{"id":1370,"date":"2026-09-03T16:30:30","date_gmt":"2026-09-03T16:30:30","guid":{"rendered":"https:\/\/www.spokeo.com\/pathfinder\/?p=1370"},"modified":"2026-08-27T16:38:21","modified_gmt":"2026-08-27T16:38:21","slug":"synthetic-fraud-solutions-document-fraud-and-more","status":"publish","type":"post","link":"https:\/\/www.spokeo.com\/pathfinder\/synthetic-fraud-solutions-document-fraud-and-more\/","title":{"rendered":"Time for a New Fraud Risk Stack? Better Data Is Key to Detecting Synthetic Fraud"},"content":{"rendered":"\n

Non-traditional lenders win in the consumer marketplace by promising \u2014 and delivering on \u2014 fast approvals, low-friction convenience, and a willingness to do business with people who\u2019ve been marginalized by the conventional banking industry. Yet those very strengths also mark a weakness: alternative lenders and \u201cbuy now, pay later\u201d (BNPL) providers<\/a> are heavily exposed to potential fraud and misrepresentation. <\/p>\n\n\n\n

As your business scales, professional criminals and opportunistic amateurs won\u2019t miss the opportunity to exploit any weakness in your processes, making fraud risk\u2014including synthetic fraud, credit manipulation, and document fraud\u2014a major operational vulnerability. Here we explore how lenders can construct a new authentication risk stack to flag bad actors\u2014without adding friction to the process for legitimate customers. <\/p>\n\n\n\n

Fraud Risk Presents Differently in Alt-Lending and BNPL<\/h2>\n\n\n\n

If the alternative-lending industry had a collective motto, it might (to paraphrase Mark Zuckerberg) be \u201cmove fast and approve things.\u201d Offering fast, seamless convenience is core to the alt-lending business model, which is a key distinction from traditional banking. For banks and credit unions, quick approvals are a positive when they occur, but they\u2019re not essential. <\/p>\n\n\n\n

This means that fraud risk presents differently for non-bank lenders.. Fraudsters targeting banks know they\u2019ll face a relatively high level of scrutiny and a background process check that\u2019s shaped by compliance with decades of KYC legislation. The fast and streamlined onboarding processes adopted by many non-bank financial institutions, on the other hand, don\u2019t have room for that kind of deep dive<\/a>. So, while impersonation looms large for both traditional and non-traditional lenders, circumvention of onboarding controls is a bigger issue for alt-lenders<\/a>.<\/p>\n\n\n\n

The end result is that bad actors don\u2019t need to invest as much time and effort in constructing a fraudulent persona if they\u2019re targeting an alternative lender. It won\u2019t need to stand up to as much scrutiny, so they can afford to cut corners. This status quo means scammers can generate plausible synthetic fraud profiles at scale. But it also means they\u2019re relatively easy to identify if \u2014 and it\u2019s an important \u201cif\u201d \u2014 you have access to a suitable tool for recognizing inconsistencies in those profiles and investigating them further. <\/p>\n\n\n\n

Legacy Fraud Detection Tools May Not Meet Modern Lending Needs<\/h2>\n\n\n\n

Many fraud detection tools used by lenders were originally designed around traditional financial services infrastructure. Credit bureaus, banks, and other established institutions rely heavily on shared reporting systems and long-established data ecosystems.<\/p>\n\n\n\n

Alternative lenders often operate in faster approval environments and may require additional data context during fraud reviews. Traditional datasets may not always provide the level of timeliness or contextual signals needed to investigate unusual application patterns efficiently.<\/p>\n\n\n\n

The alt-lending model is built partially on the assumption of the higher risk level that results from working with these types of customers and planning accordingly. But deliberate fraud, often powered by synthetic fraud schemes, can quickly derail your risk models. And differentiating between legitimate applicants with limited credit history and fraudulent application activity may require additional data signals and investigative tools. Additional public-record and contextual data sources can help fraud investigation teams review flagged applications more efficiently alongside their existing compliance and verification processes.<\/p>\n\n\n\n

