Tag: fraud detection

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

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 this article...

With more consumers online, bad actors are taking the opportunity to commit more financial crimes, such as account takeover fraud. This online scheme resulted in nearly $13 billion in losses in 2023, up from $11 billion in 2022.1 So, what do organizations need to know about this form of identity theft? And how can they prevent it? Let’s explore one type of account takeover fraud: email account takeover. What is email account takeover? Email account takeover occurs when a fraudster gains access to a legitimate user’s email account through data breaches that expose credentials, purchasing from the dark web, or phishing scams. It's usually one of the first steps in a broader account takeover scheme. Once fraudsters have access to a consumer’s email or social media account, they have access to the private information in that consumer’s inbox: financial statements, health records, and other forms of PII. Fraudsters can also now use the consumer’s email to impersonate them with friends, family, financial institutions or other businesses they interact with.   They can also gain access to other accounts and here’s where email account takeover becomes more dangerous. In this attack, the fraudster gains access to an email or mobile account. Once they have an email, they start by trying to guess the user’s password, commonly called a brute force attack, or through password spraying, where they use commonly used passwords, i.e. ‘password’ or ‘123123.  A recent Google survey found that 65% of people use the same password for some or all of their online accounts. This, along with a corresponding email address can give fraudsters further entre into a consumer’s other accounts. If unsuccessful, they’ll then execute a ‘forgot password’, password reset, or one-time password. Then, they take over the victim’s account with their financial institution to facilitate the transfer of funds from the compromised account. 57% of businesses are experiencing rising fraud losses associated with account opening and account takeover.2 While email account takeover can be quickly executed, detecting it can take time. Unlike credit card fraud, where an individual may soon notice suspicious activity, an email account takeover can go undetected for longer. The owner may not realize until later that their account has been compromised, especially with a dormant account or secondary account they use less. As a result, criminals have more time to facilitate additional attacks. LEARN MORE: Explore 2024 fraud trends listed by Experian. How does it affect your organization? Account takeover fraud doesn't just impact consumers, it can result in significant financial losses for organizations. For example, if your organization offers credit products, you might have to cover the costs of disputing chargebacks, card processing fees, or providing refunds. In the case of a data breach, you may have to pay fines against your organization for not properly protecting consumer information. Nearly two-thirds of consumers say they’re very or somewhat concerned with online security.3 But email account takeover isn't just costly — it can damage your organization's reputation. Consumers expect organizations to have proper security measures in place to protect their information. If a data breach occurs, your security can seem weak, leading consumers to lose trust in your organization. As a result, they may potentially take their business elsewhere. The importance of prevention While consumers listed identity theft as their top concern when conducting activities online, they’re still interacting, opening new accounts, and transacting digitally.4 Coupled with the rise of account takeover fraud and associated losses, it’s more crucial than ever for organizations to accurately detect and prevent these attacks. To do this, they must have a proactive fraud prevention strategy in place. Account takeover fraud prevention requires your business to maintain and continuously reaffirm confidence in the identity data you collect. Your team can monitor, segment, and proactively act on customer identities that display a higher risk of fraud than was determined at account origination through risk-based fraud detection models, machine learning, and advanced analytics. Experian offers many flexible solutions, including: CrossCore® Solutions are best practice-based groupings of fraud and identity products that enable organizations to solve common to complex issues. For example, our fraud risk solutions include email and phone intelligence to improve verification for thin-files and other challenging populations. Experian offers phone/carrier-based matching capabilities with address validity and occupancy data for >95% of U.S. households. FraudNet is a device intelligence solution that analyzes hundreds of device attributes and prevents fraud on all digital channels. Combining contextual data, behavioral data, and device data, it bridges the gap between physical and digital identity to achieve fraud capture rates that exceed industry averages. To further alleviate account takeover fraud, your organization can offer educational resources for fraud prevention. Using various, strong passwords across their accounts, and changing them regularly, is a foundational way consumers can help ensure their accounts are secure. Leveraging user names that are different from your email can also help. If a fraudster is able to takeover an account and initiate a lost password request, and that password is used for other accounts, that fraudster now has the credentials they need to further defraud that consumer. By spreading awareness about identity fraud risks and providing best practices for prevention, you can better protect your organization and consumers. LEARN MORE: Building a multilayered fraud and identity strategy with CrossCore Solutions Partnering with Experian Email account takeover, along with other types of fraud, can be detected and prevented with the right partner. Experian’s fraud management solutions can help your organization accurately verify customers and assess risk with our account takeover and fraud management solutions. Explore Experian’s account takeover solutions and watch an on-demand recording of our Fraud Risk and Identity Verification Solutions tech showcase. Learn more Watch tech showcase 1 Identity Fraud Cost Americans $43 Billion in 2023, AARP. 2-4 2023 U.S. Identity and Fraud Report, Experian.

Ensuring the reliability of tenant applications is paramount to running a successful property management business. But with an exponential rise in prospective residents using fake financial documents to inflate income and employment status, how do property managers navigate and detect fake paystubs without stepping on a landmine of liability? The marketplace of deception Paystub generator websites As you embrace the commitment to diligence, be aware that some legitimate websites can be unknowingly used by fraudsters to create counterfeit financial documents. Knowledge is your ally here. At the touch of a button, even the minimally tech inclined can produce pay stubs that appear convincing. There are dozens of sites that offer paystub generator software, including: Design and editing software websites that are accessible to people beyond just creative professionals. Popular e-commerce platform stores that host apps capable of creating paystubs. Mobile app stores that allow users to download apps for use on all major mobile devices. Key indicators of a fake paystub Remember, as a property manager or owner, you are responsible for scrutinizing these documents to protect your business interests. Use your awareness to be vigilant, verifying every piece of information to ensure the credibility of prospective tenants. While some of these falsified paystubs may appear to be legitimate, they are usually not perfect. Here are some quick checks which may help you spot a fake or trigger a deeper review quickly. Watch out for elusive typos Erroneous spelling, particularly in company names and financial terms, is a big red flag. Keep your eyes peeled for these unruly characters. Distorted watermarks A legitimate paystub should carry official watermarks or specific symbols that indicate its authenticity. However, be on the lookout for watermarks that seem off — sometimes, they're too conspicuous or amateurish, which can be a