting suspicious activity). Improve customer experience: Since behavioral analytics is invisible to users and requires no extra friction (like answering security questions), the login and transaction verification process is much smoother. Customers are not inconvenienced, and businesses can reduce the risk of fraud without annoying their users. The future of credential stuffing prevention Credential stuffing is a growing threat in today’s interconnected world, but with the right solutions, businesses can significantly reduce the risk of these attacks. By integrating our fraud prevention technologies and behavioral analytics capabilities, you can stay ahead of the curve in securing user identities and preventing unauthorized access. The key benefits of combining traditional identity verification methods with behavioral analytics are higher detection rates, reduced friction for legitimate users, and an enhanced user experience overall. In an era of increasingly sophisticated cybercrime, using data-driven behavioral insights to detect user riskiness is no longer just a luxury—it’s a necessity. Learn more Watch webinar

Bots have been a consistent thorn in fraud teams’ side for years. But since the advent of generative AI (genAI), what used to be just one more fraud type has become a fraud tsunami. This surge in fraud bot attacks has brought with it:  A 108% year-over-year increase in credential stuffing to take over accounts1  A 134% year-over-year increase in carding attacks, where stolen cards are tested1  New account opening fraud at more than 25% of businesses in the first quarter of 2024  While fraud professionals rush to fight back the onslaught, they’re also reckoning with the ever-evolving threat of genAI. A large factor in fraud bots’ new scalability and strength, genAI was the #1 stress point identified by fraud teams in 2024, and 70% expect it to be a challenge moving forward, according to Experian’s U.S. Identity and Fraud Report.  This fear is well-founded. Fraudsters are wasting no time incorporating genAI into their attack arsenal. GenAI has created a new generation of fraud bot tools that make bot development more accessible and sophisticated. These bots reverse-engineer fraud stacks, testing the limits of their targets’ defenses to find triggers for step-ups and checks, then adapt to avoid setting them off.   How do bot detection solutions fare against this next generation of bots?  The evolution of fraud bots   The earliest fraud bots, which first appeared in the 1990s2 , were simple scripts with limited capabilities. Fraudsters soon began using these scripts to execute basic tasks on their behalf — mainly form spam and light data scraping. Fraud teams responded, implementing bot detection solutions that continued to evolve as the threats became more sophisticated.   The evolution of fraud bots was steady — and mostly balanced against fraud-fighting tools — until genAI supercharged it. Today, fraudsters are leveraging genAI’s core ability (analyzing datasets and identifying patterns, then using those patterns to generate solutions) to create bots capable of large-scale attacks with unprecedented sophistication. These genAI-powered fraud bots can analyze onboarding flows to identify step-up triggers, automate attacks at high-volume times, and even conduct “behavior hijacking,” where bots record and replicate the behaviors of real users.  How next-generation fraud bots beat fraud stacks  For years, a tried-and-true tool for fraud bot detection was to look for the non-human giveaways: lightning-fast transition speeds, eerily consistent keystrokes, nonexistent mouse movements, and/or repeated device and network data were all tell-tale signs of a bot. Fraud teams could base their bot detection strategies off of these behavioral red flags.  Stopping today’s next-generation fraud bots isn’t quite as straightforward. Because they were specifically built to mimic human behavior and cycle through device IDs and IP addresses, today’s bots often appear to be normal, human applicants and circumvent many of the barriers that blocked their predecessors. The data the bots are providing is better, too3, fraudsters are using genAI to streamline and scale the creation of synthetic identities.4 By equipping their human-like bots with a bank of high-quality synthetic identities, fraudsters have their most potent, advanced attack avenue to date.   Skirting traditional bot detection with their human-like capabilities, next-generation fraud bots can bombard their targets with massive, often undetected, attacks. In one attack analyzed by NeuroID, a part of Experian, fraud bots made up 31% of a business's onboarding volume on a single day. That’s nearly one-third of the business’s volume comprised of bots attempting to commit fraud. If the business hadn’t had the right tools in place to separate these bots from genuine users, they wouldn’t have been able to stop the attack until it was too late.   Beating fraud bots with behavioral analytics: The next-generation approach  Next-generation fraud bots pose a unique threat to digital businesses: their data appears legitimate, and they look like a human when they’re interacting with a form. So how do fraud teams differentiate fraud bots from an actual human user?  NeuroID’s product development teams discovered key nuances that separate next-generation bots from humans, and we’ve updated our industry-leading bot detection capabilities to account for them. A big one is mousing patterns: random, erratic cursor movements are part of what makes next-generation bots so eerily human-like, but their movements are still noticeably smoother than a real human’s. Other bot detection solutions (including our V1 signal) wouldn’t flag these advanced cursor movements as bot behavior, but our new signal is designed to identify even the most granular giveaways of a next-generation fraud bot.  Fraud bots will continue to evolve. But so will we. For example, behavioral analytics can identify repeated actions — down to the pixel a cursor lands on — during a bot attack and block out users exhibiting those behaviors. Our behavior was built specifically to combat next-gen challenges with scalable, real-time solutions. This proactive protection against advanced bot behaviors is crucial to preventing larger attacks.  For more on fraud bots’ evolution, download our Emerging Trends in Fraud: Understanding and Combating Next-Gen Bots report.  Learn more Sources 1 HUMAN Enterprise Bot Fraud Benchmark Report  2 Abusix 3 NeuroID 4 Biometric Update

Today’s fast-paced, digital-first hiring environment calls for a more comprehensive approach to pre-employment screening. With growing pressure on employers and HR teams to make swift, accurate, and secure hiring decisions, having access to the tools and data to enhance efficiency and security is more important than ever. By evolving beyond traditional screening methods, background screeners can better meet these needs and deliver added value to their clients.  Fraud remains a significant challenge. In fact, fraud scams resulted in a staggering $485.6 billion in losses in 20231 — and hiring teams aren’t exempt from these risks. Fraudulent resumes, synthetic identities, and the risk of non-compliance with evolving regulations create a challenging landscape for pre-employment verifications. What if there was a way to make smarter, faster, and more secure hiring decisions? This article explores how background screeners can optimize pre-employment verification processes, reduce fraud risks, and ensure compliance — all while delivering a positive candidate experience. What is pre-employment screening? Employers conduct pre-employment screenings to thoroughly evaluate job candidates and make informed hiring decisions. It’s designed to verify key details about candidates, such as their identity, employment history, and references among others to assess their suitability for a role and ensure compliance with industry regulations. Enhancing traditional screening processes For decades, pre-employment background checks have been a cornerstone of the hiring process. While effective, many traditional methods face challenges in keeping up with the evolving demands of modern hiring. Delays in hiring: Background checks can oftentimes rely on manual processes, which could extend timelines leading to delays of days or even weeks. This not only slows down hiring cycles but can make it harder for employers to compete for top talent in a tight labor market. Errors and inaccuracies: Human errors, incomplete data, and inconsistencies across systems can lead to missed insights or red flags. Fraudulent activity: As hiring becomes increasingly digital, identity theft and synthetic identities present growing challenges to verifying candidate-provided data.  Regulatory challenges: With regulations like the Equal Employment Opportunity Commission (EEOC) and Fair Credit Reporting Act (FCRA), companies must navigate complex compliance requirements to avoid legal and financial repercussions. 