The Challenge of Synthetic Fraud Profiles<\/h3>\n\n\n\n

Synthetic fraud schemes often combine real and fabricated data elements to create fraudulent borrower profiles. The recent maturity of consumer-ready AI tools has made it trivially easy for crime rings to generate synthetic fraud profiles cheaply and at scale<\/a>. With few meaningful limits on criminals\u2019 ability to generate these fraudulent profiles, it\u2019s likely that their presence in your customer acquisition funnel is both large and growing.  <\/p>\n\n\n\n

To counter that, you\u2019ll need stronger fraud detection tools and data signals. Namely, you\u2019ll need tools and data partners that can provide additional context around application data and related public-record signals, highlighting the inconsistencies inherent in synthetic fraud schemes in as close to real time as possible.<\/p>\n\n\n\n

\"hands<\/figure>\n\n\n\n

Building a Modern Fraud Risk Stack<\/h2>\n\n\n\n

This need isn\u2019t limited to synthetic fraud detection, by any means. Traditional impersonation fraud and account takeovers, document fraud, and AI-fueled deepfake workarounds for biometrics and other authentication methods<\/a> all pose challenges to existing risk-management tools. <\/p>\n\n\n\n

Superficially, these challenges may seem like a \u201cperfect storm\u201d of fraud risk from the lender\u2019s perspective. But they all have vulnerabilities and flaws that can be exposed with the right tools. A fraud risk stack built from agile, real-time tools, interacting through powerful application programming interfaces (APIs), can expose them by subjecting them to closer scrutiny than they\u2019re meant to withstand. <\/p>\n\n\n\n

The Importance of Accurate, Real-Time People Data <\/h3>\n\n\n\n

A modern fraud risk stack contains multiple components, beginning with the machine-learning tools many lenders use to detect and flag anomalies. The rest of the stack, however constructed, should help teams investigate those anomalies as quickly and efficiently as possible.<\/p>\n\n\n\n

As outlined above, legacy data sources are not always well-suited to providing the breadth and timeliness of contextual data that modern fraud investigations may require. Using Spokeo\u2019s API, application data can be cross-referenced against a wide range of public and commercially sourced records. This added context can help fraud investigation teams review inconsistencies or unusual patterns in flagged transactions more efficiently.<\/p>\n\n\n\n

With faster access to broader contextual data, teams can incorporate additional fraud-detection signals into their review workflows at higher volume while minimizing unnecessary friction for legitimate customers.<\/p>\n\n\n\n

From Need Assessment to Implementation<\/h2>\n\n\n\n

The generative AI tools fueling this new generation of fraud are constantly evolving and improving, adding to pressure on lenders. Constructing a modernized fraud risk stack is the most directly impactful way of countering the threat. <\/p>\n\n\n\n

Spokeo for Business<\/a> is designed to help lenders do exactly that. Our solution brings a broad range of contextual data signals into fraud investigation workflows, providing additional public record and contextual data signals that can support fraud investigation workflows, all while ensuring a positive user experience for legitimate borrowers. <\/p>\n\n\n\n

Reach out to explore how our solutions can fit into your workflow, and learn how Spokeo for Business can support your enterprise as you scale.  <\/p>\n\n\n\n

Spokeo for Business provides access to public record and commercially sourced data that may support fraud investigation and data validation workflows. It is not a consumer reporting agency as defined by the Fair Credit Reporting Act (FCRA) and does not provide consumer reports. Customers are responsible for ensuring their use of the data complies with all applicable laws and regulations.<\/em><\/p>\n\n\n\n

Sources<\/p>\n\n\n\n

Financial Crimes Enforcement Network (FinCEN): FinCen Issues Analysis of Identity-Related Suspicious Activity<\/a><\/p>\n\n\n\n

Mastercard: What is Synthetic Identity Fraud and How Does Synthetic Identity Theft Work?<\/a><\/p>\n\n\n\n

The Wall Street Journal: Deepfakes Are Coming for the Financial Sector<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Alt-lenders are uniquely exposed to fraud risk. Learn how to integrate synthetic fraud solutions and more into an improved risk stack.<\/p>\n","protected":false},"author":7,"featured_media":1372,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[38,40],"yoast_head":"\nAlt-lenders Need Synthetic Fraud Solutions, and More | Spokeo<\/title>\n<meta name=\"description\" content=\"Alt-lenders are uniquely exposed to fraud risk. 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