tell-tale sign of forgery. Authentic watermarks should be subtle and consistent with the company's brand. Crunching the numbers Inaccurate calculations can unravel a fake paystub. If the numbers just don't add up or pay dates vary inexplicably, you should investigate further. Inconsistent font Professional payroll systems stick to a consistent font. If you notice various font styles and sizes, it's worth investigating further. Authenticity lies in uniformity. Going logo-less? A missing company logo, or one that looks like it was copied from a low-resolution image on the internet, should trigger suspicion. Unusual tax deductions Abnormal tax deductions could indicate someone's fiddling with the figures. Brush up on your tax knowledge or consult with an expert if something seems off-the-wall. Final food for thought Remember, having the right knowledge and tools empowers you to make informed decisions, safeguarding your property from potential fraudsters. Be diligent, stay informed, and leverage technology to support your processes. Action steps to take today Educate your team: Make sure everyone involved in the application review process knows what to look for. Develop a standard operating procedure: Update your existing (or develop) Standard Operating Procedures: As new ways of gaming the system arise, make sure your particular procedures are keeping up with the times. For example, include steps for the following: Understand tenant screening laws in your area. Create consistent resident screening criteria. Check credit report and background. Verify employment and income. Review rental history and evictions (if any). Check criminal record with multi-state search. Interview residents before signing a lease. Follow a consistent policy when accepting or rejecting applicants. Embrace technology: Income and employment verification solutions can verify income directly from a trusted data source and avoid the paystub predicament altogether. Consider implementing a verification system that leaves no room for guesswork. Our verification solution, Experian VerifyTM, provides accurate, efficient, and compliant income and employment verification services. With Experian Verify, property managers can navigate the complexities of tenant-related income and employment verification with ease, ensuring they are adhering to Fair Housing laws and detecting fraudulent behavior. To learn more about how Experian Verify can benefit your property business, please contact us and visit us online. Learn more

In the ever-expanding financial crime landscape, envision the most recent perpetrator targeting your organization. Did you catch them? Could you recover the stolen funds? Now, picture that same individual attempting to replicate their scheme at another establishment, only to be thwarted by an advanced system flagging their activity. The reason? Both companies are part of an anti-fraud data consortium, safeguarding financial institutions (FIs) from recurring fraud. In the relentless battle against fraud and financial crime, FIs find themselves at a significant disadvantage due to stringent regulations governing their operations. Criminals, however, operate without boundaries, collaborating across jurisdictions and international borders. Recognizing the need to level the playing field, FIs are increasingly turning to collaborative solutions, such as participation in fraud consortiums, to enhance their anti-fraud and Anti-Money Laundering (AML) efforts. Understanding consortium data for fraud prevention A fraud consortium is a strategic alliance of financial institutions and service providers united in the common goal of comprehensively understanding and combatting fraud. As online transactions surge, so does the risk of fraudulent activities. However, according to Experian’s 2023 U.S. Identity and Fraud Report, 55% of U.S. consumers reported setting up a new account in the last six months despite concerns around fraud and online security. The highest account openings were reported for streaming services (43%), social media sites and applications (40%), and payment system providers (39%). Organizations grappling with fraud turn to consortium data as a robust defense mechanism against evolving fraud strategies. Consortium data for fraud prevention involves sharing transaction data and information among a coalition of similar businesses. This collaborative approach empowers companies with enhanced data analytics and insights, bolstering their ability to combat fraudulent activities effectively. The logic is simple: the more transaction data available for analysis by artificial-intelligence-powered systems, the more adept they become at detecting and preventing fraud by identifying patterns and anomalies. Advantages of data consortiums for fraud and AML teams Participation in an anti-fraud data consortium provides numerous advantages for a financial institution's risk management team. Key benefits include: Case management resolution: Members can exchange detailed case studies, sharing insights on how they responded to specific suspicious activities and financial crime incidents. This collaborative approach facilitates the development of best practices for incident handling. Perpetrator IDs: Identifying repeat offenders becomes more efficient as consortium members share data on suspicious activities. Recognizing patterns in names, addresses, device fingerprints, and other identifiers enables proactive prevention of financial crimes. Fraud trends: Consortium members can collectively analyze and share data on the frequency of various fraud attempts, allowing for the calibration of anti-fraud systems to effectively combat prevalent types of fraud. Regulatory changes: Staying ahead of evolving financial regulations is critical. Consortiums enable FIs to promptly share updates on regulatory changes, ensuring quick modifications to anti-fraud/AML systems for ongoing compliance. Who should join a fraud consortium? A fraud consortium can benefit any organization that faces fraud risks and challenges, especially in the financial industry. However, some organizations may benefit more, depending on their size, type, and fraud exposure. Some of the organizations that should consider joining a fraud consortium are: Financial institutions: Banks, credit unions, and other financial institutions are prime targets for fraudsters, who use various methods such as identity theft, account takeover, card fraud, wire fraud, and loan fraud to steal money and information from them. Fintech companies: Fintech companies are innovative and disruptive players in the financial industry, who offer new and alternative products and services such as digital payments, peer-to-peer lending, crowdfunding, and robot-advisors. Online merchants: Online merchants are vulnerable to fraudsters, who use various methods such as card-not-present fraud, friendly fraud, and chargeback fraud to exploit their online transactions and payment systems. Why partner with Experian? What companies need is a consortium that allows FIs to collaboratively research anti-fraud and AML information, eliminating the need for redundant individual efforts. This approach promotes tighter standardization of anti-crime procedures, expedited deployment of effective anti-fraud/AML solutions, and a proactive focus on preventing financial crime rather than reacting to its aftermath. Experian Hunter is a sophisticated global application fraud and risk management solution. It leverages detection rules to screen incoming application data for identifying and preventing fraudulent activities. It matches incoming application data against multiple internal and external data sources, shared fraud databases and dedicated watch lists. It uses client-flexible matching rules to crossmatch data sources for highlighting data anomalies and velocity attempts. In addition, it looks for connections to previous suspected and known fraudulent applications. Hunter generates a fraud score to indicate a fraud risk level used to prioritize referrals. Suspicious applications are moved into the case management tool for further investigation. Overall, Hunter prevents application fraud by highlighting suspicious applications, allowing you to investigate and prevent fraud without inconveniencing genuine customers. To learn more about our fraud management solutions, visit us online or request a call. Learn more This article includes content created by an AI language model and is intended to provide general information.

