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Authorized Push Payment fraud, also known as APP fraud or APP scams, involves a fraudster persuading a victim to willingly deposit funds to their account or to the account of a complicit third party, also known as a money mule. This type of fraud often includes social engineering of the victim using fake investment schemes, impersonation scams, purchase scams or other schemes. Social engineering clouds victims' judgments and encourages them to make payments willingly to one or more money mules, with funds eventually reaching fraudsters' accounts. This type of fraud has become more attractive to criminals since the advent of real-time payment systems, which are now a reality worldwide. Fraud fueled by real-time payments Authorized push payment fraud is becoming more prevalent, and it is imperative that you know how to detect and prevent it to safeguard your organization. Real-time payment systems, such as Faster Payments in the United Kingdom (UK), PIX in Brazil, the New Payments Platform in Australia, and FedNow in the USA, make real-time payment fraud a reality.  APP fraud is notoriously difficult for banks to prevent because the victim is sending the money themselves, and steps that banks take to authenticate customers are ineffective, as the customer will pass identity checks. The victims cannot reverse a payment once they realize they have been conned, as payments made using real-time payment schemes are irrevocable. APP fraud is particularly prevalent in countries where banks have an infrastructure that facilitates fast or immediate transfers, like the UK. Learn more about the new UK legislation around APP fraud Reimbursment is vital to victims Some common types of authorized push payment fraud include attacks on individuals like romance scams, family emergency swindles, targeting property transactions, and intercepting supplier payments. To protect against APP fraud, it is important to employ layered fraud protection across all products and channels used to manage real-time payments. But that alone is not enough. Reimbursement is vital in reversing the financial distress caused by APP scams, but it cannot reverse the emotional distress these scams cause. Prevention, detection, and awareness measures must be moved up on the agenda for banks, non-traditional lenders, PSPs (Payment Service Providers), and customers alike to ensure that the customer is protected at every stage of the payment journey.  Effective alerts are a key focus area for preventing customers from falling victim to APP scams. An effective warning is one that is dynamic and tailored to the customer’s payment journey. Recent research indicates that minor changes to notifications across banking apps can have the potential to drastically reduce the number of individuals that fall victim to APP fraud. The biggest effects were achieved when a combination of risk-based and Call to Action (CTA) warnings were implemented over a period of time. A collective effort across the banking industry and beyond is crucial to protect customers and tackle the fight against APP fraud. Banks, non-traditional lenders, and PSPs can raise awareness to educate their customers on the signs and risks of APP scams, and work with industry oversight bodies to commit to voluntary standards and codes to ensure good customer outcomes. Online forums, social media platforms, and influential voices also have a role to play in raising awareness of and preventing scams. Customers can also help by being vigilant and reading and acting upon warnings and information presented to them.  Authorized push payment fraud prevention To effectively combat authorized push payment fraud, financial institutions must implement a range of measures, including:  Direct communication with consumers.  Enhanced transaction monitoring.  Effective risk mitigation and management.  Improved employee education.  Public awareness campaigns.   In response to this growing threat, banks have introduced various checks and balances, such as the Confirmation of Payee (CoP) service in the UK, which cross-references bank details with the account holder's name when processing online payments.  Banks are also leveraging sophisticated fraud prevention software stacks, incorporating machine learning and contextual data to identify and flag suspicious transactions. By utilizing AI technologies, financial institutions can process  data points faster and enhance their fraud detection capabilities, mitigating identity risk and safeguarding customer accounts. Clear communication with customers is essential in the fight against APP fraud. Higher-risk companies now include warnings in their communications, advising customers not to act on messages that request payment into new bank accounts.  Financial institutions can also offer cool-off periods before payments are sent, increase due diligence around payment destinations, and monitor accounts that regularly receive high-value payments. Additionally, financial institutions can play a crucial role in educating their customers and promoting awareness around this increasingly common type of fraud. By combining these approaches with robust fraud prevention software, the public can fight against this type of fraudulent attack.  Taking the next steps with the right partner At Experian, we offer rich data sources, advanced analytics capabilities, and the consultancy services needed to rapidly adopt data analytics solutions that mitigate fraud risks. Our solutions are used by PSPs of all types and sizes – including some of the largest banks – to identify potentially fraudulent customers and transactions, and to ensure that action is taken in real time to prevent fraudulent payments being made.  Learn more about our fraud management solutions *This article leverages/includes content created by an AI language model and is intended to provide general information.

Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

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.