When a customer says something untrue, the right response depends on what kind of untruth it is. A polite overstatement in a survey, a claim made to win a refund, and a dispute where the seller lacks the facts are different problems, and treating them as one leads to bad decisions. No reliable study establishes how often customers lie in general, so this article does not offer a percentage. It explains what published research and practitioner evidence do show about feedback, automated channels, refund claims, and disputes, and how a small seller or service business can respond proportionately.
Three situations that get called “lying”
The phrase covers at least three behaviors that differ in intent, in evidence, and in what a business can reasonably do about them.
| Situation | What it looks like | Typical response |
|---|---|---|
| Socially softened feedback | A customer rates a service higher than they privately feel, often face to face, to avoid an awkward moment. | Read the score with caution. Do not treat it as a deliberate deception or act against the customer. |
| Exaggeration for a financial benefit | A customer claims a full-price refund on a sale item, says a receipt was lost, or makes a claim when staff are visibly busy. | Apply the written policy consistently and ask for the records it requires. |
| Disputed claim with incomplete facts | An item is returned opened or damaged, and the seller and buyer describe what happened differently. | Gather what evidence exists, weigh the amount at stake, and decide without assuming bad faith. |
Keeping these apart matters. A customer who inflates a rating out of politeness has not defrauded anyone, and a customer who invents a lost receipt has. A seller who handles both with the same suspicion will misjudge the first and create friction with honest buyers.
What the feedback research shows
Christine Ringler’s 2021 paper “Truth and lies: The impact of modality on customer feedback,” published in the Journal of Business Research (volume 133, September 2021, pp. 376–387), reports five studies on how the channel for giving feedback changes what customers say. Her paper defines lying, citing DePaulo and colleagues, as “intentionally trying to mislead someone.”
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The central finding is about timing. Dynamic channels, such as face-to-face feedback, produced more inflated customer attitudes than static channels, such as a written form, when the customer formed the attitude during the interaction itself. When customers already held an attitude before responding, the channel made no comparable difference. The paper’s abstract says marketers should not automatically discard inflated feedback, because an on-the-spot attitude may persist after the conversation ends.
The practical lesson is narrower than “face-to-face feedback is unreliable.” If your feedback is collected in a live conversation about a product the customer has just encountered for the first time, the score may reflect the moment as much as the experience. Written feedback from customers who have used a product for weeks may be less inflated for that reason. The study does not show that each positive answer is a deliberate lie.
Does the channel change honesty?
A University of Michigan School of Information story dated May 10, 2022, summarizes research in which participants flipped a coin and reported the result either to a person or to a machine. Reporting was self-reported and the experiment rewarded reporting success, so cheating could be measured as a gap between what was reported and what chance would produce.
In that experiment, 9% of participants reporting to a person cheated, compared with 22% reporting to a bot. Blatant cheating, defined there as reporting nine or ten successful flips, was more than three times as common when participants reported to a machine. These are results from a laboratory-style coin-flip task with a particular participant pool. They are not a measure of how often customers lie to a support chatbot or a checkout system.
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Refund claims and low-friction processes
A November 2025 article by Dr. Princely Dibia, Ph.D., CFE, FHEA, published by the Association of Certified Fraud Examiners, describes refund fraud in retail. Its examples include requesting a full-price refund for an item bought on sale, claiming a lost receipt, and making a claim when staff are busy and less likely to check. The article presents these as examples of how trust-based processes can be exploited, not as statistics on how common such claims are.
The article also notes that customers may describe a deceptive claim to themselves as deserved, harmless, or normal. This is practitioner observation rather than a measured psychological finding, but it helps explain why a claim that looks obviously wrong to a business may seem reasonable to the person making it.
Dibia’s stated aim is not to catch liars or turn refund counters into interrogation points. In his words: “The goal is not just to catch liars or turn refund counters into interrogation zones, but rather, to design smarter systems, which are control environments that make dishonesty harder and less justifiable.”
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The article recommends several controls:
- Requiring documentation or verification in ambiguous cases or where the value is higher.