This article was updated on March 4, 2024. If you steal an identity to commit fraud, your success is determined by how long it takes the victim to find out. That window gets shorter as businesses get better at knowing when and how to reach an identity owner when fraud is suspected. In response, frustrated fraudsters have been developing techniques to commit fraud that does not involve a real identity, giving them a longer run-time and a bigger payday.  That's the idea behind  synthetic identity (SID) fraud — one of the fastest-growing types of fraud.  Defining synthetic identity fraud Organizations tend to have different  definitions of synthetic identity fraud, as a synthetic identity will look different to the businesses it attacks. Some may see a new account that goes bad immediately, while others might see a longer tenured account fall delinquent and default. The qualifications of the synthetic identity also change over time, as the fraudster works to increase the identity’s appearance of legitimacy. In the end, there is no person to confirm that fraud has occurred, in the very best case, identifying a synthetic identity is inferred and verified. As a result, inconsistent reporting and categorization can make tracking and fighting SID fraud more difficult.  To help create a more unified understanding and response to the issue, the Federal Reserve and 12 fraud experts worked together to develop a definition. In 2021, the  Boston Federal Reserve  published the result, “Synthetic identity fraud is the use of a combination of personally identifiable information to fabricate a person or entity to commit a dishonest act for personal or financial gain."1 To break down the definition, personally identifiable information (PII) can include:  Primary PII:  Such as a name, date of birth (DOB), Social Security number (SSN) or another government-issued identifier. When combined, these are generally unique to a person or entity. Secondary PII:  Such as an address, email, phone number or device ID. These elements can help verify a person or entity's identity.   Synthetic identities are created when fraudsters establish an identity from scratch using fake PII. Or they may combine real and fake PII (I.e., a stolen SSN with a fake name and DOB) to create a new identity. Additionally, fraudsters might steal and use someone's SSN to create an identity - children, the  elderly  and incarcerated people are popular targets because they don't commonly use credit.4 But any losses would still be tied to the SID rather than the victim. Exploring the Impact of SID fraud The most immediate and obvious impact of SID fraud is the fraud losses. Criminals may create a synthetic identity and spend months  building up its credit profile, opening accounts and increasing credit limits. The identities and behaviors are constructed to look like legitimate borrowers, with some having a record of on-time payments. But once the fraudster decides to monetize the identity, they can apply for loans and max out credit cards before ‘busting out’ and disappearing with the money.  Aite-Novaric Group estimates that SID fraud losses totaled $1.8 billion in 2020 and will increase to $2.94 billion in 2024.2 However, organizations that do not identify SIDs may classify a default as a credit loss rather than a fraud loss.  By some estimates, synthetic identity fraud could account for up to 20 percent of loan and credit card charge-offs, meaning the annual charge-off losses in the U.S. could be closer to $11 billion.3 Additionally, organizations lose time and resources on collection efforts if they do not identify the SID fraud.  Those estimates are only for unsecured U.S. credit products. But fraudsters use synthetic identities to take out secured loans, including auto loans.   As part of schemes used to steal relief funds during the pandemic, criminals used synthetic identities to open demand deposit accounts to receive funds. These accounts can be used to launder money from other sources and commit peer-to-peer payment fraud. Deposit account holders are also a primary source of cross-marketing for some financial institutions. Criminals can take advantage of vulnerable onboarding processes for deposit accounts where there’s low risk to the institution and receive offers for lending products. Building a successful SID prevention strategy Having an effective SID prevention strategy is more crucial than ever for organizations. Aside from fraud losses, consumers listed identity theft as their top concern when conducting activities online. And while 92% of businesses have an identity verification strategy in place, 63% of consumers are "somewhat confident" or "not very confident" in businesses' ability to accurately identify them online. Read: Experian's 2023 Identity and Fraud Report Many traditional fraud models and identity verification methods are not designed to detect fake people. And even a step up to a phone call for verification isn't enough when the fraudster will be the one answering the phone. Criminals also quickly respond when organizations update their fraud detection methods by looking for less-protected targets. Fraudsters have even signed their SIDs up for social media accounts and apps with low verification hurdles to help their SIDs pass identity checks.5  Understand synthetic identity risks across the lifecycle  Synthetic Identities are dynamic. When lending criteria is tightened to synthetics from opening new accounts, they simply come back when they can qualify. If waiting brings a higher credit line, they’ll wait. It’s important to recognize that synthetic identity isn’t a new account or a portfolio management problem - it’s both.    Use analytics that are tailored to synthetic identity  Many of our customers in the financial services space have been trying to solve synthetic identity fraud with credit data. There’s a false sense of security when criteria is tightened and losses go down—but the losses that are being impacted tend to not be related to credit. A better approach to synthetic ID fraud leverages a larger pool of data to assess behaviors and data linkages that are not contained in traditional credit data.  You can then escalate suspicious accounts to require additional reviews, such as screening through the Social Security Administration's Electronic Consent Based SSN Verification (eCBSV) system or more stringent document verification.  Find a trusted partner  Experian's interconnected data and analytics platforms offer lenders turnkey identity and synthetic identity fraud solutions. In addition, lenders can take advantage of the risk management system and continuous monitoring to look for signs of SIDs and fraudulent activity, which is important for flagging accounts after opening. These tools can also help lenders identify and prevent other common forms of fraud, including account takeovers, e-commerce fraud, child identity theft fraud and elderly fraud. Learn more about our synthetic identity fraud solutions. Learn more 1Federal Reserve Bank (2021). Defining Synthetic Identity Fraud 2Aite Novarica (2022). Synthetic Identity Fraud: Solution Providers Shining Light into the Darkness 3Experian (2022). Preventing synthetic identity fraud 4The Federal Reserve (2022). Synthetic Identity Fraud: What Is it and Why You Should Care? 5Experian (2022). Preventing synthetic identity fraud 

It's 2024, and it has never been easier to buy a car in person or online, but automobiles are not quite as affordable as prior to the pandemic. While everyone is looking for the best car deal, some folks are pushing it too far and are falling for auto scams. What is auto lending fraud? Fraud perpetrators are drawn to sectors they perceive as highly lucrative. The accessibility of online vehicle financing and purchasing, coupled with the substantial financial magnitude associated with automotive transactions, renders the auto industry an optimal avenue for cash-out endeavors. Auto lending fraud refers to deceptive or fraudulent activities related to obtaining or processing auto finance. This can involve various schemes aimed at misleading lenders, financial institutions, or individuals involved in the lending process. Criminal networks now operate on social media sites like Facebook and Telegram, offering a unique car buying service using synthetic identities. They create synthetic identities, finance cars with no down payment, and deliver vehicles to addresses chosen by buyers. The process involves selecting a car online, sending a small amount of dollars and a photo against a white background, and receiving a fake driver's license. Those networks claim to exploit car sites' policies successfully. While appealing to those in urgent need of a car, the service poses significant risks as the synthetic identity may be used for other fraudulent activities beyond car purchase. Who is at risk? Everyone involved in the car buying process is at risk of falling victim to auto loan fraud. Car buyers looking to secure financing, as well as lenders, need to be aware of the potential red flags and take necessary precautions to safeguard their interests. Thieves leverage the internet and electronic transactions to perpetrate auto loan fraud. While the growth of online commerce has improved many aspects of trade, it has also made personally identifiable information and financial details vulnerable to data breaches. Unscrupulous individuals can gain unauthorized access to such information, providing the foundation for various identity theft schemes. The internet also facilitates the creation of seemingly legitimate documents that support auto loan fraud. Online services exist to help fraudsters fabricate income statements and fake employment verification from fictitious companies. This trend has made auto loan fraud an increasingly popular method for acquiring vehicles with minimal cash and risk. Another auto loan fraud trend is the increased use of CPN (Credit Privacy Number). Credit Repair firms introduced a novel strategy targeting consumers — the CPN (Credit Privacy Number). Marketed as a nine-digit alternative to a Social Security Number (SSN), CPNs are purportedly usable for obtaining credit. However, it is crucial to note that utilizing a CPN for credit applications constitutes a criminal offense, potentially leading to legal consequences, and car dealerships should not accept them.  Detecting auto loan fraud There are several types of auto loan fraud worth noting to better understand the landscape: Income fabrication: Prospective buyers may falsify their income details to qualify for a larger loan or better terms. Lenders should verify income using documents like pay stubs, tax returns, or bank statements and watch out for inconsistencies. Employment misrepresentation: Applicants could lie about their job titles or employment status. Lenders should verify employment details through HR departments or by directly contacting the employer. Trade-in vehicle deception: Some individuals may overstate the value of their trade-in vehicle to secure a higher loan amount. Lenders should perform thorough appraisals or consult trusted sources to ascertain the accurate value of the trade-in. Identity fraud: Fraudsters can assume someone else's identity, commit first party fraud or create a fictitious persona to obtain an auto loan. Lenders must verify the applicant's identity using reliable identification documents and consider using identity verification tools. Forged documentation: Fraudsters may forge or alter documents like income statements, bank statements, or driver's licenses. Lenders should scrutinize documents carefully for discrepancies or signs of tampering. Straw borrower fraud: In this scenario, someone with poor credit convinces a friend or relative with better credit to front the deal, posing as the buyer. A better credit score allows for better terms or a more valuable vehicle. The actual buyer may continue to make payments to the friend, or the loan may become delinquent, negatively affecting the friend's credit score. In extreme cases, the straw buyer is part of a fraud ring, and the vehicle has already been sold in a foreign market. Synthetic identity fraud: Data breaches providing personally identifying information enable identity theft schemes. Perpetrators use illicitly acquired information to create false borrower profiles that appear authentic. These profiles typically have excellent credit, a social security number, an affluent home address, stable employment, and other attributes that make them seem like desirable borrowers. However, a detailed investigation reveals subtle inconsistencies indicative of high risk. How to prevent auto loan fraud To combat auto loan fraud and protect profitability, auto lenders can leverage technological advancements. By applying analytics and machine learning to millions of loan applications and histories, you can identify fraudulent patterns and inconsistencies. Machine learning can determine the type of suspected fraud and provide a confidence factor to guide further investigation and verification. Additionally, you should: Conduct thorough background checks on prospective buyers and verify their personal information and documents. and verify their personal information and documents. Implement a comprehensive loan underwriting process that includes income verification, employment verification, and collateral evaluation. Educate employees about common fraud schemes, warning signs, and best practices to ensure they remain vigilant during loan applications. Foster a culture of cooperation with local law enforcement agencies, sharing information about suspected fraudsters to help prevent future incidents. It is important for individuals and businesses to be vigilant and report any suspicious activity. Car dealerships and financial institutions work to prevent fraud through proper identification verification, credit checks, and adherence to legal and ethical standards. If you suspect fraudulent activity or identity theft, it is crucial to report it to the appropriate authorities immediately. Gearing-up Taking advantage of the latest fintech capabilities, such as cloud-based loan origination that integrates analytics, machine learning, and automated verification services, can significantly reduce the likelihood of fraudulent applications becoming another auto lending fraud statistic. By combining the best data with our automated ID verification checks, Experian helps you safeguard your business and onboard customers efficiently. Our best-in-class solutions employ device recognition, behavioral biometrics, machine learning, and global fraud databases to spot and block suspicious activity before it becomes a problem. Learn more about our automotive fraud prevention solutions *This article includes content created by an AI language model and is intended to provide general information.

