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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With an ever-present need for efficiency, security, and seamless citizen services, many agencies are looking at the benefits of a data-driven government. Last year, the federal government kicked off a unified effort to enable data-driven decision making. The goal at that level – and across all agencies – is to serve citizens more efficiently and effectively. By embracing the power of data and analytics, agencies of all sizes can set themselves up to better serve their citizens. What is a data-driven government? Agencies collect citizen data from a variety of service-based sources, including the Postal Service, Census Bureau, social welfare departments, and agencies that issue government IDs. When properly leveraged, this data holds many possibilities. However, many agencies face challenges when it comes to efficient collection, sharing, usage, integrity, and accessibility. Due to the amount of data collected and the potential lack of consistency in the collection and storage techniques, the data may not be usable. Without proper management and analysis, there’s little government agencies can do with their data to improve their processes. A data-driven government has well-managed data and uses that data to drive their decisions as they relate to citizen requests for benefits, tax collection, elections, and more. What are the benefits of data-driven decision making? Data management and government data analytics enable agencies to react quickly to citizen demands and concerns and proactively anticipate an issue before it becomes a crisis. With the right tools, agencies can gain a holistic view of their citizens, communicate effectively internally, provide digitally-driven services and improve overall efficiency through government-wide data integration and management. These changes have a wide range of benefits, including reduction of cost, fraud, waste and abuse, the automation of manual processes, and better service delivery. Why is a data-driven strategy required? In addition to the benefits listed above, a data-driven strategy also helps agencies align with published NIST guidelines and the need to monitor, evaluate, and maintain digital identity systems. Proper use of data-driven digital identity strategies will enhance equity and the usability of the solutions agencies provide to their citizens. Building an effective data-driven strategy The right strategy starts with ensuring that all departments about the need for proper data management and analytics and the guidelines that will govern it, such as maintaining up-to-date data, removing silos, and leveraging the right tools. The next step is finding the right partner. An effective partner can help agencies develop and maintain data management systems and implement the right tools and analytics – things like machine learning in government – to help each agency function efficiently and safeguard the data of its citizens. To learn how Experian can help your agency improve its use of data, visit us or request a call. Visit us

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