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Lending fraud – what is it? Lending fraud is a deceptive practice in which individuals or entities intentionally provide false or misleading information during the loan application process to secure credit or financial gain. This can include using fake identities, inflating income, forging documentation, or applying for loans without the intention of repayment.   The consequences are significant: lenders suffer financial losses, consumers experience identity theft or damaged credit scores, and the economic system bears increased risk and regulatory scrutiny. Loan fraud is a growing concern across consumer, commercial, and mortgage lending sectors, affecting institutions of all sizes. How do I safeguard my organization from loan fraud?    Preventing lending fraud is a complex, ongoing challenge that requires a multi-layered and holistic approach. As fraud tactics become more sophisticated, especially with the rise of generative AI and digital lending channels, financial institutions must continually evolve their defenses.  Strong identity verification is the first line of defense. Lenders should implement advanced authentication tools beyond basic KYC (Know Your Customer) checks. This includes biometric verification, document verification, and device intelligence —technologies that assess the authenticity of the user and the device used during the application process. These tools can help detect synthetic identities — false identities created using a blend of real and fabricated information — increasingly used in loan fraud schemes.  Another crucial strategy is real-time data analytics and behavioral monitoring. Lenders can quickly identify anomalies that may indicate fraudulent activity by analyzing applicant behavior, credit history, device usage patterns, and geolocation data in real time. For example, if an applicant submits multiple loan applications from different IP addresses in a short time frame, that could raise a red flag for potential lending fraud.  Employee training and awareness are also essential. Frontline staff must be equipped to identify warning signs, such as inconsistencies in application documents or rushed, high-pressure loan requests. Regular fraud prevention training helps employees stay alert and aligned with the organization’s risk management protocols.  57% of financial institutions reported direct fraud losses exceeding $500,000 in the past year, with 25% exceeding $1 million.1 Consumers reported losing more than $12.5 billion to fraud in 2024, which represents a 25% increase over the prior year.2 In addition, robust internal controls and auditing mechanisms are critical in prevention. Organizations should regularly audit loan origination processes and investigate unusual approval patterns to detect insider fraud or systemic vulnerabilities.  Finally, consumer education is a vital, often overlooked, aspect of combating loan fraud. Lenders should provide resources to help customers understand the risks of identity theft, encourage them to monitor their credit reports regularly, and empower them to report any suspicious activity. A well-informed customer base can be a valuable early warning system for fraud.  With digital lending becoming the norm, preventing lending fraud means staying ahead of increasingly tech-savvy fraudsters. Leveraging data, technology, and education together builds a stronger, more resilient fraud defense framework.  Lending fraud + Experian – How we can help  With access to the industry’s most advanced fraud detection and identity verification tools, partnering with us gives you a potent edge in combating lending fraud. As a global leader in data, analytics, and technology, our comprehensive and accurate sets of consumer information enable you to spot risks that might be invisible through conventional means. Our approach combines rich data insights with powerful machine learning algorithms, delivering fraud prevention tools that are intelligent, scalable, and highly adaptive.  Our fraud detection technologies are designed to protect every stage of the lending lifecycle. From real-time identity verification and multi-factor authentication solutions to behavioral biometrics and device intelligence, so you can detect synthetic identities, manipulated applications, and other forms of loan fraud before they lead to financial loss.  In an era where trust is currency, partnering with us doesn’t just help protect against lending fraud — it enhances your reputation as a secure, responsible lender. You gain the confidence of your customers by providing safe, streamlined lending experiences while meeting compliance requirements and reducing operational risk. With us, you’re not just reacting to fraud—you’re anticipating it, preventing it, and confidently growing your business.  Learn more 1State of Fraud Benchmark Report. Alloy. (2024). 2New FTC Data Show a Big Jump in Reported Losses to Fraud to $12.5 Billion in 2024. Federal Trade Commission. (2025, March 10). 