Financial institutions, merchants, and e-commerce platforms are no strangers to fraud, especially in the realm of payments. With the rise of digital currency, fraudsters are becoming more inventive, making it increasingly difficult to detect and prevent payment fraud. In this blog post, we discuss payment fraud and ways to protect your organization and your customers. What is payment fraud? Payment fraud occurs when someone uses false or stolen payment information to make a purchase or transaction. The most common types of payment fraud include: Phishing: Through emails or text messages, scammers disguise themselves as trustworthy sources to lure recipients into sharing their personal information, such as account passwords and credit card numbers. Card not present fraud: This type of fraud is one of the most challenging forms of payment fraud to detect and prevent. It occurs when a criminal uses a stolen or compromised credit card to make a purchase online, in-person, or by other means where the card is not physically present at the time of the transaction. Account takeover fraud: This type of fraud occurs when fraudsters gain unauthorized access to an individual’s account and carry out fraudulent transactions. They take over accounts by gathering and using personal or financial details to impersonate their victims. The rise of online payment fraud Online payments have become a prime destination for fraudsters as more consumers choose to store card details and make purchases digitally. As a result, consumers believe that it’s the responsibility of businesses to protect them online. If there’s a lack of trust and safety, consumers will have no problem switching providers, leading to declines in customer loyalty and monetary losses for organizations. No matter the type of payment fraud, it can result in devastating consequences for your organization and your customers. According to Experian’s 2024 U.S. Identity and Fraud Report, fraud scams and bank fraud schemes resulted in more than $458 billion in losses globally. On the consumer side, 52 million Americans had fraudulent charges on their credit or debit cards, with unauthorized purchases exceeding $5 billion. Given these findings, it’s more important than ever to implement robust online payment fraud detection and prevention measures. How can payment fraud be detected and prevented? Approaches to payment fraud detection and prevention have evolved over time. Some of the current and emerging trends include: Additional layers of security: Security measures like two-factor authentication, a CVV code, and a billing zip code can help verify a customer’s identity and make it more difficult for fraudsters to complete a transaction. Enhanced identity verification: A credit card owner verification solution, like Experian LinkTM, matches the customer identity with the credit card being presented for payment, allowing businesses to make better decisions, reduce false declines, and protect legitimate customers. Artificial intelligence (AI) and machine learning: AI-powered models and machine learning algorithms can identify patterns consistent with fraudulent activity in real time, resulting in proactive fraud prevention and reduced financial losses. Behavioral analytics: Using behavioral analytics to monitor user behavior, such as how they navigate a website or interact with the payment process, can help identify inconsistencies and potential fraud. Token-based authentication: Tokenization protects card information by replacing sensitive data with a unique identifier (token), which makes data breaches less damaging. How Experian can help As the payments landscape continues to evolve, so do fraudsters. Experian offers a wide range of payment fraud analytics, account takeover fraud prevention and fraud management solutions that allow you to better detect and prevent payment fraud. Your organization’s reputation and your customers’ trust shouldn’t be compromised. To learn more, visit us today. Learn more This article includes content created by an AI language model and is intended to provide general information.

While today’s consumers expect a smooth, frictionless digital experience, many financial institutions still rely on outdated technology and manual reviews to acquire new customers. These old processes can prevent lenders from making accurate and timely credit decisions, leading to lost opportunities, revenue, and goodwill. By optimizing their customer acquisition strategies, financial institutions can allocate their resources effectively and say yes to consumers faster. This guide will walk you through the current challenges facing customer acquisition and how robust optimization strategies can help. Current challenges in customer acquisition To stay competitive and engage high-value customers, you’ll need an efficient customer acquisition process that weeds out both fraudulent actors and risky consumers. However, achieving this balancing act comes with a unique set of challenges. Because today’s consumers can access goods and services almost anywhere online at any time, more than 54 percent of customers expect a heightened digital and frictionless experience. Failing to meet this expectation can lead to huge losses for lenders. Some of the most common challenges in customer acquisition include: Although 52 percent of consumers prefer digital banking options over visiting branches in person, many lenders still rely on paper documents, which can add weeks to the onboarding process. Requiring consumers to provide substantial information about themselves during an application process can lead to abandoned applications. 67 percent of consumers will leave an application if they experience complications. Verifying consumer identities is growing increasingly important. In fact, about 35 percent of customers drop out of digital onboarding because their identity can't be confirmed. Poorly defined campaign planning can cause businesses to market to the wrong population segments, resulting in wasted time and resources. What is optimization for customer acquisition?  Customer acquisition optimization is the process of implementing new methods and solutions to make acquiring new customers more efficient and cost-effective. For lenders, this means streamlining steps in the credit decisioning process to focus on the right prospects and reduce friction. What types of processes can be optimized for customer acquisition?  You might be surprised just how many processes can be optimized for customer acquisition. Here are just a few examples: Having a holistic view of consumers allows you to take the guesswork out of targeting so you can better identify and engage high-potential customers. Utilizing predictive and lifestyle data enables you to pinpoint a more precisely segmented audience for marketing. Digital application solutions that reach across multiple channels, allowing applicants to leave one channel and pick up right where they left off in another. Real-time identity verification and fraud detection during onboarding and after, helping expedite approvals and mitigate risks. Utilizing API integration to leverage multiple metrics beyond credit scores when screening applicants' financial situation. Building custom risk models that pair to your existing data so you can say yes to more customers and better manage portfolio risk. Benefits of customer acquisition optimization Optimization can bring numerous benefits to your business, providing a faster return on investment. Here are some examples. By better pinpointing your marketing through predictive and lifestyle data, you can achieve increased conversions. Faster onboarding with less friction helps retain more customers. Real-time fraud detection and identity verification reduce customer roadblocks, allowing you to realize significant growth. Custom risk models and decisioning platforms can pair your data with additional data elements, providing more than just a credit score rating for your applicants. This can help you say yes to more customers. Using AI and machine learning tools will reduce the need for manual reviews and thus increase booking rates and applications. A real-life example of these benefits can be found with the Michigan State University Federal Credit Union (MSUFCU.) With over $7.2 billion in assets and 330,000 members, the client was manually reviewing all its applications. Experian reviewed the client's risk levels and approvals, comparing their risk and bankruptcy scores to determine which were most predictive. This analysis led Experian to recommend a new decisioning platform (PowerCurve Originations®) for instant credit decisions, an alternative data score tool, and Experian Advisory Services for risk-based pricing. After implementing these optimization solutions, MSUFCU saw a 55 percent increase in average monthly