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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

In a series of articles, we talk about different types of fraud and how to best solve for them. This article will explore first-party fraud and how it's similar to biting into a cookie you think is chocolate chip, only to find that it’s filled with raisins. The raisins in the cookie were hiding in plain sight, indistinguishable from chocolate chips without a closer look, much like first-party fraudsters. What is first-party fraud? First-party fraud refers to instances when an individual purposely misrepresents their identity in exchange for goods or services. Borrowers may falsify income, misrepresent employment or exploit credit systems without the intention of repaying. In the financial services industry, it's often miscategorized as credit loss and written off as bad debt, which masks true fraud exposure and distorts credit-risk forecasting. Read now: Download Experian’s latest research on first-party fraud Common types of first-party fraud include: Chargeback fraud: Also known as "friendly fraud," chargeback fraud occurs when an individual knowingly makes a purchase with their credit card and then requests a chargeback from the issuer, claiming they didn't authorize the purchase. Application fraud: This takes place when an individual uses stolen or manipulated information to apply for a loan, credit card or job. In 2023, the employment sector accounted for 45% of all false document submissions — 70% of those who falsified their resumes still got hired. Fronting: Done to get cheaper rates, this form of insurance fraud happens when a young or inexperienced individual is deliberately listed as a named driver, when they're actually the main driver of the vehicle. Goods lost in transit fraud (GLIT): This occurs when an individual claims the goods they purchased online did not arrive. To put it simply, the individual is getting a refund for something they actually already received. Bust-out: This occurs when an individual builds what appears to be good credit behavior over time, making small purchases and on-time payments, and then suddenly maxes out their credit lines or abandons repayment entirely. The account looks legitimate until the “bust-out,” making it one of the hardest forms of first-party fraud to detect. Credit washing: This happens when an individual falsely disputes legitimate accounts or debts to have them removed from their credit report. By portraying valid obligations as fraud, the individual can temporarily improve their credit standing or access new credit they wouldn’t qualify for otherwise. A first-party fraudster can also recruit “money mules” — individuals who are persuaded to use their own information to obtain credit or merchandise on behalf of a larger fraud ring. This type of fraud has become especially prevalent as more consumers are active online. Money mules constitute up to 0.3% of accounts at U.S. financial institutions, or an estimated $3 billion in fraudulent transfers. How does it impact my organization? Firstly, first-party fraud can cause significant losses. According to our latest study, first-party fraud costs $36.7 million annually on average. Nearly one-third of respondents in our annual Identity and Fraud survey reported that first-party fraud had increased stress on their businesses. An imperfect first-party fraud solution can also strain relationships with good customers and hinder growth. When lenders have to interpret actions and behavior to assess customers, there’s a lot of room for error and losses. Those same losses hinder growth when misclassification inflates credit-risk estimates and masks true fraud exposure. This type of fraud isn’t a single-time event, and it doesn’t occur at just one point in the customer lifecycle. It occurs when good customers develop fraudulent intent, when new applicants who have positive history with other lenders have recently changed circumstances or when seemingly good applicants have manipulated their identities to mask previous defaults. Finally, misclassified first-party fraud losses can impact how your organization categorizes and manages risk – and that’s something that touches every department. Solving the first-party fraud problem First-party fraud detection requires a shift in how we think about the fraud problem. It starts with the ability to separate first-party fraud and credit risk, since they are often indiscernible at origination.  To effectively combat first-party fraud, businesses should consider the following actions: Define first-party fraud as its own risk: Don’t blend it into credit loss. Build targeted models that use behavioral, identity and activity signals. Start with first-payment default as a key indicator. Use a longer risk window: A 12-month view helps surface early fraud patterns and supports stronger credit and fraud analysis. Unify fraud, credit and compliance decisions: Coordinated strategies reduce blind spots and improve customer experience. Upgrade your models: Apply machine learning and segment by factors like credit age or product type to better detect bust-outs and early defaults. Combine credit and noncredit data: Use device intelligence, identity velocity and behavioral data to help separate fraud from financial hardship. Benchmark against peers: Regular comparisons help assess exposure, validate performance and refine strategies. How Experian can help As we’ve already discussed, the fraud problem is complex. However with a partner like Experian, you can leverage the fraud risk management strategies required to perform a closer examination and the ability to differentiate between the types of fraud so you can determine the best course of action moving forward. Additionally, our robust fraud management solutions can be used for synthetic identity fraud and account takeover fraud prevention, which can help you minimize customer friction to improve and deepen your relationships while preventing fraud. Contact us if you’d like to learn more about how Experian is using our identity expertise, data and analytics to improve identity resolution and detect and prevent all types of fraud. Contact us Read report Watch webinar