Now in its tenth year, Experian’s U.S. Identity and Fraud Report continues to uncover the shifting tides of fraud threats and how consumers and businesses are adapting. Our latest edition sheds light on a decade of change and unveils what remains consistent: trust is still the cornerstone of digital interactions. This year’s report draws on insights from over 2,000 U.S. consumers and 200 businesses to explore how identity, fraud and trust are evolving in a world increasingly shaped by generative artificial intelligence (GenAI) and other emerging technologies. Highlights: Over a third of companies are using AI, including generative AI, to combat fraud. 72% of business leaders anticipate AI-generated fraud and deepfakes as major challenges by 2026. Nearly 60% of companies report rising fraud losses, with identity theft and payment fraud as top concerns. Digital anxiety persists with 57% of consumers worried about doing things online. Ready to go deeper? Explore the full findings and discover how your organization can lead with confidence in an evolving fraud landscape. Download report Watch on-demand webinar Read press release  

In early 2025, European authorities shut down a cybercriminal operation called JokerOTP, responsible for over 28,000 phishing attacks across 13 countries. According to Forbes, the group used one-time password (OTP) bots to bypass two-factor authentication (2FA), netting an estimated $10 million in fraudulent transactions. It's just one example of how fraudsters are exploiting digital security gaps with AI and automation. What is an OTP bot? An OTP bot is an automated tool designed to trick users into revealing their one-time password, a temporary code used in multifactor authentication (MFA). These bots are often paired with stolen credentials, phishing sites or social engineering to bypass security steps and gain unauthorized access. Here’s how a typical OTP bot attack works: A fraudster logs in using stolen credentials. The user receives an OTP from their provider. Simultaneously, the OTP bot contacts the user via SMS, call or email, pretending to be the institution and asking for the OTP. If the user shares the OTP, the attacker gains control of the account. The real risk: account takeover OTP bots are often just one part of a larger account takeover strategy. Once a bot bypasses MFA, attackers can: Lock users out of their accounts Change contact details Drain funds or open fraudulent lines of credit Stopping account takeover means detecting and disrupting the attack before access is gained. That’s where strong account takeover/login defense becomes critical, monitoring suspicious login behaviors and recognizing high-risk signals early. How accessible are OTP bots? Mentions of OTP bots on dark web forums jumped 31% in 2024. Bot services offering OTP bypass tools were being sold for just $10 to $50 per attack. One user on a Telegram-based OTP bot platform reported earning $50,000 in a month.   The barrier to entry for fraudsters is low, and these figures highlight just how easy and profitable it is to launch OTP bot attacks at scale. The evolution of fraud bots OTP bots are one part of the rising wave of fraud bots. According to our report, The Fraud Attack Strategy Guide, bots accounted for 30% of fraud attempts at the beginning of 2024. By the end of the year, that number had risen to 80% — a nearly threefold increase in just 12 months. Today’s fraud bots are more dynamic and adaptive than before. They go beyond simple scripts, mimicking human behavior, shifting tactics in real time and launching large-scale bot attacks across platforms. Some bypass OTPs entirely or refine their tactics with each failed attempt. With generative AI in the mix, bot-based fraud is getting faster, cheaper and harder to detect. Effective fraud defense now depends on detecting intent, analyzing behavior in real time and stopping threats earlier in the process. Read this blog: Learn more about identifying and stopping bot attacks. A cross-industry problem OTP bots can target any organization that leverages 2FA, but the impact varies by sector. Financial services, fintech and buy now, pay later (BNPL) providers are top targets for OTP bot attacks due to high-value accounts, digital onboarding and reliance on 2FA. In one case outlined in The Fraud Strategy Attack Guide, a BNPL provider saw 25,000+ bot attempts in 90 days, with over 3,000 bots completing applications, bypassing OTP or using synthetic identities. Retail and e-commerce platforms face attacks designed to take over customer accounts and make unauthorized purchases using stored payment methods, gift cards or promo credits. OTP bots can help fraudsters trigger and intercept verification codes tied to checkout or login flows. Healthcare and education organizations can be targeted for their sensitive data and widespread use of digital portals. OTP bots can help attackers access patient records, student or staff accounts, or bypass verification during intake and application flows, leading to phishing, insurance fraud or data theft. Government and public sector entities are increasingly vulnerable as fraudsters exploit digital services meant for public benefits. OTP bots may be used to sign up individuals for disbursements or aid programs without their knowledge, enabling fraudsters to redirect payments or commit identity theft. This abuse not only harms victims but also undermines trust in the public system. Across sectors, the message is clear: the bots are getting in too far before being detected. Organizations across all industries need the ability to recognize bot risk at the very first touchpoint; the earlier the better. The limitations of OTP defense OTP is a strong second factor, but it’s not foolproof. If a bot reaches the OTP stage, it's highly likely that they've already: Stolen or purchased valid credentials Found a way to trigger the OTP Put a social engineering play in motion Fighting bots earlier in the funnel The most effective fraud prevention doesn’t just react to bots at the OTP step; it stops them before they trigger OTPs in the first place. But to do that, you need to understand how modern bots operate and how our bot detection solutions, powered by NeuroID, fight back. The rise of GenAI-powered bots Bot creation has become dramatically easier. Thanks to generative AI and widely available bot frameworks, fraudsters no longer need deep technical expertise to launch sophisticated attacks. Today’s Gen4 bots can simulate human-like interactions such as clicks, keystrokes, and mouse movements with just enough finesse to fool traditional bot detection tools. These bots are designed to bypass security controls, trigger OTPs, complete onboarding flows, and even submit fraudulent applications. They are built to blend in. Detecting bots across two key dimensions Our fraud detection solutions are purpose-built to uncover these threats by analyzing risk signals across two critical dimensions. 1. Behavioral patternsEven the most advanced bots struggle to perfectly mimic human behavior. Our tools analyze thousands of micro-signals to detect deviations, including: Mouse movement smoothness and randomness Typing cadence, variability and natural pauses Field and page transition timing Cursor trajectory and movement velocity Inconsistent or overly “perfect” interaction patterns By identifying unnatural rhythms or scripted inputs, we can distinguish real users from automation before the OTP step. 2. Device and network intelligenceIn parallel, our technology examines device and network indicators that often reveal fraud at scale: Detection of known bot frameworks and automation tools Device fingerprinting to flag repeat offenders Link analysis connecting devices across multiple sessions or identities IP risk, geolocation anomalies and device emulation signals This layered approach helps identify fraud rings and coordinated bot attacks, even when attackers attempt to mask their activity. A smarter way to stop bots We offer both a highly responsive, real-time API for instant bot detection and a robust dashboard for investigative analytics. This combination allows fraud teams to stop bots earlier in the funnel — before they trigger OTPs, fill out forms, or submit fake credentials — and to analyze emerging trends across traffic patterns. Our behavioral analytics, combined with device intelligence and adaptive risk modeling, empowers organizations to act on intent rather than just outcomes. Good users move forward without friction. Bad actors are stopped at the source. Ready to stop bots in their tracks? Explore Experian’s fraud prevention services. Learn more *This article includes content created by an AI language model and is intended to provide general information.

