Let\u2019s look at some specific examples of how scammers use synthetic\/stolen identities or manipulated data across multiple industries. <\/p>\n\n\n\n
Fraudulent Insurance Claims<\/h3>\n\n\n\n
Insurance scams lean heavily on identity theft \u2014 and are on the rise. The National Insurance Crime Bureau estimated a 49% rise in identity-related scams in 2025<\/a>. A representative handful of common scams includes:\u00a0<\/p>\n\n\n\n Travel fraud, unlike some of the other schemes on this list, tends to have two victims: the person whose credit card information has been stolen and the travel operator on the receiving end of the booking. Catching these types of suspicious transactions before <\/em>travel occurs is key to preventing lost revenue. <\/p>\n\n\n\n Here are two of the most common schemes:<\/p>\n\n\n\n In the most common form of logistics fraud, criminals use stolen identities to pose as legitimate logistics firms or contracted truckers and arrange false pickups of cargo. The scammers load a truck with high-value cargo at the dock, then disappear with their ill-gotten gains. <\/p>\n\n\n\n On the flipside, scammers will also take advantage of the fact that repairs to cargo trucks need to be taken care of as quickly as possible in order to ensure logistics companies can meet deadlines and deliver loads on time. In this scheme, they\u2019ll pose as drivers or repair shops, claiming that they need money or payment to cover a necessary repair. The logistics company will send over payment via credit card or e-check without a second thought, only to later find out a repair was never needed in the first place.\u00a0\u00a0<\/p>\n\n\n\n While PayPal enjoys widespread commercial use as a payment processor, it\u2019s relatively rare for companies to accept P2P payment apps like Venmo, Zelle, or CashApp, because they\u2019re complicated to administer and not especially cost-effective for the vendor<\/a>. But identity fraud that originates in P2P systems can, and does, spill into merchant disputes, chargebacks, and investigations.\u00a0<\/p>\n\n\n\n In this scheme, the criminal gains access to a consumer\u2019s account through identity theft or account takeover and uses it to make high-value purchases through the peer-to-peer account. When the fraud is discovered, the vendor is on the hook for the cost of the goods and services provided, pending the outcome of a reimbursement claim. <\/p>\n\n\n\n The main aim of these scams is to make off with both the product and <\/em>a refund, resulting in frustrating losses for merchants. Two of the most common are: <\/p>\n\n\n\n Claim fraud detection is a thorny problem, most notably when synthetic identities are in play. Ideally, it would be best to identify potential fraud before the claim is completed, but this isn\u2019t always practical. Next best is at \u201cfirst notice of loss\u201d (FNOL), when the customer (real or purported) files the claim. <\/p>\n\n\n\n Once a reimbursement is issued or a claim is paid, your revenue protection team\u2019s only option is to chase down the fraud after the fact, in an effort to recover your money and\/or generate enough evidence to potentially lead to charges being laid. This is especially problematic when the criminals have used a synthetic identity, because those are difficult even for law enforcement agencies<\/a> to pick apart using legacy data tools. Synthetic identities rarely fail because of one incorrect detail; they fail because their information doesn\u2019t hold together over time. Names, phone numbers, email addresses, addresses, and online presence are often assembled from multiple sources, creating personas that look plausible in isolation but fall apart when examined holistically.<\/p>\n\n\n\n This is where modern people data platforms like Spokeo for Business<\/a> provide unique value. By aggregating and cross-referencing signals across multiple data points and historical records, Spokeo helps claims and fraud teams assess whether a claimant\u2019s information matches those signals \u2014 or shows signs of fabrication and manipulation.<\/p>\n\n\n\n Instead of slowing claims with manual reviews, people data enables faster, more confident decisions by revealing inconsistencies early in the process.