Tag: AI fraud

As fraud continues to rise in the rental housing market, tenant screening practices are evolving. In an earlier blog, I explored how Experian Observed DataTM can provide early indicators of income and employment consistency, offering screening companies a way to reduce reliance on costly or time-intensive verification methods.  In this follow-up, I explore two additional tools that strengthen the tenant screening process: Experian VerifyTM for Research Verifications and Experian Verify for Permissioned Verification's AI-powered Document Review. Used together, these solutions enable a layered approach that boosts both efficiency and prevention of fraud. Modernized Research Verifications Manual employment and income and employment checks—once the standard for tenant screening—are time-consuming and often inconsistent. Traditionally, screening companies had to reach out directly to employers and request proof of employment. While still useful, this method puts pressure on internal resources and is not always scalable.  To streamline manual verification, many organizations are partnering with third-party providers, especially those that take a digital-first approach. Outsourcing allows screening companies to delegate outreach, follow-ups, and fraud detection to specialized teams trained in document validation and employer communication. These services deliver the same insights internal teams would gather, while freeing up in-house resources for more strategic initiatives. By leveraging digital tools such as conversational AI, online forms, and automated workflows—combined with human oversight—digital-first vendors offer a more scalable and cost-effective alternative to fully manual processes. This approach not only reduces operational costs but also shortens turnaround times, helping screening companies respond faster without compromising accuracy or fraud resistance. Key advantages:[MJ1]  Reduces the burden on internal staff Ensures consistency and fraud awareness in document review Provides a reliable fallback when other verification tools return limited data This approach is especially valuable when initial data sources yield incomplete results and further confirmation is required. AI-Enhanced Document Upload and Review Another common scenario in tenant screening is the submission of income documents by the applicant, often in the form of paystubs or bank statements. Manual review of these documents is prone to error and increasingly vulnerable to sophisticated forgeries, including those generated by artificial intelligence. AI-powered document analysis tools are now helping screening companies process uploaded documents more securely and efficiently. These platforms typically work by: Allowing applicants to upload documents through a secure portal Using AI to scan for signs of tampering, fabrication, or inconsistency Returning standardized results that are easier to evaluate and compare By automating the detection of anomalies and potential fraud indicators, these tools reduce the workload for staff while improving the reliability of the review process. Benefits include: Faster review and turnaround times Improved fraud detection capabilities Greater consistency across applicants This method is especially useful when traditional employer APIs are unavailable or when screening companies need additional confirmation beyond initial data sources. A Layered Approach to Verification By combining different verification methods, screening companies can design workflows that adapt to a wide range of applicant profiles and risk scenarios. A layered strategy might include: Starting with an inexpensive source of income or employment data to identify likely matches Using AI-based document review when additional validation is needed Turning to manual research verifications only when necessary This cascading process allows screening companies to control costs while maintaining a strong defense against fraud. It also ensures that higher-cost methods are used only when the earlier steps do not provide enough confidence to proceed. Modern Challenges Require Modern Solutions Fraud in tenant screening is increasing rapidly. According to industry surveys, over 93 percent of screening companies have encountered fraud in the past year, and the majority have dealt with falsified income documentation. Traditional approaches, especially manual review, are no longer sufficient on their own. By rethinking verification strategies and incorporating modern tools like outsourced research verification and AI-enhanced document review, screening companies can reduce risk, improve efficiency, and better prioritize their resources. Learn More For organizations interested in implementing these types of verification tools, several providers—including Experian—offer services designed to support this layered approach. These solutions can help screening companies strike the right balance between cost, compliance, and fraud resistance. To learn more, visit experian.com/verify.

