Buy Now, Pay Later: The Hidden Cost of Splitting Reality

 

Figure 1: Buy Now, Pay Later (BNPL) concept

 

 

It’s 11:47 pm. You’re scrolling through an online store and spot a jacket for £200. It feels a bit expensive. Then you see the option to pay £50 today and split the rest over four months. Suddenly, it feels affordable and you click “buy.” Nothing about the price has changed, but the way it’s presented makes it easier to justify. Instead of thinking about the full £200, you focus on the £50 in front of you.

Figure 2 shows how this plays out over time. The dotted line represents what consumers feel they are paying, low at first, then rising. The solid line symbolises real cost: flat and unchanged from day one.The gap between them is where financial trouble starts.

Figure 2: Perceived vs Actual Cost over time under BNPL

 

The problem? This changes how we see cost and risk, leading to both individual debt and market failure.

In the UK, around 27% of adults have used Buy Now, Pay Later (BNPL) in the past year (Poll and Byrne, 2021, p.6). This shows how normalised this form of borrowing has become. BNPL works by shifting attention away from the total cost and towards the first instalment. This reflects present bias, where individuals place more weight on immediate payments than future ones. The £50 feels manageable, while the remaining payments are treated as less important. This can be explained by hyperbolic discounting (Laibson, 1997), where future costs are heavily discounted, leading consumers to judge affordability based only on the present.

In reality, this creates a gap between perceived affordability and actual financial commitment. Many users underestimate the risks, with 42% reporting that they did not fully understand what they were signing up for, and 2 in 5 users having used BNPL without even realising it (Poll and Byrne, 2021, p.3). As a result, payments can quickly build up. The report shows that 41% of users have struggled to make a repayment, while FCA evidence similarly highlights that many consumers do not fully understand the terms and risks of BNPL. This suggests that consumers are misjudging affordability and taking on financial obligations they cannot sustain. BNPL, therefore, does not reduce the cost of consumption; it changes how that cost is perceived, often leading to higher spending and financial strain.

The Information Gap: When 'Buy Now' Meets 'Pay Later'

This misperception is not accidental. It is rooted in a deeper economic problem: asymmetric information.

The core issue is that both sides are guessing. Unlike banks that run deep credit checks, BNPL providers approve most purchases in seconds with almost no background checks, creating an adverse selection problem. Risky borrowers are the very people attracted to BNPL services because other lenders have already said no. Meanwhile, people with excellent credit have little reason to use BNPL when their rewards credit cards offer better perks. Over time, this drives away the "good" customers and leaves BNPL platforms with borrowers who are more likely to default. This is a version of "lemons problem," described by George Akerlof in the used-car market: when buyers can't tell a good car from a bad one, the market gets flooded with bad cars, or "lemons".

At the same time, moral hazard creeps in on both sides. Consumers take more risks because a missed payment won’t immediately wreck their credit score. BNPL providers don't report most of these small loans to credit bureaus, traditional banks can't see how much BNPL debt a person is carrying. This means they can lend to someone who is already drowning in debt without other lenders knowing, and without  bearing the full cost if that borrower eventually collapses (Berg, 2025).

But the information gap isn't one-way. Many users misunderstand the penalties for missed payments or how quickly a small debt can snowball (Di Maggio et al., 2022). BNPL interfaces are designed to feel casual and low-stakes, not like a binding credit agreement. Regulators in Norway have even accused BNPL provider, klarna of hiding the true cost of borrowing (Norwegian Consumer Authority, 2022). In early 2025, Klarna's credit losses jumped 17% as more customers struggled to keep up (Chosunbiz, 2025), highlighting the risks created by information gaps and  the need for transparency.

 

Regulations and Recommendations

To address these issues, the Financial Conduct Authority (FCA) of the UK will start regulating BNPL deferred payment credit from July 15, 2026. Service providers must obtain FCA authorisation and conduct strict affordability checks to prevent irresponsible lending. At the same time, loan terms must be fully disclosed to reduce consumer misunderstandings (FCA, 2026-1).

 

Therefore, we suggest that regulators require all BNPL platforms to share users’ total debt information and present real total costs and repayment risks in a unified, clear format. This will enable platforms to assess a user’s overall indebtedness before offering new credit, and also allow consumers to understand the full financial obligation before purchasing (FCA, 2026-2).