In today’s fast-paced world, the telecommunications industry is not just about connecting calls or sending messages. It’s about creating seamless digital experiences, especially when onboarding new customers. However, with the rise of digital services, the industry faces an increasing challenge: the need to mitigate fraud while streamlining the onboarding process.  The digital onboarding revolution Digital onboarding has transformed the way customers join telecommunications services. No longer are people required to visit a physical store or wait for lengthy paperwork. Instead, they can sign up for mobile, internet or TV services from the comfort of their homes, often within minutes. The convenience, however, has opened new doors for fraudsters. As the onboarding process happens online, the risk of identity theft, synthetic identity fraud and other fraudulent activities has surged. So, how can telecom companies provide fritctionless experiences while keeping fraud at bay? Mitigating fraud in telecommunications onboarding Know your customer (KYC) verification: Implement robust KYC solutions to verify the identity of new customers. This may include identity document checks, facial recognition or biometric authentication. Device and location data; and velocity: Analyze the device and location data of applicants. Does the device match the customer’s claimed location? Unusual patterns could signal potential fraud.  Behavioral analysis: Monitor user behavior during the onboarding process. Frequent changes in information or suspicious browsing activity may indicate fraudulent intent.   Machine learning (ML) and artificial intelligence (AI): Leverage AI/ML algorithms to detect patterns and anomalies humans might miss. These technologies can adapt and evolve to stay ahead of fraudsters.   Document verification: Use document verification services to ensure that documents provided by customers are genuine. This can include checks for altered or forged documents. Industry data sharing–consortia: Collaborate with industry databases and share fraud-related information to help identify applicants with a history of fraudulent activity or reveal patterns. The balancing act While it’s crucial to mitigate fraud, telecommunication companies must strike a balance between security and a seamless onboarding experience. Customers demand a hassle-free process, and overly stringent security measures can deter potential subscribers. By combining advanced technology, behavioral analysis and proactive fraud prevention strategies, telecom companies can create a secure digital onboarding journey that minimizes risk without compromising user experience. In doing so, they empower customers to embrace the convenience of digital services while staying one step ahead of fraudsters in today’s interconnected world.  Learn more about Experian and the telecom industry Learn more about our fraud and identity solutions

Have you heard about the mischievous ghosts haunting our educational institutions? No, I am not talking about Casper's misfit pals. These are the infamous ghost students! They are not here for a spooky study session, oh no! They are cunning fraudsters lurking in the shadows, pretending to be students who never attend classes. It is taking ghosting to a whole new level. Understanding ghost student fraud Ghost student fraud is a serious and alarming issue in the educational sector. The rise of online classes due to the pandemic has made it easier for fraudsters to exploit application systems and steal government aid meant for genuine students. Community colleges have become primary targets due to slower adoption of cybersecurity defenses. It is concerning to hear that a considerable number of applications, such as in California (where Social Security numbers are not required at enrollment), are fictitious, with potential losses in financial aid meant for students in need. The use of stolen or synthetic identities in creating bot-powered applications further exacerbates the problem. The consequences of enrollment fraud can have a profound impact on institutions and students. The recent indictment of individuals involved in enrollment fraud, where identities were stolen to receive federal student loans, highlights the severity of the issue. Unfortunately, the lack of awareness and inadequate identity document verification processes in many institutions make it difficult to fully grasp the extent of the problem. What is a ghost student? Scammers use different methods to commit ghost student loan fraud, including creating fake schools or enrolling in real colleges. Some fraudsters use deceitful tactics to obtain the real identities of students, and then