Beyond Demographic Profiling: Cross-Economy Variation in Payment Responses to Unsolicited Money Requests
Using Global Findex 2025 data across 75 economies, this study demonstrates that standardizing for demographic characteristics explains only a negligible portion of the cross-economy variation in payment responses to unsolicited money requests, suggesting that consumer-protection strategies should prioritize contextual and solicitation-specific factors over demographic profiling.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Every day, millions of people receive unexpected messages from strangers asking for money. These requests arrive as phone calls or text messages, often pretending to be from a bank, a government official, or a distant relative in trouble. For consumer protection agencies and researchers, a central question has long been how to identify who is most likely to fall for these requests. The traditional approach relies on demographic profiling: the idea that certain groups of people, defined by their age, gender, education, or where they live, are inherently more vulnerable to financial scams. It is a practical strategy because these details are easy to observe and record. However, a growing body of research suggests that vulnerability is not a fixed trait of a person but rather a situation that arises from the interaction between a person and their environment. This study asks a specific, sharp question: if we look at people who have already received such a request, does their background explain why some send money and others do not?
To answer this, researchers turned to a massive global survey known as the Global Findex Database, which collected information from over 140,000 people across the world. They focused on a specific group: 13,766 adults from 75 different economies who reported receiving an unsolicited request for money on their mobile phones. The researchers were careful to separate the experience of being contacted from the act of paying. They did not count people who never received a message as "successful" victims who resisted; they only looked at those who actually received a request and then decided whether to send the money. This distinction is crucial because the factors that make someone likely to be targeted might be different from the factors that make them likely to pay.
The team began by calculating the rate at which people in each of the 75 economies sent money after receiving a request. They found a wide gap between the countries with the lowest payment rates and those with the highest. Before doing any complex adjustments, the difference between the top 10 percent of economies and the bottom 10 percent was 16.75 percentage points. In other words, if you picked a random person from a high-risk economy and a random person from a low-risk economy, the likelihood of them sending money differed significantly. The natural assumption might be that this gap exists because the people in the high-risk countries have different backgrounds—perhaps they are older, have less education, or live in rural areas compared to those in the low-risk countries.
To test this, the researchers performed a statistical exercise called common-composition standardization. Imagine taking the population of every single country and reshuffling it so that every nation has the exact same mix of people: the same percentage of men and women, the same distribution of ages, the same levels of education, the same workforce participation, and the same split between city and country dwellers. They then recalculated the payment rates for each country using this identical, standardized population. If the differences in payment rates were caused by these demographic factors, the gap between the countries should have shrunk dramatically.
The result was surprising. After forcing every country to have the same demographic makeup, the gap between the highest and lowest payment rates barely changed. It remained at 15.86 percentage points, a reduction of less than one percentage point. The researchers ran this test in many different ways, changing the mathematical models, adding more details about income and digital habits, and removing specific countries to see if one outlier was driving the result. In every scenario, the gap persisted. The data showed that knowing a person's age, gender, education, job status, or whether they live in a city or a village tells us very little about why payment rates differ so much from one economy to another.
This finding does not mean that demographics are irrelevant to consumer safety, nor does it mean that all people face the same risk. Instead, it establishes a clear boundary for what demographic profiling can achieve. The study suggests that the reasons people in different parts of the world react differently to money requests are not found in their personal statistics. The variation likely lies in the details of the request itself, the environment in which the decision is made, or the opportunities available to verify the sender. Perhaps the messages in some countries are more convincing, the urgency is higher, or the systems for checking a sender's identity are weaker. The study concludes that to protect consumers effectively, we must look beyond simple labels like "elderly" or "uneducated" and examine the specific circumstances of the solicitation and the decision-making environment. The puzzle of why people pay remains, but the solution is not found in the demographic profile of the victim.
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