 

These rules will bring greater transparency and fairness to the BNPL market and address market imbalances caused by information gaps.

 

 

Conclusion: Why This Matters

Present bias tricks you into focusing on the £50. Asymmetric information means BNPL doesn't know your true risk, and you don't know the true cost. Adverse selection brings risky borrowers to the front of the line. Moral hazard encourages carelessness on both sides. The lemons problem then drives good customers away.

These concepts don't exist in separate boxes. They feed into each other. A consumer with present bias takes on a BNPL plan they can't really afford. Because BNPL doesn’t run proper credit checks, they get approved anyway. Since BNPL doesn't share data with other lenders, no one sees the debt adding up. When that consumer misses a payment, their credit score stays intact, so they do it again.

The result is market failure. Resources are misallocated, debt rises, and consumers end up worse off than if BNPL had never existed. The FCA's 2026 rules and our suggested data-sharing requirement would help close the information gap. But the best protection is still the simplest. Next time you see "pay £50 today," pause and ask yourself: can I really afford £200?

References:

1.    Akerlof, G. A. (1970), The Market for 'Lemons': Quality Uncertainty and the Market Mechanism, Quarterly Journal of Economics, Vol. 84, No. 3, pp. 488-500
https://www.jstor.org/stable/1879431

2.    Berg, J. (2025), Klarna and Afterpay Keep BNPL Data From Credit Bureaus, PYMNTS.com, August 2025
https://www.pymnts.com/buy-now-pay-later/2025/klarna-afterpay-keep-bnpl-data-from-credit-bureaus/

3.    Chosunbiz (2025), Klarna's Credit Losses Jump 17% as BNPL Defaults Rise
https://biz.chosun.com/en/en-international/2026/02/20/BXF6RDDP4FB4FAZY7X25K5ZSWY/

4.    Davenport, Evans, Hurwitz & Smith, LLP (2024), Managing a Buy Now, Pay Later Loan Program, Davenport Evans

https://dehs.com/managing-a-buy-now-pay-later-loan-program/

5.    Di Maggio, M., Williams, E., & Kalda, A. (2022), Buy Now, Pay Later: Credit Supply, Consumer Demand, and Welfare, National Bureau of Economic Research (NBER) Working Paper No. 30535
https://www.nber.org/papers/w30535

6.    Financial Conduct Authority. (2026-1). Buy Now Pay Later. https://www.fca.org.uk/consumers/buy-now-pay-later

7.    Financial Conduct Authority. (2026-2). New protections confirmed for Buy Now Pay Later borrowers. https://www.fca.org.uk/news/press-releases/new-protections-confirmed-buy-now-pay-later-borrowers

8.    Laibson, D. (1997) ‘Golden Eggs and Hyperbolic Discounting’, The Quarterly Journal of Economics, 112(2), pp. 443–478. Available at: https://doi.org/10.1162/003355397555253.

9.    Norwegian Consumer Authority (2022), Klarna Criticized for Misleading Marketing and Hidden Costs
https://www.forbrukertilsynet.no/english/klarna/

10.                  Poll, H. and Byrne, G. (2021) Buy Now...Pain Later? Citizen’s Advice. Available at: https://assets.ctfassets.net/mfz4nbgura3g/1YG7ZltPP1JfBXDLfAIPgI/132a8617c04231d32eca733af7cdbcfe/BNPL_20report_20_FINAL_.pdf.

11.                  Protections to help Buy Now Pay Later borrowers navigate their financial lives (2025) FCA. Available at: https://www.fca.org.uk/news/press-releases/protections-help-buy-now-pay-later-borrowers-navigate-financial-lives. 

The £30 Problem: How Dupes Are Rewriting the Rules of Luxury

 

Would you still feel cool in your designer outfit if everyone around you was wearing the dupe? While you are scrolling through TikTok, you may have noticed that influencers are racing to find less expensive, look-alike knockoff products from name brands. This is what Gen Z calls the “dupe culture”. They do not feel knockoffs are uncool. Instead, the viral spread of the “dupe culture” among Gen Z even makes them prefer knockoffs more than other generations, but it is not just an economic decision.