1 in 3 HR professionals report losing top candidates due to slow pre-employment screening processes.2 These challenges highlight the opportunity to build on existing screening practices with tools that enhance speed, provide actionable insights and prevent fraud. Adapting to the evolving fraud landscape Employment fraud is becoming increasingly sophisticated, fueled by trends like the rise of remote work and digital applications. In fact, the employment sector accounted for 45% of all false document submissions in 2023, making it the most targeted industry for fraud.3 From fake references and degrees to synthetic identities created using stolen personal information, the risks are higher than ever. Synthetic identity fraud: This form of fraud — where fake identities are created by combining real and fabricated data — makes up more than 80% of all new account fraud.4 Fake credentials: Many candidates falsify qualifications or work histories to enhance their chances of securing a role. Compliance risks: Failure to verify candidate information accurately can result in legal penalties, brand reputation damage, or internal security breaches. Modernizing pre-employment screening The good news? Experian offers advanced solutions that complement existing screening processes, empowering background screeners to deliver more efficient, secure and reliable results for their clients looking to higher faster, and with greater confidence.  Gain a more holistic view of a candidate’s risk profile: Experian’s nationwide database contains files on more than 245 million credit-active consumers, providing the most current, accurate, and comprehensive information available in the industry. Conduct real-time identity verification: Leverage a range of identity verification solutions to authenticate and verify a candidate’s identity by accessing a breadth set of non-credit and credit data sources to create a robust social footprint that defines each consumer as unique individuals. Integrate advanced fraud detection: Powered by purpose-built analytics and machine learning algorithms, Experian’s fraud detection tools can detect synthetic identities, inconsistencies, and other red flags while ensuring a seamless candidate experience. Enhance compliance efforts: Experian’s solutions are designed to help businesses navigate complex compliance requirements with ease. Fraud prevention playbook in preemployment Uncover essential strategies for fraud prevention and identity verification in employment screening. Download now The pre-employment screening landscape is evolving, and staying ahead requires tools that enhance the efficiency and effectiveness of your processes. Experian’s advanced solutions are designed to complement your existing screening services, helping you reduce fraud risks, maintain compliant, and deliver data-driven insights that empower smarter hiring decisions. Get started today Ready to transform your pre-employment verification process with fraud mitigation and identity verification solutions? Explore our innovative solutions today. Learn more 1 Nasdaq finds scams led to $486 billion in losses in 2023, 2024. 2 Research reveals Candidates’ Frustrations with Hiring Process, 2024. 3 Employment Identity Fraud: Do You Know Who You’re Hiring, 2024. 4 Report: Synthetic identity fraud is growing, 2024.

Protecting consumer information is paramount in today’s digital age, especially for financial institutions. With cyber threats on the rise, robust user authentication methods are essential to safeguard sensitive data. This guide will walk you through the various user authentication types and methods, focusing on solutions that can help financial institutions enhance their security measures and protect consumers’ personal information. Understanding user authentication types Single-factor authentication (SFA) Single-factor authentication is the most basic form of authentication, requiring only one piece of information, such as a password. While it's easy to implement, SFA has significant drawbacks, particularly in the financial sector where security is critical. Passwords can be easily compromised through phishing or brute force attacks, making SFA insufficient on its own. Two-factor authentication (2FA) Two-factor authentication uses two different factors to verify a user's identity. For example, a bank might require a consumer to enter their password and then confirm their identity with a code sent to their mobile device. This method enhances security without overcomplicating the user experience. Multi-factor authentication (MFA) Multi-factor authentication adds an extra layer of security by requiring two or more verification factors. These factors typically include something you know (a password), something you have (a token or smartphone), and something you can present with your body, such as a fingerprint or facial scan (biometric data). MFA significantly reduces the risk of unauthorized access, making it a crucial component for financial institutions. Common authentication methods Password-based authentication Passwords are the most common form of authentication. However, they come with challenges, especially in the financial sector. Weak or reused passwords can be easily exploited. Financial institutions should enforce strong password policies and educate consumers on creating secure passwords. Biometric authentication Biometric authentication uses unique biological characteristics, such as fingerprints, facial recognition, or iris scans to verify identity. This method is becoming increasingly popular in banking due to its convenience and high level of security. However, a potential drawback is that it also raises privacy concerns. Token-based authentication Token-based authentication involves the use of physical or software tokens. Physical tokens, like smart cards, generate a one-time code for login. Software tokens, such as mobile apps, provide similar functionality. This method is highly secure and is often used in financial transactions. Certificate-based authentication Certificate-based authentication uses digital certificates to establish a secure connection. This method is commonly used in secure communications within financial systems. While it offers robust security, implementing and managing digital certificates can be complex. Two-factor authentication (2FA) solutions 2FA is a practical and effective way to enhance security. Popular methods include SMS-based codes, app-based authentication, and email-based verification. Each method has its pros and cons, but all provide an additional layer of security that is vital for protecting financial data. Many financial institutions have successfully implemented two factor authentication solutions. For example, a bank might use SMS-based 2FA to verify transactions, significantly reducing fraud. Another institution might adopt app-based 2FA, offering consumers a more secure and convenient way to authenticate their identity. Multi-factor authentication (MFA) solutions MFA is essential for financial institutions aiming to enhance security. Multifactor authentication solutions can provide multiple layers of protection and ensure that even if one factor is compromised, unauthorized access is still prevented. Implementing MFA requires careful planning. Financial institutions should start by assessing their current security measures and identifying areas for improvement. It's crucial to choose MFA solutions that integrate seamlessly with existing systems. Training staff and educating consumers on the importance of MFA can also help ensure a smooth transition. Knowledge-based authentication (KBA) solutions What is KBA? Knowledge-based authentication relies on information that only the user should know, such as answers to security questions. There are two types: static KBA, which uses pre-set questions, and dynamic KBA, which generates questions based on the user's transaction history or other data. Effectiveness of KBA While KBA can be effective, it has its limitations. Static KBA is vulnerable to social engineering attacks, where fraudsters gather information about the user to answer security questions. Dynamic KBA offers more security but can be more complex to implement. Financial institutions should weigh the pros and cons of KBA and consider combining it with other methods for enhanced security. Enhancing KBA security To improve KBA security, financial institutions can combine it with other user authentication types, such as MFA or 2FA. This layered approach ensures that even if one method is compromised, additional layers of security are in place. Best practices for knowledge based authentication solutions include regularly updating security questions and using questions that are difficult for others to guess. Using authentication methods to protect consumer information Choosing the right authentication methods is crucial for financial institutions to protect consumer information and maintain trust. By understanding and implementing robust authentication solutions like MFA, 2FA, and KBA, banks and financial services can significantly enhance their security posture. As cyber threats continue to evolve, staying ahead with advanced authentication methods will be key to safeguarding sensitive data and ensuring consumer confidence. Experian’s multifactor authentication solutions can enhance your existing authentication process while reducing friction, using risk-assessment tools to apply the appropriate level of security. Learn how your organization can provide faster, more agile mobile transactions, risk protection for your business, and security and peace of mind for your consumers. Visit our website to learn more This article includes content created by an AI language model and is intended to provide general information.