they use it to fabricate loan applications. Types of ghost loan fraud, include: Fake loan offers: Fraudsters contact students via various channels, claiming to offer exclusive student loan opportunities with attractive terms and low interest rates. They often request personal and financial information including their SSN and bank account information and use it to create ghost loans. Identity theft: Threat actors will steal personal info through data breaches or phishing. They will then forge loan applications using the victim’s identity. Targeting vulnerable individuals: Ghost student loan fraud tends to prey on those already burdened by debt. Scammers may target borrowers with poor credit history, promising loan forgiveness or debt consolidation plans in exchange for a fee. Once the victim pays, the fraudsters disappear. Ultimately, addressing ghost student fraud requires a multi-faceted approach involving collaboration between educational institutions, government agencies, and law enforcement to safeguard the accessibility and integrity of education for all deserving students. Safeguarding the financial integrity of educational institutions One powerful weapon in the battle against ghost student fraudsters is the implementation of robust identity verification solutions. Financial institutions, online marketplaces, and government entities have long employed such tools to verify the authenticity of individuals, and their application in the educational domain can be highly effective. By leveraging these tools, institutions can swiftly and securely carry out synthetic fraud detection and confirm the identity of applicants by cross-referencing multiple credible sources of information. For instance, government-issued IDs can be verified against real-time selfies, email addresses can be screened against reliable databases, and personally identifiable information (PII) can be compared to third-party dark web data to detect compromised identities. Clinching evidence from various sources renders it nearly impossible for fraudsters to slip past the watchful eyes of enrollment officers. Moreover, implementation of identity verification measures can be facilitated through low-code implementation, ensuring seamless integration into existing enrollment workflows without requiring extensive technical expertise or incurring exorbitant development costs. To further fortify security measures, educational institutions may consider incorporating biometric enrollment and authentication solutions. By requiring face or voice biometrics for accessing school resources, institutions can create an additional layer of protection against fraudsters and their ethereal counterparts. The reluctance of fraudsters to enroll their own biometric data serves as a powerful deterrent against their intrusive activities. Taking action By adopting these robust measures, higher educational institutions can fortify their defenses against ghost student fraud and maintain the integrity of their finances. The use of online identity verification methods and biometric authentication systems not only strengthens the enrollment process but serves as a stringent reminder that there is no resting place for fraudsters within the hallowed halls of education. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call. *The SSN Verification tool, better known as eCBSV is also a tool that can be utilized to verify SSN.  *This article leverages/includes content created by an AI language model and is intended to provide general information.

"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

Money mule fraud is a type of financial scam in which criminals exploit individuals, known as money mules, to transfer stolen money or the proceeds of illegal activities. Money mule accounts are becoming increasingly difficult to distinguish from legitimate customers, especially as criminals find new ways to develop hard-to-detect synthetic identities. How money mule fraud typically works: Recruitment: Fraudsters seek out potential money mules through various means, such as online job ads, social media, or email/messaging apps. They will often pose as legitimate employers offering job opportunities promising compensation or claiming to represent charitable organizations. Deception: Once a potential money mule is identified, the fraudsters use persuasive tactics to gain their trust. They may provide seemingly legitimate explanations like claiming the money is for investment purposes, charity donations or for facilitating business transactions. Money Transfer: The mule is instructed to receive funds to their bank or other financial account. The funds are typically transferred from other compromised bank accounts obtained through phishing or hacking. The mule is then instructed to transfer the money to another account, sometimes located overseas. Layering: To mask the origin of funds and make them difficult to trace, fraudsters will employ layering techniques. They may ask the mule to split funds into smaller amounts, make multiple transfers to different accounts, or use various financial platforms such as money services or crypto. Compensation: The money mule is often promised a percentage of transferred funds as payment.  