automations, four times improved online application response time and began competing more effectively in the marketplace. How Experian can help Experian offers a number of customer acquisition tools, allowing companies to be more responsive in an increasingly competitive market, while still reducing fraud risk. These tools include: Acquisition optimization marketing Experian offers a web-based platform that lets clients manage their marketing efforts all in the same place. You can upload and enhance client files, identify lookalike prospects, and use firmographic and credit data to get a holistic view of your clients and your prospects. Data-driven acquisition and decisioning engine PowerCurve Originations® is a data-driven decisioning engine that accepts applications from multiple channels, automates data collection and verification and proactively monitors decision results. It's flexible enough to reach across multiple channels, letting customers set aside their application in one digital channel and resume where they left off in another. It also provides businesses with access to comprehensive data assets, proactive monitoring and streamlined development with minimal coding. Enhanced fraud detection and identity verification Experian's Precise ID® is a risk-based fraud detection and prevention platform that provides analytics to accurately verify customers and mitigate fraud loss behind the scenes, ensuring a smoother onboarding process. Robust consumer attributes for better customized models Experian gives clients access to a wider berth of consumer attributes, helping you better screen applicants beyond just looking at credit scores. Trended 3DTM attributes let you uncover unique patterns in consumers' behavior over time, allowing you to manage portfolio risk, build better models and determine the next best actions. Premier AttributesSM aggregates credit data at the most granular and meaningful levels to provide clear insights into consumer credit behavior. It encompasses more than 2,100 attributes across 51 industries to help you develop highly predictive custom models. Enterprise-wide credit decisioning engine Experian's enterprise-wide credit decision platform lets you combine machine learning with proprietary data to return optimized decisions and quickly respond to requests. Robust credit decisioning software lets you convert data into meaningful actions and strategies. With Experian's machine learning decisioning options, companies are realizing a 25 percent reduction in manual reviews, a 25 percent increase in loan and credit applications and a 26 percent increase in booking rates. Highly predictive custom models Experian's Ascend Intelligence ServicesTM can help you create highly predictive custom models that create sophisticated decisioning strategies, allowing you to accurately predict risk and make the best decisions fast. This end-to-end suite of solutions lets you achieve a more granular view of every application and grow portfolios while still minimizing risk. Experian can help optimize your customer acquisition Experian provides a suite of decisioning engines, consumer attributes and customized modeling to help you optimize your customer acquisition process. These tools allow businesses to better target their marketing efforts, streamline their onboarding with less friction and improve their fraud detection and mitigation efforts. The combination can deliver a powerful ROI. Learn more about Experian's customer acquisition solutions. Learn more

In today's fast-paced digital world, the risk of fraud across all industries is a constant threat. The traditional methods of fraud detection are no longer sufficient, as fraudsters become increasingly sophisticated in their attacks. However, with artificial intelligence (AI) and machine learning (ML) solutions, financial institutions can stay one step ahead of fraudsters. AI and machine learning-equipped fraud detection tools have the ability to identify suspicious activity and patterns of fraud that are imperceptible to the human brain. In this blog post, we’ll dive into the significance of AI and machine learning in fraud detection and how these solutions are uniquely equipped to handle the demands of modern-day risk management. Understanding artificial intelligence and machine learning AI and machine learning solutions are transformative technologies that are reshaping the landscape of many industries. AI, at its core, is a field of computer science that simulates human intelligence in machines, enabling them to learn from experience and perform tasks that normally require human intellect. Machine learning, a subset of AI, is the science of getting computers to learn and act like humans do, but with minimal human intervention. They can analyze vast amounts of data within seconds, identifying patterns and trends that would be impossible for a human to recognize. When it comes to fraud detection, this ability is invaluable.  Advantages of fraud detection using machine learning AI and machine learning have several benefits that make them valuable in fraud detection. One significant advantage is that these technologies can recognize patterns that are too complex for humans to identify. By running through a vast set of data points, these solutions can pinpoint anomalous behavior, and thereby prevent financial losses. AI analytics tools are adept at monitoring complex networks, detecting the dispersion of attacks that may involve multiple individuals and entities, and correlating activity patterns that would otherwise be hidden. Machine learning algorithms can take these patterns and turn them into mathematical models that help identify instances of fraud before the damage takes place. Secondly, they continuously learn from new data, which allows them to become more efficient in identifying fraud as they process more data. Thirdly, they automate fraud mitigation processes, which significantly reduces the need for manual interventions that may consume valuable time and resources. Another significant benefit of machine learning is its analytics capabilities, which allow organizations to gain valuable insights into customer behavior and fraud patterns. With AI analytics, they can detect and investigate fraudulent activities in real-time, and combine it with other tools to help detect and mitigate fraud risk. For example, in financial services, AI fraud detection can help banks and financial service providers detect and prevent fraud in their systems, add value to their services and improve customer satisfaction. The future of fraud detection and machine learning The rate at which technology is evolving means that machine learning and AI fraud detection will become increasingly important in the future. In the next few years, we can expect a more sophisticated level of fraud detection using unmanned machine systems, robotics process automation, and more. Ultimately, this will improve the efficiency and effectiveness of fraud detection. AI-based fraud management solutions are taking center stage. Organizations must leverage advanced machine learning and AI analytics solutions to prevent and mitigate cyber risks and comply with regulatory mandates. The benefits extend far beyond the financial bottom line to improving the safety and security of customers. AI and machine learning solutions offer accurate, efficient and proactive routes to managing the risk of fraud in an ever-changing environment. How can Experian® help Integrating machine learning for fraud detection represents a significant advancement in cybersecurity. Fraud management solutions detect, prevent and manage fraud across all industries, including financial services, healthcare and telecommunications. With the advancement of technology, fraud management solutions now integrate machine learning to improve their processes. Experian® provides fraud prevention solutions, including machine learning models and AI analytics, which can help more effectively mitigate fraud risk, streamline fraud investigations and create a more secure digital environment for all. With Experian’s AI analytics, risk mitigation tools and fraud management solutions, organizations can stay one step ahead of fraudsters and protect their brand reputation, customer trustworthiness and corporate data. Embracing these solutions can save organizations from significant losses, reputational damage and regulatory scrutiny. To learn more about how to future-proof your business and safeguard your customers from fraud, check out Experian’s robust suite of fraud prevention solutions. Want to hear what our industry experts think? Check out this on-demand webinar on artificial intelligence and machine learning strategies. Learn more Watch webinar *This article includes content created by an AI language model and is intended to provide general information.