There’s a common saying in the fraud prevention industry: where there’s opportunity, fraudsters are quick to follow. Recent advances in technology are providing ample new opportunities for cybercriminals to exploit. One of the most prevalent techniques being observed today is password spraying. From email to financial and health records, consumers and businesses are being impacted by this pervasive form of fraud. Password spraying attacks often fly under the radar of traditional security measures, presenting a unique and growing threat to businesses and individuals.  What is password spraying?  Also known as credential guessing, password spraying involves an attacker applying a list of commonly used passwords against a list of accounts in order to guess the correct password. When password spraying first emerged, an individual might hand key passwords to try to gain access to a user’s account or a business’s management system.   Credential stuffing is a similar type of fraud attack in which an attacker gains access to a victim’s credentials in one system (e.g., their email, etc.) and then attempts to apply those known credentials via a script/bot to a large number of sites in order to gain access to other sites where the victim might be using the same credentials. Both are brute-force attack vectors that eventually result in account takeover (ATO), compromising sensitive data that is subsequently used to scam, blackmail, or defraud the victim.  As password spraying and other types of fraud evolved, fraud rings would leverage “click farms” or “fraud farms” where hundreds of workers would leverage mobile devices or laptops to try different passwords in order to perpetrate fraud attacks on a larger scale. As technology has advanced, bot attacks fueled by generative AI (Gen AI) have taken the place of humans in the fraud ring. Now, instead of hand-keying passwords into systems, workers at fraud farms are able to deploy hundreds or thousands of bots that can work exponentially faster.  The rise and evolution of bots  Bots are not necessarily new to the digital experience — think of the chatbot on a company’s support page that helps you find an answer more quickly. These automated software applications carry out repetitive instructions mimicking human behavior. While they can be helpful, they can also be leveraged by fraudsters, to automate fraud on a brute-force attack, often going undetected resulting in substantial losses.   Generation 4 bots are the latest evolution of these malicious programs, and they’re notoriously hard to detect. Because of their slow, methodical, and deliberate human-like behavior, they easily bypass network-level controls such as firewalls and popular network-layer security.  Stopping Gen4 bots  For any company with a digital presence or that leverages digital networks as part of doing business, the threat from Gen AI enabled fraud is paramount. The traditional stack for fighting fraud including firewalls, CAPTCHA and block lists are not enough in the face of Gen4 bots. Companies at the forefront of fighting fraud are leveraging behavioral analytics to identify and mitigate Gen AI-powered fraud. And many have turned to industry leader, Neuro ID, which is now part of Experian.  Watch our on-demand webinar: The fraud bot future-shock: How to spot & stop next-gen attacks  Behavioral analytics is a key component of passive and continuous authentication and has become table stakes in the fraud prevention space. By measuring how a user interacts with a form field (e.g., a website, mobile app, etc.) our behavioral analytics solutions can determine if the user is: a potential fraudster, a bot, or a genuine user familiar with the PII entered. Because it’s available at any digital engagement, behavioral data is often the most consistent signal available throughout the customer lifecycle and across geographies. It allows risky users to be rejected or put through more rigorous authentication, while trustworthy users get a better experience, protecting businesses and consumers from Gen AI-enabled fraud.  As cyber threats evolve, so must our defenses. Password spraying exemplifies the sophisticated methods and technologies attackers now employ to scale their fraud efforts and gain access to sensitive information. To fight next-generation fraud, organizations must employ next-generation technologies and techniques to better defend themselves against this and other types of cyberattacks.  Experian’s approach embodies a paradigm shift where fraud detection increases efficiency and accuracy without sacrificing customer experience. We can help protect your company from bot attacks, fraudulent accounts and other malicious attempts to access your sensitive data. Learn more about behavioral analytics and our other fraud prevention solutions.  Learn more

The digital domain is rife with opportunities, but it also brings substantial risks, especially for organizations. Among the innovative tools that have risen to prominence for fraud detection and online security is browser fingerprinting. Whether you're looking to minimize security gaps or bolster your fraud prevention strategy, understanding how this technology works can provide a significant advantage in today’s ever-evolving fraud and identity landscape. This article explores the concept, functionality, and applications of browser fingerprinting while also examining its benefits and relevance for organizations. How does browser fingerprinting work? Browser fingerprinting is a powerful technology designed to collect unique identifying information about a user’s web browser and device. By compiling data points such as browser type, operating system, time zone, and installed plugins, browser fingerprinting creates a distinct profile — or "fingerprint"— that allows websites to recognize returning users without relying on cookies. Here’s a breakdown of its key steps: Data collection: When a user visits a website, their browser sends information, such as user-agent strings or metadata, to the website's servers. This data provides insights about their browser, device, and system. Fingerprint creation: The collected information is processed to generate a unique ID or fingerprint, representing the user's specific configuration. Tracking and analyzing: These fingerprints enable websites to track and analyze user behavior, detect anomalies, and identify users without relying on traditional tracking mechanisms like cookies. For organizations, employing technology that leverages such fingerprints adds an additional layer to identity verification, detecting discrepancies that may indicate fraud attempts. What are the different techniques? Not all browser fingerprinting methods are identical; varying approaches offer different strengths. The most common techniques used today include: Canvas fingerprinting: This method utilizes the "Canvas" element in HTML5. When a website sends a command to draw a hidden image on a user's device, the way the image is rendered reveals unique characteristics about the device's graphics hardware and software. Font fingerprinting: Font fingerprinting involves analyzing the fonts installed on a user's system. Since computers and browsers render text in slightly different ways based on their configurations, the resulting variations aid in identifying users. Plugin enumeration: Browsers and devices often come equipped with plugins or extensions like Flash or Java. Analyzing which plugins are installed, their versions, and their order helps websites build unique fingerprints. What are the benefits of browser fingerprinting? For organizations, browser fingerprinting is not just a technical marvel — it’s a strategic asset. Benefits include: Enhanced fraud detection: Browser fingerprinting detects inconsistencies within user accounts, flagging unauthorized logins, synthetic identity fraud, or account takeover fraud without introducing significant friction for legitimate users. By identifying patterns that deviate from the norm, organizations can better prepare for malicious activities. Learn more about addressing account takeover fraud. Supports multi-layered security: A single security measure often isn't enough to combat advanced fraudulent schemes. Browser fingerprinting pairs seamlessly with other fraud management tools, such as behavioral analytics and risk-based authentication, to provide robust security. See how behavioral analytics can help organizations spot and stop next-generation fraud bots. Seamless user experience: Unlike cookies or authentication codes, browser fingerprinting operates passively in the background. Users remain unaware of the process, ensuring their experience is unaffected while still maintaining security. Level up with Experian's fraud prevention tools Browser fingerprinting offers organizations a game-changing tool to secure online interactions. However, given the growing complexity of fraud threats, organizations will need additional layers of insights and protection. Experian offers integrated, AI-driven fraud prevention solutions tailor-made to tackle challenges in the digital space. By leveraging advanced technologies like browser fingerprinting alongside Experian’s solutions, organizations can safeguard their operations and uphold customer trust while maintaining a frictionless user experience. Learn more about our fraud prevention solutions This article includes content created by an AI language model and is intended to provide general information.