Powered by GenAI and increasingly accessible fraud tools, fraud threats are evolving faster than ever. Traditional fraud detection solutions alone are struggling to keep up with evolving fraud rings, fraud bots, and attack strategies, pushing businesses to explore smarter, more adaptive defenses. That’s why many organizations are turning to User and Entity Behavior Analytics (UEBA) as protection against growing threats, especially internal ones. But what exactly is UEBA, and how does it differ from other solutions, like behavioral analytics?

Bot fraud has long been a major concern for digital businesses, but evolving attacks at all stages in the customer lifecycle have overshadowed an ever-present issue: click fraud. Click fraud is a cross-departmental challenge for businesses, and stopping it requires a level of insight and understanding that many businesses don’t yet have. It’s left many fraud professionals asking: What is click fraud? Why is it so dangerous? How can it be prevented? What is click fraud? A form of bot fraud, click fraud occurs when bots drive fraudulent clicks to websites, digital ads, and emails. Click fraud typically exploits application flows or digital advertising; traffic from click bots appears to be genuine but is actually fraudulent, incurring excessive costs through API calls or ad clicks. These fraudulent clicks won’t result in any sales but will reveal sensitive information, inflate costs, and clutter data. What is the purpose of click fraud? It depends on the target. We've seen click bots begin (but not complete) insurance quotes or loan applications, gathering information on competitors’ rates. In other cases, fraudsters use click fraud to drive artificial clicks to ads on their sites, resulting in increased revenue from PPC/CPC advertising. The reasons behind click fraud vary widely, but, regardless of its intent, the impacts of it affect businesses deeply. The dangers of click fraud On the surface, click fraud may seem less harmful than other types of fraud. Unlike application fraud and account takeover fraud, consumers’ data isn’t being stolen, and fraud losses are relatively minuscule. But click fraud can still be detrimental to businesses' bottom lines: every API call incurred by a click bot is an additional expense, and swarms of click bots distort data that’s invaluable to fraud attack detection and customer acquisition. The impact of click fraud extends beyond that, though. Not only can click bots gather sensitive data like insurance quotes, but click fraud can also be a gateway to more insidious fraud schemes. Fraud rings are constantly looking for vulnerabilities in businesses’ systems, often using bots to probe for back-door entrances to applications and ways to bypass fraud checks. For example: if an ad directs to an unlisted landing page that provides an alternate entry to a business’s ecosystem, fraudsters can identify this through click fraud and use bots to find vulnerabilities in the alternate application process. In doing so, they lay the groundwork for larger attacks with more tangible losses. Keys to click fraud prevention Without the right tools in place, modern bots can appear indistinguishable from humans — many businesses struggle to identify increasingly sophisticated bots on their websites as a result. Allowing click fraud to remain undetected can make it extremely difficult to know when a more serious fraud attack is at your doorstep. Preventing click fraud requires real-time visibility into your site’s traffic, including accurate bot detection and analysis of bot behavior. It’s one of many uses for behavioral analytics in fraud detection: behavioral analytics identifies advanced bots pre-submit, empowering businesses to better differentiate click fraud from genuine traffic and other fraud types. With behavioral analytics, bot attacks can be detected and stopped before unnecessary costs are incurred and sensitive information is revealed. Learn more about our behavioral analytics for fraud detection.