As the sophistication of fraudulent schemes increases, so must the sophistication of your fraud detection analytics. This is especially important in an uncertain economic environment that breeds opportunities for fraud. It's no longer enough to rely on old techniques that worked in the past. Instead, you need to be plugged into machine learning, artificial intelligence (AI) and real-time monitoring to stay ahead of criminal attempts. Your customers have come to expect cutting-edge security, and fraud analytics is the best way to meet — and surpass — those expectations. Leveraging these analytics can help your business better understand fraud techniques, uncover hidden insights and make more strategic decisions. What is fraud analytics? Fraud analytics refers to the idea of preventing fraud through sophisticated data analysis that utilizes tools like machine learning, data mining and predictive AI.1 These services can analyze patterns and monitor for anomalies that signal fraud attempts.2 While at first glance this may sound like a lot of work, it's necessary in today's technologically savvy culture. Fraud attempts are becoming more sophisticated, and your fraud detection services must do the same to keep up. Why is fraud analytics so important? According to the Experian® 2023 US Identity and Fraud Report, fraud is a growing issue that businesses cannot ignore, especially in an environment where economic uncertainty provides a breeding ground for fraudsters. Last year alone, consumers lost $8.8 billion — an increase of 30 percent over the previous year. Understandably, nearly two-thirds of consumers are at least somewhat concerned about online security. Their worries range from authorized push payment scams (such as phishing emails) to online privacy, identity theft and stolen credit cards. Unfortunately, while 75 percent of surveyed businesses feel confident in protecting against fraud, only 45 percent understand how fraud impacts their business. There's a lot of unearned confidence out there that can leave businesses vulnerable to attack, especially with nearly 70 percent of businesses admitting an increase in fraud loss in recent years. The types of fraud that businesses most frequently encounter include: Authorized push payment fraud: Phishing emails and other schemes that persuade consumers to deposit funds into fraudulent accounts. Transactional payment fraud: When fraudulent actors steal credit card or bank account information, for example, to make unauthorized payments. Account takeover: When a fraudster gains access to an account that doesn't belong to them and changes login details to make unauthorized transactions. First-party fraud: When an account holder uses their own account to commit fraud, like misrepresenting their income to get a lower loan rate. Identity theft: Any time a person's private information is used to steal their identity. Synthetic identity theft: When someone combines real and fake personal data to create an identity that's used to commit fraud. How can fraud analytics be used to help your business? More than 85% of consumers expect businesses to respond to their security and fraud concerns. A good portion of them (67 percent) are even ready to share their personal data with trusted sources to help make that happen. This means that investing in risk and fraud analytics is not only vital for keeping your business and customer data secure, but it will score points with your consumers as well. So how can your business utilize fraud analytics? Machine learning is a great place to start. Rather than relying on outdated rules-based analytic models, machine learning can vastly increase your speed in identifying fraud attempts. This means that when a new fraudulent trend emerges, your machine learning software can pinpoint it fast and flag your security team. Machine learning also lets you automatically analyze large data sets across your entire customer portfolio, improving customer experiences and your response time. In general, the best way for your business to use fraud analytics is by utilizing a multi-layered approach, such as the robust fraud management solutions offered by Experian. Instead of a one-size-fits-all solution, Experian lets you customize a framework of physical and digital data security that matches your business needs. This framework includes a cloud-based platform, machine learning for streamlined data analytics, biometrics and other robust identity-authentication tools, real-time alerts and end-to-end integration. How Experian can help Experian's platform of fraud prevention solutions and advanced data analytics allows you to be at the forefront of fraud detection. The platform includes options such as: Account takeover prevention. Account takeovers can go unnoticed without strong fraud detection. Experian's account takeover prevention tools automatically flag and monitor unusual activities, increase efficiency and can be quickly modified to adapt to the latest technologies. Bust-out fraud prevention. Experian utilizes proactive monitoring and early detection via machine learning to prevent bust-out fraud. Access to premium credit data helps enhance detection.  Commercial entity fraud prevention. Experian's Sentinel fraud solutions blend consumer and business datasets to create predictive insights on business legitimacy and credit abuse likelihood. First-party fraud prevention. Experian's first-party fraud prevention tools review millions of transactions to detect patterns, using machine learning to monitor credit data and observations. Global data breach protection. Experian also offers data breach protection services, helping you use turnkey solutions to build a program of customer notifications and identity protection. Identity protection. Experian offers identity protection tools that deliver a consistent brand experience across touchpoints and devices. Risk-based authentication. Minimize risk with Experian's adaptive risk-based authentication tools. These tools use front- and back-end authentication to optimize cost, risk management and customer experience. Synthetic identity fraud protection. Synthetic identity fraud protection guards against the fastest-growing financial crimes. Automated detection rules evaluate behavior and isolate traits to reduce false positives. Third-party fraud prevention. Experian utilizes third-party prevention analytics to identify potential identity theft and keep your customers secure. Your business's fraud analytics system needs to increase in sophistication faster than fraudsters are fine-tuning their own approaches. Experian's robust analytics solutions utilize extensive consumer and commercial data that can be customized to your business's unique security needs. Experian can help secure your business from fraud Experian is committed to helping you optimize your fraud analytics. Find out today how our fraud management solutions can help you. Learn more 1 Pressley, J.P. "Why Banks Are Using Advanced Analytics for Faster Fraud Detection," BizTech, July 25, 2023. https://biztechmagazine.com/article/2023/07/why-banks-are-using-advanced-analytics-faster-fraud-detection 2 Coe, Martin and Melton, Olivia. "Fraud Basics," Fraud Magazine, March/April 2022. https://www.fraud-magazine.com/article.aspx?id=4295017143

"Grandma, it’s me, Mike.” Imagine hearing the voice of a loved one (or what sounds like it) informing you they were arrested and in need of bail money. Panicked, a desperate family member may follow instructions to withdraw a large sum of money to provide to a courier. Suspicious, they even make a video call to which they see a blurry image on the other end, but the same voice. When the fight or flight feeling settles, reality hits. Sadly, this is not the scenario of an upcoming Netflix movie. This is fraud – an example of a new grandparent scam/family emergency scam happening at scale across the U.S. While generative AI is driving efficiencies, personalization and improvements in multiple areas, it’s also a technology being adopted by fraudsters. Generative AI can be used to create highly personalized and convincing messages that are tailored to a specific victim. By analyzing publicly available social media profiles and other personal information, scammers can use generative AI to create fake accounts, emails, or phone calls that mimic the voice and mannerisms of a grandchild or family member in distress. The use of this technology can make it particularly difficult to distinguish between real and fake communication, leading to increased vulnerability and susceptibility to fraud. Furthermore, generative AI can also be used to create deepfake videos or audio recordings that show the supposed family member in distress or reinforce the scammer's story. These deepfakes can be incredibly realistic, making it even harder for victims to identify fraudulent activity. What is Generative AI? Generative artificial intelligence (GenAI) describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Generative AI has the potential to revolutionize many industries by creating new and innovative content, but it also presents a significant risk for financial institutions. Cyber attackers can use generative AI to produce sophisticated malware, phishing schemes, and other fraudulent activities that can cause data breaches, financial losses, and reputational damage. This poses a challenge for financial organizations, as human error remains one of the weakest links in cybersecurity. Fraudsters capitalizing on emotions such as fear, stress, desperation, or