Despite being a decades-old technology, behavioral analytics is often still misunderstood. We’ve heard from fraud, identity, security, product, and risk professionals that exploring a behavior-based fraud solution brings up big questions, such as: What does behavioral analytics provide that I don’t get now? (Quick answer: a whole new signal and an earlier view of fraud) Why do I need to add even more data to my fraud stack? (Quick answer: it acts with your stack to add insights, not overload) How is this different from biometrics? (Quick answer: while biometrics track characteristics, behavioral analytics tracks distinct actions) These questions make sense — stopping fraud is complex, and, of course, you want to do your research to fully understand what ROI any tool will add. NeuroID, now part of Experian, is one of the only behavioral analytics-first businesses built specifically for stopping fraud. Our internal experts have been crafting behavioral-first solutions to detect everything from simple script fraud bots through to generative AI (genAI) attacks. We know how behavioral analytics works best within your fraud stack, and how to think strategically about using it to stop fraud rings, bot fraud, and other third-party fraud attacks. This primer will provide answers to the biggest questions we hear, so you can make the most informed decisions when exploring how our behavioral analytics solutions could work for you. Q1. What is behavioral analytics and how is it different from behavioral biometrics? A common mistake is to conflate behavioral analytics with behavioral biometrics. But biometrics rely on unique physical characteristics — like fingerprints or facial scans — used for automated recognition, such as unlocking your phone with Face ID. Biometrics connect a person’s data to their identity. But behavioral analytics? They don’t look at an identity. They look at behavior and predict risk. While biometrics track who a person is, behavioral analytics track what they do. For example, NeuroID’s behavioral analytics observes every time someone clicks in a box, edits a field, or hovers over a section. So, when a user’s actions suggest fraudulent intent, they can be directed to additional verification steps or fully denied. And if their actions suggest trustworthiness? They can be fast-tracked. Or, as a customer of ours put it: "Using NeuroID decisioning, we can confidently reject bad actors today who we used to take to step-up. We also have enough information on good applicants sooner, so we can fast-track them and say ‘go ahead and get your loan, we don’t need anything else from you.’ And customers really love that." - Mauro Jacome, Head of Data Science for Addi (read the full Addi case study here). The difference might seem subtle, but it’s important. New laws on biometrics have triggered profound implications for banks, businesses, and fraud prevention strategies. The laws introduce potential legal liabilities, increased compliance costs, and are part of a growing public backlash over privacy concerns. Behavioral signals, because they don’t tie behavior to identity, are often easier to introduce and don’t need the same level of regulatory scrutiny. The bottom line is that our behavioral analytics capabilities are unique from any other part of your fraud stack, full-stop. And it's because we don’t identify users, we identify intentions. Simply by tracking users’ behavior on your digital form, behavioral analytics powered by NeuroID tells you if a user is human or a bot; trustworthy or risky. It looks at each click, edit, keystroke, pause, and other tiny interactions to measure every users’ intention. By combining behavior with device and network intelligence, our solutions provide new visibility into fraudsters hiding behind perfect PII and suspicious devices. The result is reduced fraud costs, fewer API calls, and top-of-the-funnel fraud capture with no tuning or model integration on day one. With behavioral analytics, our customers can detect fraud attacks in minutes, instead of days. Our solutions have proven results of detecting up to 90% of fraud with 99% accuracy (or <1% false positive rate) with less than 3% of your population getting flagged. Q2. What does behavioral analytics provide that I don’t get now? Behavioral analytics provides a net-new signal that you can’t get from any other tools. One of our customers, Josh Eurom, Manager of Fraud for Aspiration Banking, described it this way: “You can quantify some things very easily: if bad domains are coming through you can identify and stop it. But if you see things look odd, yet you can’t set up controls, that’s where NeuroID behavioral analytics come in and captures the unseen fraud.” (read the full Aspiration story here) Adding yet another new technology with big promises may not feel urgent. But with genAI fueling synthetic identity fraud, next-gen fraud bots, and hyper-efficient fraud ring attacks, time is running out to modernize your stack. In addition, many fraud prevention tools today only focus on what PII is submitted — and PII is notoriously easy to fake. Only behavioral analytics looks at how the data is submitted. Behavioral analytics is a crucial signal for detecting even the most modern fraud techniques. Watch our webinar: The Fraud Bot Future-Shock: How to Spot and Stop Next-Gen Attacks  Q3. Why do I need to add even more data to my fraud stack? Balancing fraud, friction, and financial impact has led to increasingly complex fraud stacks that often slow conversions and limit visibility. As fraudsters evolve, gaps grow between how quickly you can keep up with their new technology. Fraudsters have no budget constraints, compliance requirements, or approval processes holding them back from implementing new technology to attack your stack, so they have an inherent advantage. Many fraud teams we hear from are looking for ways to optimize their workflows without adding to the data noise, while balancing all the factors that a fraud stack influences beyond overall security (such as false positives and unnecessary friction). Behavioral analytics is a great way to work smarter with what you have. The signals add no friction to the onboarding process, are undetectable to your customers, and live on a pre-submit level, using data that is already captured by your existing application process. Without requiring any new inputs from your users or stepping into messy biometric legal gray areas, behavioral analytics aggregates, sorts, and reviews a broad range of cross-channel, historical, and current customer behaviors to develop clear, real-time portraits of transactional risks. By sitting top-of-funnel, behavioral analytics not only doesn’t add to the data noise, it actually clarifies the data you currently rely on by taking pressure off of your other tools. With these insights, you can make better fraud decisions, faster. Or, as Eurom put it: “Before NeuroID, we were not automatically denying applications. They were getting an IDV check and going into a manual review. But with NeuroID at the top of our funnel, we implemented automatic denial based on the risky signal, saving us additional API calls and reviews. And we’re capturing roughly four times more fraud. Having behavioral data to reinforce our decision-making is a relief.” The behavioral analytics difference Since the world has moved online, we’re missing the body language clues that used to tell us if someone was a fraudster. Behavioral analytics provides the digital body language differentiator. Behavioral cues — such as typing speed, hesitation, and mouse movements — highlight riskiness. The cause of that risk could be bots, stolen information, fraud rings, synthetic identities, or any combination of third-party fraud attack strategies. Behavioral analytics gives you insights to distinguish between genuine applicants and potentially fraudulent ones without disrupting your customer’s journey. By interpreting behavioral patterns at the very top of the onboarding funnel, behavior helps you proactively mitigate fraud, reduce false positives, and streamline onboarding, so you can lock out fraudsters and let in legitimate users. This is all from data you already capture, simply tracking interactions on your site. Stop fraud, faster: 5 simple uses where behavioral analytics shine  While how you approach a behavioral analytics integration will vary based on numerous factors, here are some of the immediate, common use cases of behavioral analytics.  