Fake IDs have been around for decades, but today’s fraudsters aren’t just printing counterfeit driver’s licenses — they’re using artificial intelligence (AI) to create synthetic identities. These AI fake IDs bypass traditional security checks, making it harder for businesses to distinguish real customers from fraudsters. To stay ahead, organizations need to rethink their fraud prevention solutions and invest in advanced tools to stop bad actors before they gain access. The growing threat of AI Fake IDs   AI-generated IDs aren’t just a problem for bars and nightclubs; they’re a serious risk across industries. Fraudsters use AI to generate high-quality fake government-issued IDs, complete with real-looking holograms and barcodes. These fake IDs can be used to commit financial fraud, apply for loans or even launder money. Emerging services like OnlyFake are making AI-generated fake IDs accessible. For $15, users can generate realistic government-issued IDs that can bypass identity verification checks, including Know Your Customer (KYC) processes on major cryptocurrency exchanges.1 Who’s at risk? AI-driven identity fraud is a growing problem for: Financial services – Fraudsters use AI-generated IDs to open bank accounts, apply for loans and commit credit card fraud. Without strong identity verification and fraud detection, banks may unknowingly approve fraudulent applications. E-commerce and retail – Fake accounts enable fraudsters to make unauthorized purchases, exploit return policies and commit chargeback fraud. Businesses relying on outdated identity verification methods are especially vulnerable. Healthcare and insurance – Fraudsters use fake identities to access medical services, prescription drugs or insurance benefits, creating both financial and compliance risks. The rise of synthetic ID fraud Fraudsters don’t just stop at creating fake IDs — they take it a step further by combining real and fake information to create entirely new identities. This is known as synthetic ID fraud, a rapidly growing threat in the digital economy. Unlike traditional identity theft, where a criminal steals an existing person’s information, synthetic identity fraud involves fabricating an identity that has no real-world counterpart. This makes detection more difficult, as there’s no individual to report fraudulent activity. Without strong synthetic fraud detection measures in place, businesses may unknowingly approve loans, credit cards or accounts for these fake identities. The deepfake threat AI-powered fraud isn’t limited to generating fake physical IDs. Fraudsters are also using deepfake technology to impersonate real people. With advanced AI, they can create hyper-realistic photos, videos and voice recordings to bypass facial recognition and biometric verification. For businesses relying on ID document scans and video verification, this can be a serious problem. Fraudsters can: Use AI-generated faces to create entirely fake identities that appear legitimate Manipulate real customer videos to pass live identity checks Clone voices to trick call centers and voice authentication systems As deepfake technology improves, businesses need fraud prevention solutions that go beyond traditional ID verification. AI-powered synthetic fraud detection can analyze biometric inconsistencies, detect signs of image manipulation and flag suspicious behavior. How businesses can combat AI fake ID fraud Stopping AI-powered fraud requires more than just traditional ID checks. Businesses need to upgrade their fraud defenses with identity solutions that use multidimensional data, advanced analytics and machine learning to verify identities in real time. Here’s how: Leverage AI-powered fraud detection – The same AI capabilities that fraudsters use can also be used against them. Identity verification systems powered by machine learning can detect anomalies in ID documents, biometrics and user behavior. Implement robust KYC solutions – KYC protocols help businesses verify customer identities more accurately. Enhanced KYC solutions use multi-layered authentication methods to detect fraudulent applications before they’re approved. Adopt real-time fraud prevention solutions – Businesses should invest in fraud prevention solutions that analyze transaction patterns and device intelligence to flag suspicious activity. Strengthen synthetic identity fraud detection – Detecting synthetic identities requires a combination of behavioral analytics, document verification and cross-industry data matching. Advanced synthetic fraud detection tools can help businesses identify and block synthetic identities. Stay ahead of AI fraudsters AI-generated fake IDs and synthetic identities are evolving, but businesses don’t have to be caught off guard. By investing in identity solutions that leverage AI-driven fraud detection, businesses can protect themselves from costly fraud schemes while ensuring a seamless experience for legitimate customers. At Experian, we combine cutting-edge fraud prevention, KYC and authentication solutions to help businesses detect and prevent AI-generated fake ID and synthetic ID fraud before they cause damage. Our advanced analytics, machine learning models and real-time data insights provide the intelligence businesses need to outsmart fraudsters. Learn more *This article includes content created by an AI language model and is intended to provide general information. 1 https://www.404media.co/inside-the-underground-site-where-ai-neural-networks-churns-out-fake-ids-onlyfake/

Fraud rings cause an estimated $5 trillion in financial damages every year, making them one of the most dangerous threats facing today’s businesses. They’re organized, sophisticated and only growing more powerful with the advent of Generative AI (GenAI). Armed with advanced tools and an array of tried-and-true attack strategies, fraud rings have perfected the art of flying under the radar and circumventing traditional fraud detection tools. Their ability to adapt and innovate means they can identify and exploit vulnerabilities in businesses' fraud stacks; if you don’t know how fraud rings work and the right signs to look for, you may not be able to catch a fraud ring attack until it’s too late. What is a fraud ring? A fraud ring is an organized group of cybercriminals who collaborate to execute large-scale, coordinated attacks on one or more targets. These highly sophisticated groups leverage advanced techniques and technologies to breach fraud defenses and exploit vulnerabilities. In the past, they were primarily humans working scripts at scale; but with GenAI they’re increasingly mobilizing highly sophisticated bots as part of (or the entirety of) the attack. Fraud ring attacks are rarely isolated incidents. Typically, these groups will target the same victim multiple times, leveraging insights gained from previous attack attempts to refine and enhance their strategies. This iterative approach enables them to adapt to new controls and increase their impact with each subsequent attack. The impacts of fraud ring attacks far exceed those of an individual fraudster, incurring significant financial losses, interrupting operations and compromising sensitive data. Understanding the keys to spotting fraud rings is crucial for crafting effective defenses to stop them. Uncovering fraud rings There’s no single tell-tale sign of a fraud