inattention can make it difficult to protect against malicious content generated by generative AI, which could be used as a tactic to defraud financial institutions. Four types of Generative AI used for Fraud: Fraud automation at scale Fraudulent activities often involve multiple steps which can be complex and time-consuming. However, GenAI may enable fraudsters to automate each of these steps, thereby establishing a comprehensive framework for fraudulent attacks. The modus operandi of GenAI involves the generation of scripts or code that facilitates the creation of programs capable of autonomously pilfering personal data and breaching accounts. Previously, the development of such codes and programs necessitated the expertise of seasoned programmers, with each stage of the process requiring separate and fragmented development. Nevertheless, with the advent of GenAI, any fraudster can now access an all-encompassing program without the need for specialized knowledge, amplifying the inherent danger it poses. It can be used to accelerate fraudsters techniques such as credential stuffing, card testing and brute force attacks. Text content generation In the past, one could often rely on spotting typos or errors as a means of detecting such fraudulent schemes. However, the emergence of GenAI has introduced a new challenge, as it generates impeccably written scripts that possess an uncanny authenticity, rendering the identification of deceit activities considerably more difficult. But now, GenAI can produce realistic text that sounds as if it were from a familiar person, organization, or business by simply feeding GenAI prompts or content to replicate. Furthermore, the utilization of innovative Language Learning Model (LLM) tools enables scammers to engage in text-based conversations with multiple victims, skillfully manipulating them into carrying out actions that ultimately serve the perpetrators' interests. Image and video manipulation In a matter of seconds, fraudsters, regardless of their level of expertise, are now capable of producing highly authentic videos or images powered by GenAI. This innovative technology leverages deep learning techniques, using vast amounts of collected datasets to train artificial intelligence models. Once these models are trained, they possess the ability to generate visuals that closely resemble the desired target. By seamlessly blending or superimposing these generated images onto specific frames, the original content can be replaced with manipulated visuals. Furthermore, the utilization of AI text-to-image generators, powered by artificial neural networks, allows fraudsters to input prompts in the form of words. These prompts are then processed by the system, resulting in the generation of corresponding images, further enhancing the deceptive capabilities at their disposal. Human voice generation The emergence of AI-generated voices that mimic real people has created new vulnerabilities in voice verification systems. Firms that rely heavily on these systems, such as investment firms, must take extra precautions to ensure the security of their clients' assets. Criminals can also use AI chatbots to build relationships with victims and exploit their emotions to convince them to invest money or share personal information. Pig butchering scams and romance scams are examples of these types of frauds where AI chatbots can be highly effective, as they are friendly, convincing, and can easily follow a script. In particular, synthetic identity fraud has become an increasingly common tactic among cybercriminals. By creating fake personas with plausible social profiles, hackers can avoid detection while conducting financial crimes. It is essential for organizations to remain vigilant and verify the identities of any new contacts or suppliers before engaging with them. Failure to do so could result in significant monetary loss and reputational damage. Leverage AI to fight bad actors In today's digital landscape, businesses face increased fraud risks from advanced chatbots and generative technology. To combat this, businesses must use the same weapons than criminals, and train AI-based tools to detect and prevent fraudulent activities. Fraud prediction: Generative AI can analyze historical data to predict future fraudulent activities. By analyzing patterns in data and identifying potential risk factors, generative AI can help fraud examiners anticipate and prevent fraudulent behavior. Machine learning algorithms can analyze patterns in data to identify suspicious behavior and flag it for further investigation. Fraud Investigation: In addition to preventing fraud, generative AI can assist fraud examiners in investigating suspicious activities by generating scenarios and identifying potential suspects. By analyzing email communications and social media activity, generative AI can uncover hidden connections between suspects and identify potential fraudsters. To confirm the authenticity of users, financial institutions should adopt sophisticated identity verification methods that include liveness detection algorithms and document-centric identity proofing, and predictive analytics models. These measures can help prevent bots from infiltrating their systems and spreading disinformation, while also protecting against scams and cyberattacks. In conclusion, financial institutions must stay vigilant and deploy new tools and technologies to protect against the evolving threat landscape. By adopting advanced identity verification solutions, organizations can safeguard themselves and their customers from potential risks. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

Trust is the primary factor in any business building a long-lasting relationship, especially when a company operates globally and wants to build a loyal customer base. With the rapid acceleration of digital shopping and transactions comes a growing fraud landscape. And, given the continual increase of people wanting to transact online, marketplace companies – from ecommerce apps, ridesharing, to rental companies – need to have ideal strategies in place to protect themselves and their customers from fraudulent activities. Without ideal risk mitigation or comprehensive fraud and identity proofing strategies, marketplaces may find themselves facing the following: Card-not-present (CNP) Fraud: As online shopping increases, customers can’t provide a credit card directly to the merchant. That’s why fraudsters can use stolen credit card information to make unauthorized transactions. And in most cases, card owners are unaware of being compromised. Without an integrated view of risk, existing credit card authentication services used in isolation can result in high false positives, friction and a lack of card issuer support. Unverified Consumer Members, Vendors, Hosts & Drivers: From digital marketplace merchants like Etsy and Amazon, to peer-to-peer sharing economies like AirBnB, Uber and Lyft, the marketplace ecosystem is prone to bad actors who use false ID techniques to exploit both the platform and consumers for monetary gain. Additionally, card transaction touchpoints across the customer lifecycle increases risks of credit card authentication. This can be at account opening, account management when changes to existing account information is necessary, or at checkout. Buy Now, Pay Later (BNPL) Muling: While a convenient way for consumers to plan for their purchases, experts warn that without cautionary and security measures, BNPL can be a target for digital fraud. Fraudsters may use their own or fabricated identities or leverage account takeover to gain access to a legitimate user’s account and payment information to make purchases with no intent to repay. This leaves the BNPL provider at the risk of unrecoverable monetary losses that can impact the business’ risk tolerance. Forged Listings & Fake Accounts: Unauthorized vendors that create a fake account using falsified identities, stolen credit cards and publish fake listings and product reviews are another threat faced by ecommerce marketplaces. These types of fraud can happen without the vast data sources necessary to assess the risk of a customer and authenticate credit cards among other fraud and identity verification solutions. By not focusing on establishing trust, fraud mitigation management solutions and identity proofing strategies, businesses can often find themselves with serious monetary, reputational, and security qualms. Interested in learning more? Download Experian’s Building Trust in Digital Marketplaces e-book and discover the strategies digital marketplaces, like the gig economy and peer-to-peer markets, can take to keep their users safe, and protected from fraudulent activity. For additional information on how Experian is helping businesses mitigate fraud, explore our comprehensive suite of identity and fraud solutions. Download e-book