Detecting fraud bots and fraud rings Behavioral analytics can identify fraud bots by their frameworks, such as Puppeter or Stealth, and through their behavioral patterns, so you can protect against even the most sophisticated fourth-generation bots. NeuroID provides holistic coverage for bot and fraud ring detection — passively and with no customer friction, often eliminating the need for CAPTCHA and reCAPTCHA. With this data alone, you could potentially blacklist suspected fraud bot and fraud ring attacks at the top of the fraud prevention funnel, avoiding extra API calls. Sussing out scams and coercions When users make account changes or transactions under coercion, they often show unfamiliarity with the destination account or shipping address entered. Our real-time assessment detects these risk indicators, including hesitancy, multiple corrections, and slow typing, alerting you in real-time to look closer. Stopping use of compromised cards and stolen IDs Traditional PII methods can fall short against today’s sophisticated synthetic identity fraud. Behavioral analytics uncovers synthetic identities by evaluating how PII is entered, instead of relying on PII itself (which is often corrupted). For example, our behavioral signals can assess users’ familiarity with the billing address they’re entering for a credit card or bank account. Genuine account holders will show strong familiarity, while signs of unfamiliarity are indicators of an account under attack. Detecting money mules Our behavioral analytics solutions track how familiar users are with the addresses they enter, conducting a real-time, sub-millisecond familiarity assessment. Risk markers such as hesitancy, multiple corrections, slow typing speed raise flags for further exploration. Stopping promotion and discount abuse Our behavioral analytics identifies risky versus trustworthy users in promo and discount fields. By assessing behavior, device, and network risk, we help you determine if your promotions attract more risky than trustworthy users, preventing fraudsters from abusing discounts. Learn more about our behavioral analytics solutions. Learn more Watch webinar

As online accounts become essential for activities ranging from shopping and social media to banking, "account farming" has emerged as a significant fraud risk. This practice involves creating fake or unauthorized accounts en masse, often for malicious purposes. Understanding how account farming works, why it’s done and how businesses can protect themselves is crucial for maintaining data integrity, safeguarding customer trust and protecting your bottom line. How does account farming work? Account farming is the process of creating and cultivating multiple user accounts, often using fake or stolen identities. These accounts may look like legitimate users, but they’re controlled by a single entity or organization, usually with fraudulent intent. Here’s a breakdown of the typical steps involved in account farming: Identity generation: Account farmers start by obtaining either fake or stolen personal information. They may buy these datasets on the dark web or scrape publicly available information to make each account seem legitimate. Account creation: Using bots or manual processes, fraudsters create numerous accounts on a platform. Often, they’ll employ automated tools to expedite this process, bypassing CAPTCHA or reCAPTCHA systems or using proxy servers to mask their IP addresses and avoid detection. Warm-up phase: After initial creation, account farmers often let the accounts sit for a while, engaging in limited, non-suspicious activity to avoid triggering security alerts. This “warming up” process helps the accounts seem more authentic. Activation for fraudulent activity: Once these accounts reach a level of credibility, they’re activated for the intended purpose. This might include spamming, fraud, phishing, fake reviews or promotional manipulation. Why is account farming done? There are several reasons account farming has become a widespread problem across different industries. Here are some common motivations: Monetary gain: Fraudsters use farmed accounts to commit fraudulent transactions, like applying for loans and credit products, accessing promotional incentives or exploiting referral programs. Spam and phishing: Fake accounts enable widespread spam campaigns or phishing attacks, compromising customer data and damaging brand reputation. Data theft: By creating and controlling multiple accounts, fraudsters may access sensitive data, leading to further exploitation or resale on the dark web. Manipulating metrics and market perception: Some industries use account farming to boost visibility and credibility falsely. For example, on social media, fake accounts can be used to inflate follower counts or engagement metrics. In e-commerce, fraudsters may create fake accounts to leave fake reviews or upvote products, falsely boosting perceived popularity and manipulating purchasing decisions. How does account farming lead to fraud risks? Account farming is a serious problem that can expose businesses and their customers to a variety of risks: Financial loss: Fake accounts created to exploit promotional offers or referral programs can cause victims to experience significant financial losses. Additionally, businesses can incur costs from chargebacks or fraudulent refunds triggered by these accounts. Compromised customer experience: Legitimate customers may suffer from poor experiences, such as spam messages, unsolicited emails or fraudulent interactions. This leads to diminished brand trust, which is costly to regain. Data breaches and compliance risks: Account farming often relies on stolen data, increasing the risk of data breaches. Businesses subject to regulations like GDPR or CCPA may face hefty fines if they fail to protect consumer information adequately. READ MORE: Our Data Breach Industry Forecast predicts what’s in store for the coming year. How can businesses protect themselves from account farming fraud? As account farming tactics evolve, businesses need a proactive and sophisticated approach to detect and prevent these fraudulent activities. Experian’s fraud risk management solutions provide multilayered and customizable solutions to help companies safeguard themselves against account farming and other types of fraud. Here’s how we can help: Identity verification solutions: Experian’s fraud risk and identity verification platform integrates multiple verification methods to confirm the authenticity of user identities. Through real-time data validation, businesses can verify the legitimacy of user information provided at the account creation stage, detecting and blocking fake identities early in the process. Its flexible architecture allows companies to adapt their identity verification process as new fraud patterns emerge, helping them stay one step ahead of account farmers. Behavioral analytics: One effective way to identify account farming is to analyze user behavior for patterns consistent with automated or scripted actions (AKA “bots”). Experian’s behavioral analytics solutions, powered by NeuroID, use advanced machine learning algorithms to identify unusual behavioral trends among accounts. By monitoring how users interact with a platform, we can detect patterns common in farmed accounts, like uniform interactions or repetitive actions that don’t align with human behavior. Device intelligence: To prevent account farming fraud, it’s essential to go beyond user data and examine the devices used to create and access accounts. Experian’s solutions combine device intelligence with identity verification to flag suspicious devices associated with multiple accounts. For example, account farmers often use virtual machines, proxies or emulators to create accounts without revealing their actual location or device details. By identifying and flagging these high-risk devices, we help prevent fraudulent accounts from slipping through the cracks. Velocity checks: Velocity checks are another way to block fraudulent account creation. By monitoring the frequency and speed at which new accounts are created from specific IP addresses or devices, Experian’s fraud prevention solutions can identify spikes indicative of account farming. These velocity checks work in real-time, enabling businesses to act immediately to block suspicious activity and minimize the risk of fake account creation. Continuous monitoring and risk scoring: Even after initial account creation, continuous monitoring of user activity helps to identify accounts that may have initially bypassed detection but later engage in suspicious behavior. Experian’s risk scoring system assigns a fraud risk score to each account based on its behavior over time, alerting businesses to potential threats before they escalate. Final thoughts: Staying ahead of account farming fraud Preventing account farming is about more than just blocking bots — it’s about safeguarding your business and its customers against fraud risk. By understanding the mechanics of account farming and using a multi-layered approach to fraud detection and identity verification, businesses can protect themselves effectively. Ready to take a proactive stance against account farming and other evolving fraud tactics? Explore our comprehensive solutions today. Learn More This article includes content created by an AI language model and is intended to provide general information.