Signalling and Differential Costs

Try to think about why you would like top brand products even though they are expensive. A Hermès Birkin can cost £10,000, with an 18-month waitlist to get one. This phenomenon is known as Veblen goods, named after American economist Thorstein Veblen. Veblen argued that in a "leisure class," consuming expensive, non-essential goods is a way of displaying social power.  This seems reasonable, but how does a Hermès Birkin display social power?

Here, we have to introduce an economic concept of signalling, which refers to when one party acts to credibly convey private information to another party to overcome asymmetric information (Spence, 1973). In our example, the leisure class are the signallers who take the Hermès Birkin as a signal to show their social power to others who are the receivers.

Can a cheap and average bag be a suitable signal? The answer is no, as an important condition of a signal is that it only works if it has differential costs. In our example, the Hermès Birkin is a small amount of spending for a leisure class person, but expensive to an average person, so the high cost screens out the latter, as they cannot genuinely afford it. However, what if someone found a cheap and similar-looking “Hermès Birkin”?

When Everyone Can Signal, Nobody Can

So, what happens when a £30 AliExpress bag becomes indistinguishable from a £10,000 Birkin? The signal loses its informational value.

Global trade in counterfeit goods and imitation products has reached $467 billion (OECD, 2025), and on TikTok alone, the hashtag #dupe has amassed over 7 billion views. Dupe culture is not a niche trend, it is a mass market phenomenon.

In economic terms, this represents a shift from a separating equilibrium to a pooling equilibrium. In a healthy luxury market, consumers sort themselves based on their ability to bear the cost of the signal: those who can genuinely afford luxury goods (high-type senders) purchase them, while those who cannot (low-type senders) are screened out by price. However, when dupes collapse this cost differential, low-type senders can mimic high-type signals at minimal cost. The result is a pooling equilibrium: nobody can tell who is who anymore. The Birkin stops being proof of wealth and becomes just another bag.

Fighting Back: How Luxury Brands Are Raising the Bar

Luxury brands are not sitting still. Faced with a pooling equilibrium, they are actively redesigning signals to restore their credibility.

First, firms intensify exclusivity. By restricting supply through waitlists and controlled distribution, brands like Hermès ensure that access (not just price) becomes the barrier. Even if a product is copied, it cannot be legitimately obtained.

Second, brands invest in non-replicable experiences. Companies such as Louis Vuitton and Chanel embed value in in-store rituals, craftsmanship, and heritage narratives. The signal shifts from the physical object to the intangible experience surrounding it.

The third, and perhaps most interesting, is digital authentication. Luxury houses are increasingly adopting Digital Product Passports, embedded microchips and blockchain-backed certificates of authenticity that verify a product's origin instantly (FashionBI, 2025). LVMH, Prada and Cartier have already piloted this through the Aura Blockchain Consortium. The signal moves somewhere a £30 knockoff cannot follow.

In effect, luxury brands are engaged in an ongoing effort to re-establish a separating equilibrium in a world where visual imitation is no longer costly.

The psychology behind the price tag

Even if brands are successful in rebuilding their signals, the way buyers and owners perceive their products still plays a crucial role. But dupe culture does not just disrupt markets, it gets inside people's heads. To understand why, we need to look beyond standard economic theory and into behavioural economics.

One key concept is the endowment effect, which describes our tendency to value items we own more highly than their market price would suggest. A £3,000 bag is more than just a bag, once it is yours, the emotional pride and social status it carries become part of its value.

However, it is at this stage where dupe culture starts to undermine the psychological value of ownership too.

Imagine paying £10,000 and waiting 18 months for an Hermès Birkin, only to see near identical versions roaming the streets. That sense of pride and ‘showing off’ that comes with owning the authentic bag is eroded, as others can no longer distinguish the real from a convincing dupe. Both the bag and its owner lose a kind of celebrity status and instead you start to blend into the crowd filled with £30 lookalikes.

This reaction is best understood through loss aversion, the idea that losses feel more significant than gains (Kahneman and Tversky, 1979). In this case, buyers who paid thousands aren't just worried about the financial loss, but also the symbolic loss of exclusivity. What was once a costly way to access the perks of owning an authentic bag is now easily mirrored through dupes, which are able to send the same signals whilst saving thousands of pounds. Due to loss aversion, that loss of exclusivity feels far more costly than the satisfaction of owning an Hermès Birkin ever did.