More than half of U.S. businesses say they discuss fraud management often, making fraud detection in banking top-of-mind. Banking fraud prevention can seem daunting, but with the proper tools, banks, credit unions, fintechs, and other financial institutions can frustrate and root out fraudsters while maintaining a positive experience for good customers. What is banking fraud? Banking fraud is a type of financial crime that uses illegal means to obtain money, assets, or other property owned or held by a bank, other financial institution, or customers of the bank. This type of fraud can be difficult to detect when misclassified as credit risk or written off as a loss rather than investigated and prevented in the future. Fraud that impacts financial institutions consists of small-scale one-off events or larger efforts perpetrated by fraud rings. Not long ago, many of the techniques utilized by fraudsters required in-person or phone-based activities. Now, many of these activities are online, making it easier for fraudsters to disguise their intent and perpetrate multiple attacks at once or in sequence. Banking fraud can include: Identity theft: When a bad actor steals a consumer’s personal information and uses it to take money, open credit accounts, make purchases, and more. Check fraud: This type of fraud occurs when a fraudster writes a bad check, forges information, or steals and alters someone else’s check. Credit card fraud: A form of identity theft where a bad actor makes purchases or gets a cash advance in the name of an unsuspecting consumer. The fraudster may takeover an existing account by gaining access to account numbers online, steal a physical card, or open a new account in someone else’s name.  Phishing: These malicious efforts allow scammers to steal personal and account information through use of email, or in the case of smishing, through text messages. The fraudster often sends a link to the consumer that looks legitimate but is designed to steal login information, personally identifiable information, and more. Direct deposit account fraud: Also known as DDA fraud, criminals monetize stolen information to open new accounts and divert funds from payroll, assistance programs, and more. Unfortunately, this type of fraud doesn’t just lead to lost funds – it also exposes consumer data, impacts banks’ reputations, and has larger implications for the financial system. Today, top concerns for banks include generative AI (GenAI) fraud, peer-to-peer (P2P) payment scams, identity theft and transaction fraud. Without the proper detection and prevention techniques, it’s difficult for banks to keep fraudsters perpetrating these schemes out. What is banking fraud prevention? Detecting and preventing banking fraud consists of a set of techniques and tasks that help protect customers, assets and systems from those with malicious intent. Risk management solutions for banks identify fraudulent access attempts, suspicious transfer requests, signs of false identities, and more. The financial industry is constantly evolving, and so are fraudsters. As a result, it’s important for organizations to stay ahead of the curve by investing in new fraud prevention technologies. Depending on the size and sophistication of your institution, the tools and techniques that comprise your banking fraud prevention solutions may look different. However, every strategy should include multiple layers of friction designed to trip up fraudsters enough to abandon their efforts, and include flags for suspicious activity and other indicators that a user or transaction requires further scrutiny.   Some of the emerging trends in banking fraud prevention include: Use of artificial intelligence (AI) and machine learning (ML). While these technologies aren’t new, they are finding footing across industries as they can be used to identify patterns consistent with fraudulent activity – some of which are difficult or time-consuming to detect with traditional methods. Behavioral analytics and biometrics. By noting standard customer behaviors — e.g., which devices they use and when — and how they use those devices — looking for markers of human behavior vs. bot or fraud ring activity — organizations can flag riskier users for additional authentication and verification. Leveraging additional data sources. By looking beyond standard credit reports when opening credit accounts, organizations can better detect signs of identity theft, synthetic identities, and even potential first-party fraud.     With real-time fraud detection tools in place, financial institutions can more easily identify good consumers and allow them to complete their requests while applying the right amount and type of friction to detect and prevent fraud.   How to prevent and detect banking fraud In order to be successful in the fight against fraud and keep yourself and your customers safe, financial institutions of all sizes and types must: Balance risk mitigation with the customer experience Ensure seamless interactions across platforms for known consumers who present little to no risk Leverage proper identity resolution and verification tools Recognize good consumers and apply the proper fraud mitigation techniques to riskier scenarios With Experian’s interconnected approach to fraud detection in banking, incorporating data, analytics, fraud risk scores, device intelligence, and more, you can track and assess various activities and determine where additional authentication, friction, or human intervention is required. 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Banking uncertainty creates opportunity for fraud The recent regional bank collapses left anxious consumers scrambling to withdraw their funds or open new accounts at other institutions. Unfortunately, this situation has also created an opportunity for fraudsters to take advantage of the chaos. Criminals are exploiting the situation and posing as legitimate customers looking to flee their current bank to open new accounts elsewhere. Financial institutions looking to bring on these consumers as new clients must remain vigilant against fraudulent activity. Fraudsters also prey on vulnerable individuals who may be financially stressed and uncertain about the future. This creates a breeding ground for scams as fear and uncertainty cloud judgment and make people more susceptible to manipulation. Beware of fraudulent tactics Now, it is more important than ever for financial institutions to be vigilant in their due diligence processes. As they navigate this period of financial turbulence, they must take extra precautions to ensure that new customers are who they say they are by verifying customer identities, conducting thorough background checks where necessary, and monitoring transactions for any signs of suspicious activity. Consumers should also maintain vigilance — fraudulent schemes come in many forms, from phishing scams to fake investment opportunities promising unrealistic returns. To protect yourself against these risks, it is important to remain vigilant and take precautions such as verifying the legitimacy of any offers or investments before investing, monitoring your bank and credit card statements regularly for suspicious activity, and being skeptical of unsolicited phone calls, emails, or text messages. Security researcher Johannes Ulrich reported that threat actors are jumping at the opportunity, registering suspicious domains related to Silicon Valley Bank (SVB) that are likely to be used in attacks. Ulrich warned that the scammers might try to contact former clients of SVB to offer them a support package, legal services, loans, or other fake services relating to the bank's collapse. Meanwhile, on the day of the SVB closure, synthetic identity fraud began to climb from an attack rate of .57 to a first peak of 1.24% on the Sunday following the closure, or an increase of 80%. After the first spike reduced on March 14, we only saw a return of an even higher spike on March 21 to 1.35%, with bumps continuing since then. As the economy slows and fraud rises, don’t let your guard down The recent surge in third-party attack rates on small business and investment platforms is a cause for concern. There was a staggering nearly 500% increase in these attacks between March 7th and 11th, which coincided with the release of negative news about SVB. Bad actors had evidently been preparing for this moment and were quick to exploit vulnerabilities they had identified across our financial system. They used sophisticated bots to create multiple accounts within minutes of the news dropping and stole identities to perpetrate fraudulent activities. This underscores the need for increased vigilance and proactive measures to protect against cyber threats impacting financial institutions. Adopting stronger security measures like multi-factor authentication, real-time monitoring, and collaboration with law enforcement agencies for timely identification of attackers is of paramount importance to prevent similar fraud events in the future. From frictionless to friction-right As businesses seek to stabilize their operations in the face of market turbulence, they must also remain vigilant against the threat of fraud. Illicit activities can permeate a company's ecosystem and disrupt its operations, potentially leading to financial losses and reputational damage. Safeguarding against fraud is not a simple task. Striking a balance between ensuring a smooth customer experience and implementing effective fraud prevention measures can be a challenging endeavor. For financial institutions in particular, being too stringent in fraud prevention efforts may drive customers away, while being too lenient can expose them to additional fraud risks. This is where a waterfall approach, such as that offered by Experian CrossCore®, can prove invaluable. By leveraging an array of fraud detection tools and technologies, businesses can tailor their fraud prevention strategies to suit the specific needs and journeys of different customer segments. This layered, customized approach can help protect businesses from fraud while ensuring a seamless customer experience. Learn more