The advent of artificial intelligence (AI) is significantly transforming the landscape of real estate fraud, enabling criminals to execute complex schemes like deed theft with greater ease. A notable case involves Spelling Manor, a $137.5 million mansion in Los Angeles, where the owner alleges they are entangled in deed fraud. Scammers reportedly filed fraudulent documents that have prevented the owner from selling the estate, thwarting offers from buyers, including former Google CEO Eric Schmidt. Understanding deed/title fraud Deed fraud, also known as title or property fraud, occurs when someone illegally transfers ownership of a property without the owner’s knowledge or consent. Typically, fraudsters create fake documents or forge the owner’s signature on a deed to make it look like the property has been legally transferred to them. Once the title is in their name, they may try to sell or mortgage the property, leaving the original owner unaware until it’s too late. How deed fraud works Identify a target: Fraudsters often look for properties that appear vulnerable, such as vacant land, unoccupied homes, or properties owned by elderly individuals who may not check their records frequently. Forge documentation: Using fake IDs and forged signatures, scammers create documents that appear to show a legitimate transfer of ownership. With modern technology, these documents can look highly convincing. Record the fake deed: Fraudsters then file these documents with the local county clerk or recorder’s office. This officially changes the ownership records, making it seem as if the scammer is the legitimate owner. Exploit the ownership: Once listed as the owner, the fraudster may sell the property to an unsuspecting buyer, take out loans against it, or even rent it out. The impact on victims In the summer of 2024, Elvis Presley’s family got confronted to a forged deed scam. A fake firm, Naussany Investments, falsely claimed Lisa Marie Presley owed millions and used Graceland as collateral. They placed a foreclosure notice and attempted to auction the estate. Riley Keough filed a lawsuit, exposing the firm as fraudulent and halting the foreclosure through a judge’s injunction. Lisa Jeanine Findley, who forged documents and posed as firm employees, was arrested and charged with deed forgery fraud and identity theft. She faces up to 22 years in prison if convicted.  The FBI's Internet Crime Complaint Center does not specifically monitor deed fraud. However, in 2023, it processed a total of 9,521 real estate-related complaints defined as the loss of funds from a real estate investment, resulting in more than $145 million in losses. Victims of deed fraud can face severe financial and legal issues. They may discover the fraud only when trying to sell, refinance, or even pay taxes on the property. Reversing deed fraud typically requires a costly and time-consuming legal process, as courts must determine that the transfer was fraudulent and restore the original owner’s rights. Prevention and safeguards There are several preventive measures and fraud prevention solutions that can be established to help mitigate the risks associated with deed/title fraud. These include: For lending institutions: Enhanced ID verification: Implement multi-factor identity checks at the loan approval stage. Regular portfolio audits: Conduct periodic audits to detect unusual property transfers and title changes in their loan portfolios. For title companies: AI-driven document verification tools: Use machine learning algorithms to identify inconsistencies in deed and ownership documents. Real-time fraud monitoring: Employ analytics to track suspicious behavior patterns, such as rapid ownership changes. Seller authentication: Require biometric or multi-step identity verification for anyone claiming ownership or initiating sales. For realtors: Training and awareness: Educate realtors on how to spot warning signs of fraudulent listings and seller impersonations. Pre-transaction verification: Collaborate with title companies to validate ownership early in the listing process. Acting with the right solution Mortgage fraud is a constant threat that demands ongoing vigilance and adaptability. As fraudsters evolve their tactics, the mortgage industry must stay one step ahead to safeguard homeowners and lenders alike. With concerns over deed/title-related fraud rising, it is vital to raise awareness, strengthen preventive measures, and foster collaboration to protect the integrity of the mortgage market. By staying informed and implementing robust safeguards, we can collectively combat and prevent mortgage fraud from disrupting the financial security of individuals and the industry. Experian mortgage powers advanced capabilities across the mortgage lifecycle by gaining market intelligence, enhancing customer experience to remove friction and tapping into industry leading data sources to gain a complete view of borrower behavior. Visit our website to see how these solutions can help your business prevent deed fraud. Learn more

In 2023, mobile fraud attacks surged by over 50%.1 With people relying more on mobile devices for day-to-day activities, like banking, shopping and healthcare, fraudsters have found new ways to exploit mobile security. With phones housing such sensitive data, how can businesses ensure that the person on the other end of a mobile device is who they claim to be? Enter mobile identity verification, a process designed to protect consumers and businesses in today’s mobile-driven world. Understanding mobile identity Mobile identity refers to the digital identity associated with a mobile device. This includes information like phone numbers, SIM cards, device IDs and user credentials that uniquely identify a person or device. Verifying that the mobile identity belongs to the correct individual is crucial for secure digital transactions. What is mobile identity verification? Mobile identity verification confirms the legitimacy of users accessing services via their mobile device. This process uses personal data, biometrics and mobile network information to authenticate identity, ensuring businesses interact with real customers without unnecessary friction. Why is mobile identity verification important? The rise of mobile banking, mobile payments and other mobile-based services has increased the need for robust security measures. Cybercriminals have found ways to exploit the mobile ecosystem through SIM swapping, phishing and other fraud tactics. This makes mobile identity verification critical for businesses looking to protect sensitive customer data and prevent unauthorized access. Here are some of the key reasons why mobile identity verification is essential: Preventing fraud: Identity theft and fraud are major concerns for businesses and consumers alike. Mobile identity verification helps to reduce the risk of fraud by ensuring that the user is who they say they are. Enhancing user trust: Customers are more likely to trust a service that prioritizes their security. Businesses that implement mobile identity verification solutions provide an extra layer of protection, which can help build customer confidence. Regulatory compliance: Many industries, including finance and healthcare, are subject to strict regulations concerning data privacy and security. Mobile identity verification helps businesses meet these regulatory requirements by offering a secure way to verify customer identities. Improving user experience: While security is essential, businesses must also ensure that they do not create a cumbersome user experience. Mobile identity verification solutions offer a quick and seamless way for users to verify their identities without sacrificing security. This is especially important for onboarding new users or completing transactions quickly. How does mobile identity verification work? Mobile identity verification involves a combination of different techniques and technologies, depending on the service provider and the level of security required. Some common methods include: Biometric authentication: Biometrics like fingerprint scans, facial recognition and voice recognition are becoming increasingly popular for verifying identities. These methods are secure and convenient for users since they don't require remembering passwords or PINs. SMS-based verification: One-time passwords (OTPs) sent via SMS to a user's mobile phone are still widely used. This method links the verification process directly to the user's mobile device, ensuring that they have possession of their registered phone number. Device-based verification: By analyzing the unique identifiers of a mobile device, such as IMEI numbers, businesses can confirm that the device is registered to the user attempting to access services. This helps