ring. These groups are too agile and adaptive to be defined by one trait. However, all fraud rings — whether it be an identity fraud ring, coordinated scam effort, or large-scale ATO fraud scheme — share common traits that produce warning signs of imminent attacks. First and foremost, fraud rings are focused on efficiency. They work quickly, aiming to cause as much damage as possible. If the fraud ring’s goal is to open fraudulent accounts, you won’t see a fraud ring member taking their time to input stolen data on an application; instead, they’ll likely copy and paste data from a spreadsheet or rely on fraud bots to execute the task. Typically, the larger the fraud ring attack, the more complex it is. The biggest fraud rings leverage a variety of tools and strategies to keep fraud teams on their heels and bypass traditional fraud defenses. Fraud rings often test strategies before launching a full-scale attack. This can look like a small “probe” preceding a larger attack, or a mass drop-off after fraudsters have gathered the information they needed from their testing phase. Fraud ring detection with behavioral analytics Behavioral analytics in fraud detection uncovers third-party fraud, from large-scale fraud ring operations and sophisticated bot attacks to individualized scams. By analyzing user behavior, organizations can effectively detect and mitigate these threats. With behavioral analytics, businesses have a new layer of fraud ring detection that doesn’t exist elsewhere in their fraud stack. At a crowd level, behavioral analytics reveals spikes in risky behavior, including fraud ring testing probes, that may indicate a forthcoming fraud ring attack, but would typically be hidden by sheer volume or disregarded as normal traffic. Behavioral analytics also identifies the high-efficiency techniques that fraud rings use, including copy/paste or “chunking” behaviors, or the use of advanced fraud bots designed to mimic human behavior. Learn more about our behavioral analytics solutions and their fraud ring detection capabilities. Learn more

Fraud never sleeps, and neither do the experts working to stop it. That’s why we’re thrilled to introduce Meet the Maker, our new video series spotlighting the brilliant minds behind Experian’s cutting-edge fraud solutions. In our first episode, Matt Ehrlich, Senior Director of Identity and Fraud Product Management, and Andrea Nighswander, Senior Director of Global Solution Strategy, share how they use data, advanced analytics, and deep industry expertise to stay ahead of fraudsters. With 35+ years of combined experience, these fraud-fighting veterans know exactly what it takes to keep bad actors at bay. Watch now for an exclusive look at the minds shaping the future of fraud prevention.    Stay tuned for more episodes featuring the visionaries driving fraud innovation.

The days of managing credit risk, fraud prevention, and compliance in silos are over. As fraud threats evolve, regulatory scrutiny increases, and economic uncertainty persists, businesses need a more unified risk strategy to stay ahead. Our latest e-book, Navigating the intersection of credit, fraud, and compliance, explores why 94% of forward-looking companies expect credit, fraud, and compliance to converge within the next three years — and what that means for your business.1 Key insights include: The line between fraud and credit risk is blurring. Many organizations classify first-party fraud losses as credit losses, distorting the true risk picture. Fear of fraud is costing businesses growth. 68% of organizations say they’re denying too many good customers due to fraud concerns. A unified approach is the future. Integrating risk decisioning across credit, fraud, and compliance leads to stronger fraud detection, smarter credit risk assessments, and improved compliance. Read the full e-book to explore how an integrated risk approach can protect your business and fuel growth. Download e-book 1Research conducted by InsightAvenue on behalf of Experian

With cybersecurity threats on the rise, organizations are turning to token-based authentication as a secure and efficient solution to safeguard sensitive data and systems. Data breaches impacted 1.1 billion individuals in 2024, a staggering 490% increase from the previous year.1 Token-based authentication is a method of verifying a user's identity through digital tokens rather than traditional means such as passwords. These tokens are temporary and serve as access keys, allowing users to securely interact with systems, applications, and networks. The goal of token authentication is to strengthen security while improving the user experience. Instead of relying solely on static credentials (like passwords), which can be intercepted or stolen, leveraging a type of multi-factor authentication like tokens adds an additional layer of security by functioning as dynamic access credentials. How token-based authentication works Token authentication unfolds through a series of steps to ensure robust security. Here's a simplified breakdown of how it works in practice: User request and authentication: When a user attempts to log in, they provide their credentials (e.g., username and password). These credentials are verified by the authentication server. Token generation: After verifying the user's credentials, the server generates a token — a cryptographically secured string often containing information like the user's ID and permissions. Token sent to the user: The generated token is sent back to the user or their device to confirm authentication. Token usage for access: Now authenticated, the user uses the token to access the system or application. The token is passed along with each request to ensure the user is authorized to proceed. Token validation: Each time a token is presented to the server, its integrity and expiration are verified. If the token is valid, access is granted; if not, the session is terminated. Token expiration and renewal: Tokens are typically temporary and expire after a set period. Users must either re-authenticate or renew the token for continued access. This limits the time window during which a stolen token can be misused. Types of token authentication methods Token authentication comes in different forms to meet various use case requirements. Common types include: JSON Web Tokens (JWT) Lightweight, self-contained, and easily transferred between clients and servers, JWT is one of the most widely used token formats. It includes claims, which are bits of information about a user encoded within the token, such as roles and permissions. Example: A financial application uses JWTs to ensure only registered users can access private account data. OAuth tokens OAuth is an industry-standard authorization protocol that uses tokens to grant limited access to applications without revealing the user's credentials. It’s often used for third-party service integration. Example: When you log into an e-commerce platform using your Google credentials, OAuth tokens authorize access. Session tokens These are temporary tokens stored on the server to track authenticated sessions, commonly used in web applications to ensure secure browsing. Example: Online banking platforms rely on session tokens for secure user sessions. Refresh tokens Refresh tokens are designed to renew access tokens without requiring the user to log in repeatedly. They