- Publishing refund terms clearly and where customers will see them before purchase.
- Using brief ethical reminders at the point of a claim.
- Giving frontline staff discretion to escalate unclear cases.
- Providing scripts so staff respond consistently.
These are proposed controls. The article does not report measured effect sizes for them, and it does not claim they eliminate refund fraud. It also describes behavioral experiments as a way to study motives and trade-offs, without publishing numerical pilot results.
A small seller’s trade-off
Brandon Eley’s SitePoint article “When Customers Lie,” first published November 30, 2011 and updated November 6, 2024, is a first-person account from an online retailer. A customer returned an opened item, disputed its condition, and threatened a chargeback. The retailer refunded a small amount after weighing the staff time and the cost of a dispute, and chose not to accept future orders from that customer.
The account is useful because it shows the calculation a small business actually makes: the evidence available, the amount at stake, how much staff time a dispute would consume, and whether the relationship is worth keeping. It is one owner’s decision, not a study or a rule. A larger sum, stronger evidence, or a different customer base could justify a different choice.
A case of claims that crossed borders
A 2021 Routledge casebook chapter by M. Mercedes Galan-Ladero and Julie Robson, titled “Unsocial and Irresponsible Behaviour: What Happens When Customers Lie?”, describes alleged British tourist “holiday sickness” claims against Spanish hotels. The chapter attributes roles to claims-management companies and notes the involvement of institutions in both countries. Only a synopsis of the chapter is described here, so the case shows how claims can be organized and pursued through intermediaries. It does not establish how many claims were made, how many were fraudulent, or how they were resolved.
How to respond to a suspected false claim
When a claim looks doubtful, work through these steps before deciding:
- Check the written policy. Find the refund, return, or warranty term that applies. If it was not clear before the purchase, that weakens any refusal, regardless of what the customer said.
- Collect the records. Pull the order, payment record, shipping information, photos, and any prior contact notes. Keep them with the date you pulled them.
- Ask neutral questions. Ask what happened and when, then compare the answer with the records. An inconsistency is a reason to look further, not proof of intent.
- Match the effort to the stakes. A small, reversible decision rarely needs formal verification. A large or hard-to-reverse one may justify asking for documentation or escalating.
- Decide and explain. State the decision and the policy it rests on, in plain language, without accusing the customer of lying.
- Record the outcome. Note what was decided and whether the customer should be treated differently next time.
Several factors should guide the choice between these options:
- Evidence quality: whether there are contemporaneous records and whether the customer’s account holds together.
- Value and reversibility: the amount at stake and how easily a mistaken decision could be corrected.
- Process fairness: whether the policy was clear, consistently applied, and communicated before the dispute.
- Customer and staff impact: the risk of alienating an honest customer, the burden on frontline staff, and the need to protect staff from abusive exchanges.
- Proportionality: verification that matches the ambiguity and value of the claim, rather than treating every customer as a suspect.
These factors are a practical framework drawn from the business account and the ACFE recommendations. They have not been tested as a scoring system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Signs that do not prove a lie
Several things get mistaken for dishonesty. None of them is sufficient on its own:
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- A customer who complains often, who may have legitimate problems.
- An unusual request, which may simply reflect a situation the policy did not anticipate.
Labeling a customer a liar on these grounds alone risks punishing honest people and creating the adversarial relationship that makes disputes more costly.
What the evidence cannot tell you
Available studies and cases do not establish how often customers lie, whether most customers are honest, or whether any particular type of business faces more false claims than another. The experimental percentages above come from a specific task and should not be read as customer-wide rates.
Chargeback, consumer-protection, and service-refusal rules depend on jurisdiction and payment provider. The sources discussed here do not establish those rules, so check your payment processor’s dispute terms and local consumer law before refusing a refund or ending a customer relationship.
Finally, process design is a management lever, not a guarantee. Clear policies, verification, and staff discretion can make dishonest claims harder and less attractive, but none of the sources claims they remove fraud.
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