However, the promised monies are lower than the dollars transferred, or sometimes the mule receives no payment at all. Legal consequences: Regardless whether mules know they are supporting a criminal enterprise or are unaware, they can face criminal charges. In addition, their personal information could be compromised leading to identity theft and financial loss. How can banks get ahead of the money mule curve: Know your beneficiaries Monitor inbound paymentsEngage identity verification solutionsCreate a “Mule Persona” behavior profileBeware that fraudsters will coach the mule, therefore confirmation of payee is no longer a detection solution Educate your customers to be wary of job offers that seem too good to be true and remain vigilant of requests to receive and transfer money, particularly from unknown individuals and organizations. How financial institutions can mitigate money mule fraud risk When new accounts are opened, a financial institution usually doesn’t have enough information to establish patterns of behavior with newly registered users and devices the way they can with existing users. However, an anti-fraud system should catch a known behavior profile that has been previously identified as malicious. In this situation, the best practice is to compare the new account holder’s behavior against a representative pool of customers, which will analyze things like: Spending behavior compared to the averagePayee profileSequence of actionsNavigation data related to machine-like or bot behaviorAbnormal or risky locationsThe account owner's relations to other users The risk engine needs to be able to collect and score data across all digital channels to allow the financial institution to detect all possible relationships to users, IP addresses and devices that have proven fraud behavior. This includes information about the user, account, location, device, session and payee, among others. If the system notices any unusual changes in the account holder’s personal information, the decision engine will flag it for review. It can then be actively monitored and investigated, if necessary. The benefits of machine learning This is a type of artificial intelligence (AI) that can analyze vast amounts of disparate data across digital channels in real time. Anti-fraud systems based on AI analytics and predictive analytics models have the ability to aggregate and analyze data on multiple levels. This allows a financial institution to instantly detect all possible relationships across users, devices, transactions and channels to more accurately identify fraudulent activity. When suspicious behavior is flagged via a high risk score, the risk engine can then drive a dynamic workflow change to step up security or drive a manual review process. It can then be actively monitored by the fraud prevention team and escalated for investigation. How Experian can help Experian’s fraud prevention solutions incorporate technology, identity-authentication tools and the combination of machine learning analytics with Experian’s proprietary and partner data to return optimal decisions to protect your customers and your business. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

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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Credit risk refers to the likelihood that a borrower will fail to repay a debt as agreed. Credit risk management is the art and science of utilizing risk mitigation tools to minimize losses while maximizing profits from lending activities.   Lenders can establish credit underwriting criteria for each of their products and utilize risk-based pricing to adjust the terms of a loan or line of credit based on the risk associated with the product and borrower. Credit portfolio management extends beyond originations and individual decisions to encompass portfolios as a whole.   Why is credit risk management important? Continuously managing credit risk matters because there's always a balancing act.   Tightening a credit box – using more restrictive underwriting criteria – might reduce credit losses. However, it can also decrease approval rates, excluding borrowers who would have repaid as agreed. Expanding a credit box might increase approval rates, but it is only beneficial if the profit from good new loans exceeds credit losses.   Fraud is also on the rise and becoming increasingly complex, making fraud management a crucial part of understanding risk. For instance, with synthetic identity fraud, fraudsters might “age an account" or make on-time payments before “busting out” or maxing out a credit card, and then abandoning the account.  If you examine payment activity alone, it may be challenging to classify the loss as either a fraud loss or a credit loss.  Additionally, external economic forces and consumer behavior are constantly in flux. Financial institutions need effective consumer risk management and to adjust their strategies to minimize losses. And they must dynamically adjust their underwriting criteria to account for these changes. You could be pushed off balance if you don't react in time. What does managing credit risk entail? Lenders have used the five C’s of credit to measure credit risk and make lending decisions for decades:  Character: The likelihood a borrower will repay the loan as agreed, often measured by analyzing their credit report and a credit risk score.   Capacity: The borrower's ability to pay, which lenders might measure by reviewing their outstanding debt, income, and debt-to-income ratio.   Capital: The borrower's commitment to the purchase, such as their down payment when buying a vehicle or home.   