extend session durations while maintaining a high-security standard. Example: A subscription service app uses refresh tokens to maintain a seamless user experience without frequent logouts. Benefits of token-based authentication Token-based authentication offers several advantages that make it a preferred security measure for organizations of all sizes. Enhanced security: Tokens reduce the risk of breaches as they are temporary and encrypted. They’re also specific to sessions, applications, or devices, meaning unauthorized users cannot reuse stolen tokens effectively. Elimination of password reliance: Tokens reduce dependence on static passwords, which are often reused and susceptible to brute-force attacks. This bolsters an organization’s overall cybersecurity posture. Improved user experience: Token authentication allows for more seamless interactions by minimizing the need for repeated logins. With features like single sign-on (SSO), users enjoy convenient access to multiple platforms with a single token. Scalability: Tokens are flexible and can adapt to varied business use cases, making them ideal for organizations of all scales. For instance, application programming interfaces (APIs) and microservices can communicate securely via token exchanges. Supports compliance: Token-based authentication helps organizations meet regulatory compliance requirements by offering robust access control and audit trails. This is critical for industries like finance, healthcare, and e-commerce. Cost efficiency: While implementing token-based authentication may require an initial investment, it reduces long-term risks and costs associated with data breaches, system downtime, and customer trust. How Experian can help strengthen your authentication process At Experian, we recognize that strong security measures should never compromise the user experience. That's why we offer cutting-edge identity solutions tailored to meet the needs of organizations. Our tools allow you to integrate token-based authentication seamlessly into your systems while ensuring compliance with security best practices and industry regulations. Are you ready to take your business's security and user experience to the next level? Visit us online today. Learn more 12024-2025 Data Breach Response Guide, Experian, 2024. This article includes content created by an AI language model and is intended to provide general information.

Picture this: you’re sipping your morning coffee when an urgent email from your CEO pops up in your inbox, requesting sensitive information. Everything about it seems legit — their name, email address, even their usual tone. But here’s the twist: it’s not actually them. This is the reality of spoofing attacks. And these scenarios aren’t rare. According to the Federal Bureau of Investigation (FBI), spoofing/phishing is the most common type of cybercrime.¹   In these attacks, bad actors disguise their identity to trick individuals or systems into believing the communication is from a trusted source. Whether it’s email spoofing, caller ID spoofing, or Internet Protocol (IP) spoofing, the financial and reputational consequences can be severe. By understanding how these attacks work and implementing strong defenses, organizations can reduce their risk and protect sensitive information. Let’s break down the key strategies for staying one step ahead of cybercriminals. What is a spoofing attack? A spoofing attack occurs when a threat actor impersonates a trusted source to gain access to sensitive information, disrupt operations or manipulate systems. Common types of spoofing attacks include: Email spoofing: Fraudulent emails are carefully crafted to mimic legitimate senders, often including convincing details like company logos, real employee names, and professional formatting. These emails trick recipients into sharing sensitive information, such as login credentials or financial details, or prompt them to download malware disguised as attachments. For example, attackers might impersonate a trusted vendor to redirect payments or a senior executive requesting immediate access to confidential data. Caller ID spoofing: Attackers manipulate phone numbers to impersonate trusted contacts, making calls appear as if they are coming from legitimate organizations or individuals. This tactic is often used to extract sensitive information, such as account credentials, or to trick victims into making payments. For instance, a scammer might pose as a bank representative calling to warn of suspicious activity on an account, coercing the recipient into sharing private information or transferring funds. IP spoofing: IP addresses are falsified to disguise the origin of malicious traffic to bypass security measures and mask malicious activity. Cybercriminals use this method to redirect traffic, conduct man-in-the-middle attacks, where a malicious actor intercepts and possibly alters the communication between two parties without their knowledge, or overwhelm systems with distributed denial-of-service (DDoS) attacks. For example, attackers might alter the source IP address of a data packet to appear as though it is coming from a trusted source, making it easier to infiltrate networks and compromise sensitive data. These tactics are often used in conjunction with other cyber threats, such as phishing or bot fraud, making detection and prevention more challenging. How behavioral analytics can combat spoofing attacks Traditional fraud prevention methods provide a strong foundation but behavioral analytics adds a powerful layer to fraud stacks. By examining user behavior patterns, behavioral analytics enhances existing tools to: Detect anomalies that signal a spoofing attack. Identify bot fraud attempts, where automated scripts mimic legitimate users. Enhance fraud prevention solutions with friction-free, real-time insights. Behavioral analytics is particularly effective when paired with device and network intelligence and machine learning (ML) solutions. These advanced tools can continuously adapt to new fraud tactics, ensuring robust protection against evolving threats. The role of artificial intelligence (AI) and ML in spoofing attack prevention AI fraud detection is revolutionizing how organizations protect themselves from spoofing attacks. By leveraging AI analytics and machine learning solutions, organizations can: Analyze vast amounts of data to identify spoofing patterns. Automate threat detection and response. Strengthen overall fraud prevention strategies. These technologies are essential for staying ahead of cybercriminals, particularly as they increasingly use AI to perpetrate attacks.   