In this article...Rise of AI in fraudulent activitiesFighting AI with AI Addressing fraud threatsBenefits of leveraging AI fraud detectionFinancial services use caseExperian's AI fraud detection solutions In a world where technology evolves at lightning speed, fraudsters are becoming more sophisticated in their methods, leveraging advancements in artificial intelligence (AI). According to our 2025 U.S. Identity and Fraud Report, 72% of businesses expect AI fraud to be among their top challenges by 2026. To combat emerging fraud threats, organizations are turning to AI fraud detection to stay ahead and protect their businesses and their customers, essentially fighting AI with AI. This blog post explores the evolving AI fraud and AI fraud detection landscape. The rise of AI in fraudulent activities Technology is a double-edged sword. While it brings numerous advancements, it also provides fraudsters with new tools to exploit. AI is no exception. Here are some ways fraudsters are utilizing AI: Automated bot attacks: Fraudsters employ AI to design automated scripts that launch large-scale attacks on systems. These scripts can perform credential stuffing, where stolen usernames and passwords are automatically tested across multiple sites to gain unauthorized access. Deepfakes and synthetic identities: Deepfake technology and the creation of synthetic identities are becoming more prevalent. Fraudsters use AI to manipulate videos and audio, making it possible to impersonate individuals convincingly. Similarly, synthetic identities blend real and fake information to create false personas. Phishing and social engineering: AI-driven phishing attacks are more personalized and convincing than traditional methods. By analyzing social media profiles and other online data, fraudsters craft tailored messages that trick individuals into revealing sensitive information. Watch now: Our 2025 Fraud Trends webinar explores how businesses can navigate rising risk, meet growing consumer expectations, and stay ahead of increasingly complex attacks. Fighting AI with AI in fraud detection To combat these sophisticated threats, businesses must adopt equally advanced measures. AI fraud detection offers a robust solution: Machine learning algorithms: Fraud detection machine learning algorithms analyze vast datasets to identify patterns and anomalies that indicate fraudulent behavior. These algorithms can continuously learn and adapt, improving their accuracy over time. Real-time monitoring: AI systems provide real-time monitoring of transactions and activities. This allows businesses to detect and respond to fraud attempts instantly, minimizing potential damage. Predictive analytics: Predictive analytics uses historical data to forecast future fraud trends. By anticipating potential threats, organizations can take proactive measures to safeguard their assets. Addressing fraud threats with AI fraud detection AI's versatility allows it to tackle various types of fraud effectively: Identity theft: According to our research, 68% of consumers rank identity theft as their top online concern. AI systems can help safeguard consumers by cross-referencing multiple data points to verify identities. They can spot inconsistencies that indicate identity theft, such as mismatched addresses or unusual login locations. Payment fraud: Coming in second to identity theft, 61% of consumers rank stolen credit card information as their top online concern. Payment fraud includes unauthorized credit card transactions and chargebacks. AI can be used in payment fraud detection to surface unusual spending patterns and flag suspicious transactions for further investigation. Account takeover: Account takeover fraud, one of the top-most encountered fraud events reported by U.S. businesses, occurs when fraudsters gain access to user accounts and conduct unauthorized activities.* AI identifies unusual login behaviors and implements additional security measures to prevent account breaches. Synthetic identity fraud: Synthetic identity fraud involves the creation of fake identities using real and fabricated information. AI fraud solutions detect these false identities by analyzing data inconsistencies and behavioral patterns. Benefits of leveraging AI fraud detection Implementing AI fraud detection offers numerous advantages: Enhanced accuracy: AI systems are highly accurate in identifying fraudulent activities. Their ability to analyze large datasets and detect subtle anomalies surpasses traditional methods. Cost savings: By preventing fraud losses, AI systems save businesses significant amounts of money. They also reduce the need for manual investigations, freeing up resources for other tasks. Improved customer experience: AI fraud detection minimizes false positives, ensuring genuine customers face minimal friction. This enhances the overall customer experience and builds trust in the organization. Scalability: AI systems can handle large volumes of data, making them suitable for organizations of all sizes. Whether you're a small business or a large enterprise, AI can scale to meet your needs. Financial services use case The financial sector is particularly vulnerable to fraud, making AI an invaluable tool for fraud detection in banking. Protecting transactions: Banks use AI to monitor transactions for signs of fraud. Machine learning algorithms analyze transaction data in real time, flagging suspicious activities for further review. Enhancing security: AI enhances security by implementing multifactor authentication and behavioral analytics. These measures make it more challenging for fraudsters to gain unauthorized access. Reducing fraud losses: By detecting and preventing fraudulent activities, AI helps banks reduce their fraud losses throughout the customer lifecycle. This not only saves money but also protects the institution's reputation. Experian's AI fraud detection solutions AI fraud detection is revolutionizing the way organizations combat fraud. Its ability to analyze vast amounts of data, detect anomalies, and adapt to new threats makes it an essential element of any comprehensive fraud strategy. Experian’s range of AI fraud detection solutions help organizations enhance their security measures, reduce fraud losses, authenticate identity with confidence, and improve the overall customer experience. If you're interested in learning more about how AI can protect your business, explore our fraud management solutions or contact us today. Learn More * Source: 2025 U.S. Identity and Fraud Report This article includes content created by an AI language model and is intended to provide general information.