Collateral: The value of the collateral, such as a vehicle or home, for an auto loan or mortgage.   Conditions: The external conditions that can impact a borrower's ability to afford payments, such as broader economic trends.  Credit risk management considers these within the context of a lender’s goals and its specific lending products. For example, capital and collateral aren't relevant for unsecured personal loans, which makes character and capacity the primary drivers of a decision.   Credit risk management best practices at origination Advances in analytics, computing power and real-time access to additional data sources are helping lenders better measure some of the C’s.   For example, credit risk scores can more precisely assess character for a lender's target market than generic risk scores. Open banking data enables lenders to more accurately assess a borrower's capacity by directly analyzing their cash flows.   With these advances in mind, leading lenders:  View underwriting as a dynamic process: Lenders have always had to respond to changing forces, and the pandemic highlighted the need to be nimble. Consider how you can utilize analytical insights to quickly adjust your strategies.   Test the latest credit risk modeling techniques: Artificial intelligence (AI) and machine learning (ML) techniques can improve credit risk model performance and drive automated credit risk decisioning.  Use multiple data sources: Alternative credit data and consumer-permissioned data offer increased and real-time visibility into borrowers' creditworthiness to help lenders more accurately assess credit risk. These additional data sources can score those who are unscoreable by conventional models and help fuel ML credit risk models. Experian helps lenders measure and manage credit risk Experian is a leading provider of traditional credit data, alternative credit data and credit risk analytics.   For those who want to quickly benefit from the latest technological advancements, our Lift Premium credit risk model utilizes both traditional and alternative data to score up to 96 percent of U.S. consumers — compared to the 81 percent that conventional models can score.¹  Experian’s Ascend Platform and Ascend Intelligence Services™ can help lenders develop, deploy and monitor custom credit risk models to optimize their decisions.    With end-to-end platforms, our account and portfolio management services can help you limit risk, detect fraud, automate underwriting and identify opportunities to grow your business.   Learn more about credit risk management ¹Experian (2023). Lift Premium™ and Lift Plus™

The rise of the digital channel lead to a rise in new types of fraud – like cryptocurrency and buy now, pay later scams.  While the scams themselves are new, they’re based on tried-and-true schemes like account takeover and synthetic identity fraud that organizations have been working to thwart for years, once again driving home the need for a robust fraud solution.   While the digital channel is extremely attractive to many consumers due to convenience, it represents a balancing act for organizations – especially those with outdated fraud programs who are at increased risk for fraud. As organizations look for ways to keep themselves and the consumers they serve safe, many turn to fraud risk mitigation. What are fraud risk management strategies? Fraud risk management is the process of identifying, understanding, and responding to fraud risks. Proper fraud risk management strategies involve creating a program that detects and prevents fraudulent activity and reduces the risks associated with fraud. Many fraud risk management strategies are built on five principles: Fraud Risk AssessmentFraud Risk GovernanceFraud PreventionFraud DetectionMonitoring and Reporting By understanding these principles, you can build an effective strategy that meets consumer expectations and protects your business. Fraud risk assessment Fraud protection begins with an understanding of your organization’s vulnerabilities. Review your top risk areas and consider the potential losses you could face. Then look at what controls you currently have in place and how you can dial those up or down to impact both risk and customer experience. Fraud risk governance Fraud risk governance generally takes the form of a program encompassing the structure of rules, practices, and processes that surround fraud risk management. This program should include the fraud risk assessment, the roles and responsibilities of various departments, procedures for fraud events, and the plan for on-going monitoring. Fraud prevention “An ounce of prevention is worth a pound of cure.” This adage certainly rings true when it comes to fraud risk management. Having the right controls and procedures in place can help organizations stop a multitude of fraud types before they even get a foot in the door. Account takeover fraud prevention is an ideal example of how organizations can keep themselves and consumers safe. Fraud detection The only way to stop 100% of fraud is to stop 100% of interactions. Since that’s not a sustainable way to run a business, it’s important to have tools in place to detect fraud that’s already entered your ecosystem so you can stop it before damage occurs. These tools should monitor your systems to look for anomalies and risky behaviors and have a way to flag and report suspicious activity. Monitoring and reporting Once your fraud detection system is in place, you need active