prevent fraud attempts from unregistered or stolen devices. Mobile network data: Mobile network operators have access to valuable information, such as the user’s location, SIM card status and network activity. By leveraging this data, businesses can further verify that the user is legitimate and actively using their mobile network as expected. Behavioral analytics: By analyzing patterns in user behavior — such as typing speed, navigation habits, and interactions with apps — mobile identity verification solutions can detect anomalies that might indicate fraudulent activity. For instance, if a user’s behavior demonstrates low-to-no familiarity with the PII they provide, it can trigger an additional layer of verification to ensure security. The role of identity solutions in mobile identity verification Mobile identity verification is just one part of a broader range of identity solutions that help businesses authenticate users and protect sensitive data. These solutions not only cover mobile devices but extend to other digital touchpoints, ensuring that organizations have a holistic, multilayered approach to identity verification across all channels. Companies that provide comprehensive identity verification solutions can help organizations build robust security infrastructures while offering seamless customer experiences. For instance, Experian offers cutting-edge solutions designed to meet the growing demand for secure and efficient identity verification and authentication. These solutions can significantly reduce fraud and improve customer satisfaction. The growing importance of digital identity In the digital age, managing and verifying identities extends beyond traditional physical credentials like driver’s licenses or social security numbers. Digital identity plays an essential role in enabling secure online transactions, personalizing user experiences and protecting individuals' privacy. However, with great convenience comes great responsibility. Businesses need to strike a balance between security and personalization to ensure they protect user data while still offering a smooth customer experience. As mobile identity verification becomes more widespread, it’s clear that safeguarding digital identity is more important than ever. To learn more about the importance of digital identity and how businesses can find the right balance between security and personalization, check out this article: Digital identity: finding the balance between personalization and security. How Experian can help Experian is at the forefront of providing innovative identity verification solutions that empower businesses to protect their customers and prevent fraud. With solutions tailored for mobile identity verification, businesses can seamlessly authenticate users while minimizing friction. Experian’s technology integrates behavioral analytics, device intelligence and mobile network data to create a comprehensive and secure identity verification process. Whether you’re looking for a complete identity verification solution or need specialized mobile identity verification services, Experian’s identity verification and authentication solutions offer the solutions and expertise your organization needs to stay secure in the evolving digital landscape. Learn More 1 Kapersky This article includes content created by an AI language model and is intended to provide general information.    

In this article...Understanding the scope of fintech fraudThe importance of fintech fraud preventionSynthetic identity (ID) fraud: A growing threatHow fintech fraud detection and prevention are evolvingGet started today The integration of technology with traditional financial services has unlocked unprecedented convenience and opportunities for consumers and businesses alike. However, this digital shift has opened the door for more sophisticated fraud tactics. With fraudsters continuously refining their methods, fintech companies must invest in advanced fintech fraud detection and prevention solutions. Understanding the scope of fintech fraud As fintech platforms expand, they also attract the attention of cybercriminals. The accessibility of digital financial services can create vulnerabilities that fraudsters exploit, executing everything from personal account takeovers to larger-scale breaches involving synthetic identities.  Source: Experian’s 2024 U.S. Identity & Fraud Report To counter these threats, fintech companies must deploy innovative fraud management solutions powered by artificial intelligence (AI), machine learning (ML), and advanced analytics. Unlike traditional methods that often rely on static rules and manual reviews, these solutions can process vast amounts of data, learn from historical patterns, and detect anomalies in real-time. This allows organizations to identify suspicious activities before they lead to significant losses. The importance of fintech fraud prevention While detecting fraud is crucial, preventing it from occurring in the first place is even more important. Fraud prevention solutions aim to create robust systems that stop fraudsters in their tracks before they can cause damage. With the rise of digital financial services, the need for proactive fraud prevention measures has never been greater. These solutions protect both consumers and businesses from financial harm, reducing the risk of financial loss and reputational damage. Advanced fraud prevention solutions employ multi-layered strategies, combining AI-driven fraud detection tools with methods such as multifactor authentication and biometric identity verification. These tools create an extra layer of security, making it difficult for fraudsters to access sensitive data or execute fraudulent transactions. Experian’s fraud prevention solutions offer businesses a comprehensive suite of tools designed to prevent various types of fraud. From real-time transaction monitoring to sophisticated user authentication methods, these solutions provide the protection businesses need to stay ahead of evolving fraud tactics. Synthetic identity (ID) fraud: A growing threat One of the most concerning forms of fraud that fintech companies face is synthetic ID fraud. This type of fraud involves the creation of a fake identity using a combination of real and fabricated information. Fraudsters often steal pieces of personal data—such as Social Security numbers or addresses—and then combine them with fictional information to create a new, synthetic identity. These synthetic identities can be used to open bank accounts, apply for credit cards, or take out loans, leaving businesses and consumers vulnerable to significant financial losses. Synthetic ID fraud is particularly difficult to detect because the synthetic identity often looks legitimate to traditional verification systems. As a result, fintech companies must deploy sophisticated fraud detection systems that can identify synthetic identities before they’re used to commit fraud. Machine learning algorithms, for instance, can analyze behavioral data, detecting discrepancies that may indicate a synthetic identity. Experian is ranked #1 by the Center for Financial Professionals (CeFPro®) for Identity and Fraud. The ranking appeared in CeFPro’s Fintech Leaders Report, a comprehensive annual study of the fintech industry. How fintech fraud detection and prevention are evolving As fraudsters continue to evolve their tactics, fintech companies must remain one step ahead by investing in cutting-edge fraud detection and prevention technologies. Real-time monitoring, predictive analytics, and biometrics are just a few of the technologies shaping the future of fraud detection. By integrating these technologies into their fraud management processes, fintech companies can offer a more secure and seamless experience for their users. With the acquisition of NeuroID, an industry leader in behavioral analytics, Experian has amplified its fraud risk suite by providing a new layer of insight into digital behavioral signals and analytics. Available through our fraud solutions on the Experian Ascend Technology PlatformTM, clients can proactively monitor and analyze a user’s real-time digital behavior, allowing them to confidently navigate the online landscape and provide frictionless customer experiences. Get started today As the fraud landscape continues to evolve, fintech companies must adopt comprehensive solutions to stay ahead of emerging threats. By doing so, they can protect themselves and their customers, ensuring the continued success of digital financial services in the years to come. To learn more, check out our fraud management and fintech solutions. Fraud management solutions Fintech solutions This article includes content created by an AI language model and is intended to provide general information.