In other words, dupes don't destroy the market for bags like the Hermès Birkin as there will always be demand for authenticity and exclusivity. Instead, they strip away the magic of owning one. Luxury brands and dupe culture are locked in an arms race, and whether the signal survives will determine whether that £10,000 bag is ever worth the price.

References:

FashionBI (2025). How Luxury Brands Are Embracing the Digital Product Passports. Available at: https://www.fashionbi.com/insights/how-luxury-brands-are-embracing-the-digital-product-passports

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185

Ken Research (2025). Global Fast Fashion Market. Available at: https://www.kenresearch.com/global-fast-fashion-market

Mosendz, P. and Rubin, C. (2023). Gen Z Is the Dupe Generation. Business Insider. Available at: https://www.businessinsider.com/gen-z-is-the-dupe-generation-2023-12

OECD (2025). Global trade in fake goods reached USD 467 billion, posing risks to consumer safety and compromising intellectual property. Available at: https://www.oecd.org/en/about/news/press-releases/2025/05/global-trade-in-fake-goods-reached-USD-467-billion-posing-risks-to-consumer-safety-and-compromising-intellectual-property.html

Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010

Trust Place (2025). Dupes on TikTok: How Brands Are Losing Control of Their Image. Available at: https://www.trust-place.com/post/dupes-on-tiktok-how-brands-are-losing-control-of-their-image

Why can’t we stop scrolling? The Economics of attention in the age of Algorithms

 

Perhaps we all have had such an experience: we intended to look up a word but ended up watching videos for an hour without realizing it. Is it the content itself or the continuous anticipation for "the next more interesting content" that makes people addicted? During this process, the algorithm continuously learns user behaviour, makes precise recommendations, gradually shapes our information environment, and influences our attention and decision-making. It is like an "invisible hand", guiding our choices imperceptibly. This raises a key question: In the AI era, are our decisions autonomous or driven by algorithms? This blog explores that algorithms are influencing individual decisions by taking advantage of uncertainty and human behavioural biases and may even deviate from the optimal outcome. Therefore, we need to view algorithms rationally, regarding them as auxiliary tools rather than being dominated by them, and become the true "decision-making agents". 

Image 1 (AI-generated using ChatGPT-4o, OpenAI, 2026)

 

You think you are just "swiping casually", but in fact, you are participating in a carefully designed economic system. Each swipe seems like an independent choice, but it occurs in an environment full of uncertainties: you don't know what the next content will be, but you always feel - perhaps the next one will be better. So, you continue. This kind of behaviour is not about maximizing the average return, but rather about chasing after "possible better outcomes". In other words, you are not calculating the expected value but gambling on luck. 

 

Behavioural economics predicted this long ago. Prospect theory tells us that people overreact to "possible good things" (Kahneman, Tversky, 1979), while hyperbolic discounting indicates that we overestimate the immediate attractiveness of "one more" (Laibson, 1997). In short: It's certain to stop, but there's hope to keep scrolling - humans would choose the latter. But the key point is that this kind of "uncertainty" does not exist naturally but is deliberately created. The algorithmic goal of the platform is not to make you happier, but to make you stay a little longer. By constantly providing fresh and unpredictable content, it keeps you in a state of "the next one might be better". This is precisely the application of information design in reality (Kamenica, Gentzkow, 2011). Even better (and even more terrifying) is that all of this will reinforce itself. Every time you scroll and every second you stay, it will be recorded to train the algorithm, making it better understand you - and then more accurately get you to keep scrolling. This is a typical feedback loop: behaviour → data → algorithm → more behaviour. Over time, the system will converge to a stable state: you keep scrolling, and it keeps feeding. Does it sound like balanced? Indeed. But the problem is that this equilibrium may not be good. According to the bounded rationality viewpoints of Herbert A. Simon and Reinhard Selten, people do not make optimal decisions in the first place, and the system happens to take advantage of this. 

 

Add a classic economic issue: information asymmetry. As George Akerlof (1970) pointed out, when one party has more information than the other, the result will be systematically distorted. Here, the platform knows everything about you, while you have almost no idea what the algorithm is doing. This information advantage enables it not only to predict you but also to "shape you". What's more interesting is that having more information doesn't necessarily mean better. Jonathan Levin (2001) pointed out that an increase in information sometimes reduces overall efficiency instead. On the platform, more data about you doesn't necessarily mean better results; instead, it might make it harder for you to stop. So, the question has never been "Why can't you stop?", but rather: In such a system, do you really have a chance to stop? This is not a matter of individual willpower, but rather a systematic outcome composed of uncertainty, behavioural bias, algorithmic incentives, feedback loops and information asymmetry. You are scrolling through your phone, but the system is also "scrolling through you". 