monitoring and reporting set up. Some fraud detection tools may include automatic next steps for suspicious activity such as step-up authentication or another risk mitigation technique. In other cases, you’ll need to get a person involved. In these cases it’s critical to have documented procedure and routing in place to ensure that potential fraud is assessed and addressed in a timely fashion. How to implement fraud risk management By adhering to the principles above, you can gain a holistic view of your current risk level, determine where you want your risk level to be, and what changes you’ll need to make to get there. While you might already have some of the necessary tools in place, the right next step is usually finding a trusted partner who can help you review your current state and help you use the right fraud prevention services that fit your risk tolerance and customer experience goals. To learn more about how Experian can help you leverage fraud prevention solutions, visit us or request a call. Learn more

With fraud expected to surge amid uncertain economic conditions, fraudsters are preparing new deception techniques to outsmart businesses and deceive consumers. To help businesses prepare for the coming fraud threats, we created the 2023 Future of Fraud Forecast. Here are the fraud trends we expect to see over the coming year: Fake texts from the boss: Given the prevalence of remote work, there’ll be a sharp rise in employer text fraud where the “boss” texts the employee to buy gift cards, then asks the employee to email the gift card numbers and codes. Beware of fake job postings and mule schemes: With changing economic conditions, fraudsters will create fake remote job postings, specifically designed to lure consumers into applying for the job and providing private details like a social security number or date of birth on a fake employment application. Frankenstein shoppers spell trouble for retailers: Fraudsters can create online shopper profiles using synthetic identities so that the fake shopper’s legitimacy is created to outsmart retailers’ fraud controls. Social media shopping fraud: Social commerce currently has very few identity verification and fraud detection controls in place, making the retailers that sell on these platforms easy targets for fraudulent purchases. Peer-to-peer payment problems: Fraudsters love peer-to-peer payment methods because they’re an instantaneous and irreversible way to move money, enabling fraudsters to get cash with less work and more profit “As fraudsters become more sophisticated and opportunistic, businesses need to proactively integrate the latest technology, data and advanced analytics to mitigate the growing fraud risk,” said Kathleen Peters, Chief Innovation Officer at Experian Decision Analytics in North America. “Experian is committed to continually innovating and bringing solutions to market that help protect consumers and enable businesses to detect and prevent current and future fraud.” To learn more about how to protect your business and customers from rising fraud trends, download the Future of Fraud Forecast and check out Experian’s fraud prevention solutions. Future of Fraud Forecast Press Release

What is elder abuse fraud? Financial abuse is reportedly the fastest-growing form of elder abuse, leaving many Americans vulnerable to theft scams, and putting businesses and other organizations on the frontlines to provide protection and help prevent fraud losses.   Financial elder abuse fraud occurs when someone illegally uses a senior’s money or other property. This can be someone they know, or a third party – like fraudsters who are perpetrating romance scams Older consumers and other vulnerable digital newbies were prime targets for this type of abuse during the start of the pandemic when many of them became active online for the first time or started transacting in new ways. This made them especially attractive targets for social engineering (when a fraudster manipulates a person to divulge confidential or private information) and account takeover fraud. While most of us have become used to life online (in fact, there’s been a 25% increase in online activity since the start of the pandemic), some seniors still have risky habits such as poor password maintenance, that can make them more attractive targets for fraudsters. What is the impact of elder abuse fraud? According to the FBI’s Internet Crime Complaint Center (IC3), elder abuse fraud cost Americans over the age of 60 more than $966 million in 2020. In addition to the direct cost to consumers, elder abuse fraud can leave organizations vulnerable to the fallout from data breaches via account takeover, and lost time and money spent helping seniors and other vulnerable Americans recoup their losses, reset accounts, and more. Further, the victim may associate the fraud with the bank, healthcare provider, or other businesses where the account was taken over and decide to stop utilizing that entity all together. How can organizations prevent elder abuse fraud? Preventing elder abuse fraud can take many forms. Organizations should start with a robust fraud management solution that can help prevent account takeover, first-party, synthetic identity fraud, and more. This platform should also include the ability to use data analysis to detect and flag sudden changes in financial behavior, online activities, and transaction locations that could indicate abuse or takeover of the account. With the right fraud strategy in place, organizations can help prevent fraud and build trust with older generations. Given that 95% of Baby Boomers cite security as the most important aspect of their online experience, this step is too important to miss.   To learn more about how Experian is helping organizations develop and maintain effective fraud and identity solutions, be sure to visit us or request a call. Contact us  