Best practices for preventing spoofing attacks Organizations can take proactive steps to minimize the risk of spoofing attacks. Key strategies include: Implementing robust authentication protocols: Use multifactor authentication (MFA) to verify the identity of users and systems. Monitoring network traffic: Deploy tools that can analyze traffic for signs of IP spoofing or other anomalies. Leveraging behavioral analytics: Adopt advanced fraud prevention solutions that include behavioral analytics to detect and mitigate threats. Educating employees: Provide training on recognizing phishing attempts and other spoofing tactics. Partnering with fraud prevention experts: Collaborate with trusted providers like Experian to access cutting-edge solutions tailored to your needs. Why proactive prevention matters The financial and reputational damage caused by spoofing attacks can be devastating. Organizations that fail to implement effective prevention measures risk: Losing customer trust. Facing regulatory penalties. Incurring significant financial losses. Businesses can stay ahead of cyber threats by prioritizing spoofing attack prevention and leveraging advanced technologies such as behavioral analytics, AI fraud detection, and machine learning, Investing in fraud prevention solutions today is essential for protecting your organization’s future. How we help organizations detect spoofing attacks Spoofing attacks are an ever-present danger in the digital age. With tactics like IP spoofing and bot fraud becoming more sophisticated, businesses must adopt advanced strategies to safeguard their operations. Our comprehensive suite of fraud prevention solutions can help businesses tackle spoofing attacks and other cyber threats. Our advanced technologies like behavioral analytics, AI fraud detection and machine learning solutions, enable organizations to: Identify and respond to spoofing attempts in real-time. Detect anomalies and patterns indicative of fraudulent behavior. Strengthen defenses against bot fraud and IP spoofing. Ensure compliance with industry regulations and standards. Click ‘learn more’ below to explore how we can help protect your organization. Learn more 1 https://www.ic3.gov/AnnualReport/Reports/2023_IC3Report.pdf This article includes content created by an AI language model and is intended to provide general information. 

In today's evolving financial landscape and with delinquencies rising, debt collection remains a critical function for financial institutions. However, traditional methods often fall short in efficiency and customer satisfaction. Enter artificial intelligence (AI), a game-changer poised to revolutionize the debt collection industry. This blog post explores the benefits and uses of AI in debt collection, shedding light on how financial institutions can leverage this technology to enhance their strategies. Understanding AI in debt collection Artificial intelligence – which encompasses machine learning, natural language processing, and other advanced technologies – is transforming various industries, including debt collection. AI in debt collection involves using these technologies to automate and optimize processes, making them more efficient and effective. Examples of AI technologies in debt collection include chatbots, predictive analytics, and automated communication systems. Uses Predictive analytics Predictive debt collection analytics is a powerful tool in AI collections. By analyzing patterns and trends in debtor behavior, AI can forecast the likelihood of repayment. This information allows financial institutions to tailor their collection strategies to individual debtors, improving the chances of successful recovery. Chatbots and virtual assistants AI-powered chatbots and virtual assistants handle routine customer interactions, providing instant responses to common queries. These tools can escalate complex issues to human agents when necessary, ensuring that customers receive the appropriate level of support. By automating routine tasks, chatbots free up human agents to focus on more complex cases. Automated communication AI can automate communication with debtors, sending payment reminders and notifications through various channels such as email, SMS, and phone calls. These messages can be customized based on debtor profiles, ensuring that communication is personalized and effective. Automated communication helps maintain consistent contact with debtors, increasing the likelihood of timely payments. Benefits Improved operational efficiency One of the most significant advantages of AI in debt collection is improved operational efficiency. AI can automate repetitive tasks such as sending payment reminders and processing payments, reducing the need for manual intervention. This automation speeds up the process, reduces costs, and minimizes human errors, ensuring more accurate and timely collections. Enhanced customer experience AI-driven chatbots and virtual assistants can provide personalized communication, enhancing the customer experience. These AI tools are available 24/7, allowing customers to get instant responses to their queries at any time. By offering a seamless and responsive service, financial institutions can improve customer satisfaction and engagement strategies. Better decision making AI collections leverage predictive analytics to assess debtor risk and provide data-driven insights. This information enables financial institutions to develop more effective collection strategies and prioritize high-risk accounts. By making informed decisions based on predictive models, institutions can optimize collections processes and increase their chances of successful debt recovery. Cost savings Automation through AI can lead to significant cost savings. Financial institutions can achieve higher profitability by reducing the need for human intervention and lowering operational costs. Additionally, increased recovery rates due to better cure strategies contribute to overall cost efficiency. Challenges and considerations While AI offers numerous benefits, there are challenges and considerations to keep in mind. Data privacy and security are paramount, as financial institutions must ensure compliance with regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Balancing automation with the need for a human touch is also crucial, as some customers may prefer interacting with human agents. Additionally, addressing potential biases in AI algorithms is essential to ensure fair and equitable treatment of all debtors. Future Trends in AI and debt collection The future of AI in debt collection looks promising, with emerging technologies poised to make a significant impact. Integration of AI with other technologies such as blockchain and the Internet of Things (IoT) could further enhance the efficiency and security of debt collection processes. As AI continues to evolve, financial institutions must stay abreast of these trends to remain competitive and effective in their collection strategies. Our debt management and collection solutions With more than 25 years of experience and a comprehensive suite of collection products, our enhanced decisioning, improved processes, and account prioritization can enable your organization to move toward a customer-centric approach that helps reduce losses and control costs. AI in debt collection offers a myriad of benefits, from improved efficiency and enhanced customer experience to better decision-making and cost savings. By leveraging AI technologies such as predictive analytics, chatbots, and automated communication, financial institutions can optimize their debt collection strategies and achieve higher recovery rates. As the industry continues to evolve, embracing AI will be crucial for financial institutions looking to stay ahead of the curve. Click below to learn more about how we can help your organization optimize your debt collection strategies to lose less and recover more. Learn more Watch our webinar on-demand This article includes content created by an AI language model and is intended to provide general information.