 

This system brings up a deeper issue: market failure caused by externalities. As mentioned earlier, behavioural biases - hyperbolic discounting - can cause individuals to underestimate future costs when making decisions under uncertainty. When people decide to keep scrolling, they often prioritize the immediate entertainment benefits and overlook long-term costs such as reduced productivity, weakened attention and decreased happiness (Laibson, 1997). Therefore, the perceived cost of watching videos is lower than its actual cost. Excessive consumption occurs when private costs are lower than social costs.

 

More importantly, this influence is not limited to the individual level. When a large number of users systematically allocate their time to low-value content, it will have a broader impact on human capital, productivity and information quality. However, these social costs are not reflected in the platform's incentive mechanism. On the contrary, because attention can be monetized, platforms tend to maximize user engagement, thereby causing a deviation between private incentives and social welfare. Therefore, the final equilibrium formed is inefficient: the consumption of attention exceeds the social optimum level, although every individual decision seems rational

Image 2 (AI-generated using ChatGPT-4o, OpenAI, 2026)

 

If the problem stems from the system itself, then the answer is not merely to enhance self-control. AI-driven platforms are not neutral tools; they reinforce behaviours and maintain usage habits. Under the combined effect of uncertainty, behavioural bias and algorithmic incentives, seemingly simple choices accumulate continuously, eventually forming a stable yet inefficient equilibrium - attention is overly consumed.

 

Against such a backdrop, a more effective approach might start from system design. On the one hand, the "choice architecture" can be redesigned. By adding moderate "friction" (such as using reminders or natural stop points), behaviour can be transformed from automation to conscious decision-making. On the other hand, it can enhance transparency, enabling users to have a clearer understanding of their own actions and the operational logic of the platform. Given the scale of screen usage time (Barber, 2025), this problem is difficult to be alleviated naturally and consciously by individuals alone. Therefore, the key issue is not whether we use technology, but whether we still hold the initiative in the process of using it.

 

 

Reference List:

Akerlof, G.A. (1970) ‘The market for “lemons”: Quality uncertainty and the market mechanism’, The Quarterly Journal of Economics, 84(3), pp. 488–500. Available at: https://doi.org/10.2307/1879431(Accessed: 5 April 2026). 

Barber, S. (2025) Mobile phone and internet usage statistics in the UK. Finder. Available at: Mobile internet statistics UK (Accessed: 23 April 2026). 

Image 1 (AI-generated using ChatGPT-4o, OpenAI, 2026)

Image 2 (AI-generated using ChatGPT-4o, OpenAI, 2026)

Kahneman, D. and Tversky, A. (1979) ‘Prospect theory: An analysis of decision under risk’, Econometrica, 47(2), pp. 263–291. Available at:https://doi.org/10.2307/1914185 (Accessed: 7 April 2026). 

Kamenica, E. and Gentzkow, M. (2011) ‘Bayesian persuasion’, American Economic Review, 101(6), pp. 2590–2615. Available at: https://www.nber.org/papers/w15540 (Accessed: 6 April 2026)

Laibson, D. (1997) ‘Golden eggs and hyperbolic discounting’, The Quarterly Journal of Economics, 112(2), pp. 443–478. Available at: https://doi.org/10.1162/003355397555253(Accessed: 3 April 2026)

Levin, J. (2001) ‘Information and the market for lemons’, The RAND Journal of Economics, 32(4), pp. 657–666. Available at: https://doi.org/10.2307/2696386 (Accessed: 3 April 2026). 

Selten, R. (1990) ‘Bounded rationality’, Journal of Institutional and Theoretical Economics, 146(4), pp. 649–658. Available at:https://www.jstor.org/stable/40751353 (Accessed: 5 April 2026). 

Simon, H.A. (1955) ‘A behavioral model of rational choice’, The Quarterly Journal of Economics, 69(1), pp. 99–118. Available at: https://doi.org/10.2307/1884852 (Accessed: 5 April 2026).