With consumers continuing to take a digital-first approach to everything from shopping to dating and investing, fraudsters are finding new and innovative ways to commit fraud. To help businesses anticipate and prepare for the road ahead, we created the 2022 Future of Fraud Forecast. Here are the fraud trends we expect to see over the coming year: Buy Now, Pay Never: Buy now, pay later lenders will see an uptick in identity theft and synthetic identity fraud. Beware of Cryptocurrency Scams: Fraudsters will set up cryptocurrency accounts to extract, store and funnel stolen funds, such as the billions of stimulus dollars swindled by criminals. Double the Trouble for Ransomware Attacks: Fraudsters will not only ask for a hefty ransom to cede control back to the companies they’ve hacked but also steal and leverage data from the hacked company. Love, Actually?: Romance scams will continue to see an uptick, with fraudsters asking victims for money or loans to cover fabricated travel costs, medical expenses and more. Digital Elder Abuse Will Rise: Older consumers and other vulnerable digital newbies will be hit with social engineering and account takeover fraud. “Businesses and consumers need to be aware of the creativity and agility that fraudsters are using today, especially in our digital-first world,” said Kathleen Peters, Chief Innovation Officer at Experian Decision Analytics in North America. “Experian continues to leverage data and advanced analytics to develop innovative solutions to help businesses prevent fraudulent behavior and protect consumers.” To learn more about how to protect your business and customers from rising fraud trends, download the Future of Fraud Forecast and check out Experian’s fraud prevention solutions. Future of Fraud Forecast Read Press Release

Experian’s Sure Profile was selected as a Platinum winner in the “Fraud and Security Innovation” category in the sixth annual Fintech & Payments awards from Juniper Research, a firm dedicated to delivering thought leadership and analysis in the Fintech and Payment industries.   An innovative service in the fight against synthetic identity fraud, Sure Profile is a comprehensive credit profile that provides a composite history of a consumer’s identification, public record, and credit information in order to detect synthetic identities. It utilizes premium data to help businesses identify potential synthetic fraud threats across credit inquiries, thus allowing lenders to transact more confidently with the vast majority of legitimate consumers.   “Experian has always been a leader in delivering innovative services that both combat fraud and provide identity verification and trust to lending environments. Sure Profile delivers an industry-first fraud offering—integrated directly into the credit profile—that mitigates lender losses while protecting millions of legitimate consumers’ identities,” said Keir Breitenfeld, Senior Vice President, Portfolio Marketing, Experian Decision Analytics. “In times of rapid changes to customer interactions, growth strategies, and risk management practices, it’s particularly important to focus on building tools that can help businesses make better decisions and I’m proud that Experian has again provided an instrument to enable those decisions.”   To learn more about Sure Profile and how Experian is working to solve this multibillion-dollar problem, visit us or request a call. Learn more

Earlier this year, we shared our predictions for five fraud threats facing businesses in 2021. Now that we’ve reached the midpoint of the year and economic recovery is underway, we’re taking another look at how these threats can impact businesses and consumers.   Putting a Face to Frankenstein IDs: Synthetic identity fraudsters will attempt to bypass fraud detection methods by using AI to combine facial characteristics from different people to form a new identity. Overexposure: As many as 80% of SSNs may have been exposed on the dark web, creating opportunities for account application fraud. The Heist: Surges in data breaches, advances in automation, expanded online banking services and vulnerabilities exposed from social engineering mistakes have lead to rises in account takeover fraud. Overstimulated: Opportunistic fraudsters may take advantage of ongoing relief payments by using stolen data from consumers. Behind the Times: Businesses with lackluster fraud prevention tools and insufficient online security technology will likely experience more attacks and suffer larger losses.   To learn more about upcoming fraud threats and how to protect your business, download our new infographic and check out Experian’s fraud prevention solutions. Download infographic Request a call