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

Property managers and landlords nationwide aim to attract reliable, long-term tenants. Gaining insight into modern renters—their tenant data, financial situations, lifestyle choices, and key priorities—gives proactive property owners and managers a competitive edge in appealing to the ideal tenant. While certain elements of the rental landscape are not within the control of market professionals, knowledge is power, and understanding the preferences, spending habits, and profiles of today’s renters can inform their business approach and success. To understand today’s renter, Experian® took a deep dive into the tenant data of the rental market landscape in its 2024 report on the U.S. rental market. Among the principal findings, Generation Z and younger millennials' dominance in this sector is rising. Today’s Renter Profile Experian research reveals movements in the demographics of the average U.S. renter, now dominated by younger individuals and lower average-income consumers. These renters face challenges as they navigate the rising costs of securing housing. The 2024 rental report delves into these changes, highlighting age and income level shifts in tenant data. Critical to property managers and landlords, this information offers an understanding of their customer base and provides insight into the rental market landscape. Gen Z on the Rise: Gen Z alone accounts for 30.5% of all renters, and their numbers are increasing, up 3.5% over a year. Gen Z and younger millennials (adults under 35) represent over 50% of the rental population. Income Declines: From January 2023 to January 2024, the tenant data showed the average income of RentBureau® renters fell from $53,100 to $52,600[1]. Higher Rent Costs: In 2024, over 50% of renters paid $1,500+ per month, with the average U.S. renter's monthly payment of $1,713. Gen Z,the youngest renter population, spends an average of $1,600 monthly on rent. This context plays an important role in examining the state of the 2024 rental market. Propensity to Move In addition to age and economic well-being, landlords should take a keen interest in tenant data related to renters’ moving habits, as these provide valuable insights into behavior and market trends. Landlords generally prefer longer-term leaseholders, and renters who stay longer provide more stability to property management efforts. Not surprisingly, generational trends appear here as well. While over 90% of all renters retained one lease over a 2-year period, tenant data indicates that Gen Z and younger millennial renters tend to move more than other age groups. This tendency stems from various factors, including a willingness to relocate to more affordable regions or areas that better suit their lifestyle preferences. With today’s evolving work environment, remote work has opened new possibilities. Again, the overarching trend is that renters stay in one place for two years. In fact, this represents 92.5% of all renters. Signs of Overall Renter Financial Health Housing is a significant monthly cost of living expense, especially for many younger adults just starting out and lower-income individuals and families. The percentage of a renter’s monthly income allocated to rental costs clearly indicates housing affordability. This tenant data reflects that higher rent-to-income ratios (RTIs) signify that renters have less financial flexibility, as a larger portion of their monthly income is allocated to rent, leaving less available for essentials, savings, and discretionary spending. On average, renters spend over 44% of their monthly income on rent, and low-to-moderate-income renters dedicate over 50% to rent. General guidelines suggest that the percentage should be no more than 30%. Higher rental costs and declining annual incomes disproportionately impact those with fewer financial means. Credit and Other Signs Landlords and property managers value tenant data, such as renter applicants' stability. Indicators such as overall credit quality and negative payment history provide valuable insights into economic well-being. While negative payment history has improved slightly, the market shows a rise in delinquencies. Experian’s research highlights that while credit scores for the general U.S. population are on the rise, the trends for renters tell a slightly different story. Between May 2023 and May 2024, tenant data revealed a 2% increase in renters fell into the near-prime and subprime credit categories. Although the implications for the future remain uncertain, this data, combined with other analytics, may offer clues about market trends and opportunities. The Future The demand for rentals remains high, particularly among young adults and lower-income households. As the economy and market forces fluctuate, so do the financial pressures on renters and rental housing availability and costs. The role of young adults and lower-income households in the rental market will continue. Landlords and property managers must tune in to demographic realities in their efforts to develop risk management and success strategies. To learn more about the state of the U.S. rental market, download Experian’s 2024 rental report. [1] RentBureau income is based on modeled income, which is estimated using credit data and other predictive factors.

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