In today’s digital age, call center fraud is a growing threat that businesses can no longer afford to ignore. As fraudsters become increasingly sophisticated, it’s crucial for companies to implement robust security measures to protect both their operations and their consumers. Various forms of call center fraud can have a significant impact on businesses. To prevent this, companies can use effective strategies including multifactor authentication solutions and account takeover prevention techniques. But first, what is call center fraud? Understanding call center fraud Call center fraud occurs when fraudsters exploit vulnerabilities in customer service operations to gain unauthorized access to sensitive information and commit identity theft. This type of fraud can take many forms, including social engineering, which occurs when a fraudster manipulates a call center agent into providing information or access, and phishing, which occurs when fraudsters use deceptive tactics to obtain confidential details from unsuspecting individuals. One of the most concerning tactics used by fraudsters is impersonation, or pretending to be legitimate consumers to gain access to accounts. Once they have access, they can make unauthorized transactions, change account details, or even take over the account entirely—a scenario known as an account takeover. The impact of these fraudulent activities can be devastating, leading to significant financial losses, damage to brand reputation, and a loss of consumer trust. Key strategies for preventing call center fraud According to recent research, account takeover fraud has increased by 330% in the past two years, projecting to cost $6.24 billion globally.[1] In addition, the number of U.S. consumers who have experienced account takeover has increased from 22% in 2021 to 29% in 2023.[2] To effectively combat call center fraud, businesses must adopt a multi-layered approach that includes advanced technological solutions, comprehensive employee training, and real-time monitoring. Here are some of the most effective strategies: 1. Implementing multifactor authentication (MFA) solutions One of the most effective ways to secure consumer interactions is by implementing multifactor authentication (MFA) solutions. MFA requires users to provide two or more verification factors to gain access to an account or complete a transaction. This adds an extra layer of security, making it significantly more difficult for fraudsters to succeed even if they have obtained some of the consumer’s information. MFA can be integrated into call center operations in several ways. For example, businesses can use voice recognition as a biometric factor, requiring consumers to verify their identity through a unique voiceprint. Other methods include sending a one-time code via text message, which the consumer must provide during the call, or using mobile app verification, where consumers approve transactions directly through their smartphones. 2. Account takeover prevention Account takeover is one of the most serious threats to call centers, as they involve fraudsters gaining control of a consumer’s account, often with disastrous consequences. To prevent account takeover, businesses can employ a combination of technological solutions and best practices. First, understanding what account takeover entails is crucial. It typically begins when a fraudster obtains some of the consumer’s personal information—often through phishing, social engineering, or a data breach. They then use this information to impersonate the consumer and convince call center agents to provide them with access to the account. To combat this, businesses can employ several account takeover prevention techniques. Anomaly detection systems can flag unusual activities, such as login attempts from unfamiliar locations or devices, prompting additional verification steps. Behavioral biometrics is another powerful tool, analyzing patterns in how users interact with their devices to detect inconsistencies that may indicate fraud. Continuous authentication, where the system continuously verifies the user’s identity throughout the session, is also effective in catching fraudsters in the act. 3. Training and awareness Technology alone may not be enough to entirely prevent call center fraud—human factors are equally important. Regular training for call center staff is essential to ensure team members can recognize and respond to potential fraud attempts. Employees should be trained to identify common tactics used by fraudsters, such as social engineering, and to follow strict verification procedures before providing any sensitive information. Awareness campaigns can also play a significant role in preventing fraud. Internally, companies should run regular campaigns to remind employees of the importance of adhering to security protocols. Externally, educating consumers about the risks of fraud and encouraging them to use security features like MFA can help reduce the likelihood of successful attacks. 4. Real-time monitoring and analytics Real-time monitoring is a critical component of an effective fraud prevention strategy. By continuously monitoring calls and transactions, businesses can quickly identify and respond to suspicious activities before they escalate. Advanced analytics tools, including voice analytics and behavior analysis, can provide valuable insights into potential fraud, allowing companies to take proactive measures. Voice analytics, for instance, can detect stress or hesitation in a caller’s voice, which may indicate that they are not who they claim to be. Behavior analysis can track how consumers typically interact with their accounts, flagging deviations from the norm as potential fraud. Continuous improvement is key here—regularly reviewing and updating monitoring protocols ensures that businesses stay ahead of evolving threats. Preventing call center fraud in your business By using a multi-layered fraud approach through a variety of authentication solutions, your business can quickly detect call center fraud without disrupting your consumers’ experience. Identify the risk Identity-based risk detection can pinpoint when a specific identity may be in the hands of fraudsters. Device intelligence solutions can recognize the risk associated with a specific device used to attempt online access. Address the risk Knowledge-based authentication (KBA) can quickly authenticate users by asking questions only they can answer, which can deter fraudsters. MFA services can generate and deliver a one-time password to a consumer’s mobile device to verify their identity in real time. Document verification allows your business to collect and verify images of identity documents uploaded from a consumer’s mobile device. Protect your business and your consumers from call center fraud Call center fraud is a significant threat that requires a proactive and comprehensive approach to prevention. By implementing strategies such as multifactor authentication solutions, account takeover prevention techniques, and robust employee training, businesses can significantly reduce their risk of falling victim to fraud. In today’s fast-paced digital world, staying vigilant and proactive is the key to safeguarding your call center against fraud. Act now to protect your business and maintain the trust of your consumers. Enable your call center to detect risk quickly and effectively with our robust fraud prevention solutions. Get started Download our identity and fraud report This article includes content created by an AI language model and is intended to provide general information. [1] Worldmetrics.org, Account Takeover Statistics: Losses to Reach $6.24 Billion Globally, 2024. [2] Security.org, Account Takeover Incidents are Rising: How to Protect Yourself in 2024.