In today’s digital-first environment, fraud threats are growing in sophistication and scope. It’s critical for credit unions to not only understand the specific threats presented by life online, but to also be prepared with a solid fraud detection and prevention plan. Below, we’ve outlined a few fraud trends that credit unions should be aware of and prepared to address. 2021 Trends to Watch: Digitization and the Movement to Life Online Trend #1: Digital Acceleration As we look ahead to the rest of 2021 and beyond, we expect to see adoption of digital strategies nearing the top of credit unions’ list of priorities. Members’ expectations for their digital experience have permanently shifted, and many credit unions now have members using online channels who traditionally wouldn’t have. This has led to a change in the types of fraud we see as online activities increased in volume. Trend #2: First-Party Fraud is On the Rise First party fraud is on the rise – 43% of financial executives say that mule activity is up 10% or more compared to attack rates prior to the pandemic, according to Trace Fooshee, Senior Analyst for Aite Group, and we expect to see this number grow. The ability for credit unions to identify and segregate the “good guys” from “bad guys” is getting more difficult to discern and this detail is more important than ever as credit unions work to create frictionless digital experiences by using digital tools and strategies. Trend #3: Continual Uptick in Synthetic Identity Fraud We expect synthetic identity fraud (SID) to continue to rise in 2021 as cybercriminals become more sophisticated in the digital space and as members continue with their new digital habits. Additionally, fraudsters can use SIDs to bring significant damage and loss to credit unions through fraudulent checks, debit cards, person-to-person and automated clearing house (ACH) transactions. More and more, fraudsters are seen opening accounts and remaining very patient – using an account to build and nurture a trusted relationship with the credit union and then remain dormant for two years before ensuing in any sort of abuse. Once the fraudster feels confident that they can bypass authentication processes or avoid a new product vetting, oftentimes, they will take that opportunity to get easy access to all solutions credit unions have available and will abuse them all at once. There are no signs of fraud slowing, so credit unions will need to stay vigilant in their fraud protection and prevention plans. We’ve outlined a few tips for credit unions to help protect member data while reducing risk. The Fight Against Fraud: Four Key Tips Tip #1: Manage Each Fraud Type Appropriately Preventing and detecting fraud requires a multi-level solution. This can involve new methods for authenticating current and prospective members, as well as incorporating synthetic identity services and identity proofing throughout the member lifecycle. For example, credit unions should consider taking extra verification steps during the account opening process as a preventative measure to minimize SID infiltration and associated fraud losses. As credit unions continue down the path of digitization, it’s also important to add in digital signals and behavior-based verification, such as information about the device a consumer is logging in from to heighten defenses against bad actors. Tip #2: Be Resourceful In the wake of the COVID-19 pandemic, many have asked, “How should credit unions approach fraud prevention tactics when in-person contact is limited or unavailable?” In some cases, you might need to be willing to say no to requests or get creative and find other options. Sometimes, it takes leveraging current resources and using what’s readily available to allow for a binary decision tree. For example, if you’re suspicious of a dormant account that you think could be synthetic, call them, and ask yourself these questions: Did they answer? Was the phone still active? Send the account holder an email – did you get a reply? Is this a new member? Is this a new channel for the member? Could they have logged on to do this instead of calling the call center? Tip #3: Empower Members Through Education Members like to know that their credit unions are taking the necessary steps and applying the right measures to keep their data secure. While members might not want every detail, they do want to know that the security measures are there. Require the use of strong passwords, step-up authentication, and empower members with alerts, notifications, and card controls. Additionally, protect members by providing resources like trainings, webinars, and best practices articles, where they can learn about current cyber trends and how to protect their data. Tip #4: Trust Data Many credit unions rely on an employee’s decision to decide when to take action and what action to take. The challenge with this approach comes when the credit union needs to reduce friction for members or tighten controls to prevent fraud, because it’s extremely hard to know exactly what drove prior actions. A better alternative is to rely on scores and specific data. Tweaks to the scores or data points that drive actions allow credit unions to achieve the desired member experience and risk tolerance – just be sure to leverage internal experts help figure out those policies. By determining what conditions drive actions before the actions are taken (instead of doing it one case at a time) the decisions remain transparent and actionable. Looking for more insights around how to best position your credit union to mitigate and prevent fraud? Watch our webinar featuring experts from around the industry and key credit unions in this Fraud Insight Form hosted by CUES. Watch now Contact us