<\/p>\n\n\n\n There are many tools that companies can use to automatically flag and suspend suspicious-looking transactions as they\u2019re occurring. This should be the first line of defense for any business offering self-serve purchasing, but it does not always go far enough\u2014especially once a transaction has been flagged for review by automated tools. <\/p>\n\n\n\n For example, Spokeo searches can quickly help establish whether the transaction originates from phone numbers or email addresses that are consistent with the account-holder\u2019s information. This can be especially helpful for investigating the types of travel and e-commerce merchant fraud discussed above, as the contact information signals can help determine if there is a sufficient-enough match to allow a transaction to proceed forward or move to a higher level of manual review. <\/p>\n\n\n\n First notice of loss is often the most critical and vulnerable moment in the claims process. At this stage, fraud teams are asked to evaluate documentation, images, and personal details that may be incomplete, manipulated, or entirely fabricated. <\/p>\n\n\n\n Here are a few ways to empower teams to act quickly once a red flag is raised:\u2026<\/p>\n\n\n\n Incorporating Spokeo for Business into your investigative process for claims or refunds places an additional powerful fraud detection tool at your disposal. Aside from verifying photos, you might also opt for additional checks, such as: <\/p>\n\n\n\n After any money has been disbursed on a fraudulent claim or refund request, or merchandise has been trucked away from your warehouse, recovery and potential prosecution become priorities. Law enforcement agencies are chronically overworked, so any information you can uncover about the persons behind the fraud makes it more likely an arrest or settlement will result. <\/p>\n\n\n\n Helpful steps you could take include: <\/p>\n\n\n\n As digital claims processes accelerate, fraud has evolved to exploit identity gaps rather than policy loopholes. Documents, images, and even video evidence can now be fabricated at scale, making it increasingly difficult to separate legitimate claims from fraudulent ones using traditional verification methods alone.<\/p>\n\n\n\n Organizations that succeed in this environment are those that augment existing fraud controls with identity intelligence, adding context, continuity, and confidence to every decision.<\/p>\n\n\n\n Spokeo for Business<\/a> helps insurance and claims-driven organizations do exactly that by bringing people data into the fraud prevention workflow, supporting faster investigations, fewer fraudulent payouts, and better outcomes for legitimate customers.<\/p>\n\n\n\n Reach out to explore our solutions and learn how Spokeo for Business can support your claims and fraud teams.<\/p>\n\n\n\n <\/p>\n\n\n\n Sources<\/strong><\/p>\n\n\n\n Shopify User Blog: Frictionless Experience: How to Create Smooth User Flows<\/a><\/p>\n\n\n\n National Insurance Crime Board: NICB Projects 49% Rise in Insurance Fraud Linked to Identity Theft in 2025<\/a><\/p>\n\n\n\n The Federal Reserve: Synthetic Identity Fraud Defined<\/a><\/p>\n\n\n\n Mozilla Foundation \u201cDistilled\u201d Blog: What Is a SIM Swapping Scam and How Can You Protect Yourself?<\/a><\/p>\n\n\n\n Life Insurance Attorney: Life Insurance Denials in Digital Identity Theft Cases<\/a><\/p>\n\n\n\n Insurance Journal: Generative AI: A Double Agent Serving Good and Evil In the World of Claims<\/a><\/p>\n\n\n\n ABTA: The Five Most Common Types of Travel Fraud<\/a>\u00a0<\/p>\n\n\n\n\n
Travel Fraud Claims<\/h3>\n\n\n\n
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Logistics Fraud<\/h3>\n\n\n\n
Peer-to-peer Platform Fraud<\/h3>\n\n\n\n
Marketplace and E-commerce Disputes<\/h3>\n\n\n\n
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![\"two](\"https:\/\/www.spokeo.com\/pathfinder\/wp-content\/uploads\/2026\/03\/pexels-kampus-8204367-1024x684.jpg\")
<\/figure>\n\n\n\nWhy Better Data is Key to Efficient Claim Fraud Detection<\/h2>\n\n\n\n
During a Transaction<\/h3>\n\n\n\n
At First Notice of Loss<\/h3>\n\n\n\n
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During the Investigative Process<\/h3>\n\n\n\n
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After the Claim or Reimbursement is Paid Out<\/h3>\n\n\n\n
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Where Digital Identity Fraud Meets New Data Tools<\/h2>\n\n\n\n