3 post-COVID-19 fintech trends you should know about

Iwo Hachulski

29 Jun 2020
3 post-COVID-19 fintech trends you should know about

It is no doubt that fintech has been gradually implementing successive stages of the revolution in the banking services sector. The main beneficiaries of this state of affairs are, apart from fintech itself, consumers. Traditional banking adopts various strategies regarding the existing status quo, some banks, including Santander, are constantly investing heavily in the most promising fintech startups in order to then implement their solutions for their customers. Others - try to create their own unique products, which are then implemented by other players in the market. One of the best examples here is Bank PKO BP and the contactless payment system BLIK developed by the bank's IT department. The constantly ongoing time of the epidemic has changed many behaviors and habits. What mark has COVID-19 left on the modern financial services sector, a popular fintech? What prospects should we expect from a full opening of economies in a global context?

Extraordinary times require extraordinary solutions

Revaluation of priorities - this is probably the simplest and most rational way to describe the changes introduced by the coronavirus in our lives. Sanitary restrictions have forced the financial sector, like many others, to a new opening - and a look into the future from a completely different perspective. The need for full mobility introduced along with the full compatibility of the solutions used became, within a few weeks, a determinant of the effectiveness of the adaptation of both traditional banking and the fintech giants. 

However, it would be unfair to put them next to each other in this context - mainly due to the fact that it was not so much an unimaginable challenge for fintech to move almost 100 percent of their business into the digital world. This state of affairs is primarily due to the fact that the vast majority (and very often 100%) of fintech services offered within the framework of retail banking, for example, are available only online. The vast majority of them have decided on such a business model from the very beginning - on the one hand, they have focused on reducing the costs of running branches together with minimizing fixed costs and, as a result, full mobility, and on the other hand, they have often closed themselves off to clients currently almost exclusively connected with traditional banking. However, such a strategy has brought the expected results. Fintechs, although also often forced to make cuts - among others, Revolut announced the introduction of restrictions in the cheapest plan offered to customers and numerous layoffs in the Polish branch of the company - usually did not have to face the complicated task of transferring several thousand employees into remote operation almost overnight. Thus, they were able to focus on introducing specific solutions offered to their clients instead of dealing with their internal problems in the first place. For example, Starling Bank launched the "combined card" function, which enables the transfer of a second, "back-up" debit card linked to the customer's account to someone who can spend on their behalf. A team of developers from Fronted, Credit Kudos and 11:FS created Covid Credit for the self-employed, allowing access to financial aid for the most vulnerable people who are not covered by government support. A significant role is also slowly being played by fintech software houses, which offer IT services using the latest Fintech solutions such as Blockchain or AI.

Mobility and security above all

Due to health restrictions and recommendations, the volume of both card and phone payments increased slightly, for instance, in India it was about 5%. According to many experts in banking and social psychology, such a trend may last longer. According to the Mordor Intelligence report "Mobile Payments Market - Growth, Trends, and Forecast" (2020-2025) The use of m-payments will continue to grow strongly with an annual cumulative growth rate of as much as 26.93%. In Central Europe, this is mainly due to the still very young banking system, often developed from scratch only in the 1990s. For this reason, many behaviors are not so deeply rooted in society, which is thus much more susceptible to all kinds of innovation.

Another element that is hard not to mention is budgeting apps, i.e. applications for planning and controlling the budget. Although their popularity in Poland and other Central European countries is not as impressive as in the United States, this may gradually change due to the inevitable economic crisis caused by the coronavirus pandemic. Full control over one's own budget due to the difficult social and economic situation will undoubtedly become one of the priorities - thus bringing the possibility of a structured review of one's own spending to the fore. The applications differ in many ways, so that everyone can find something for themselves. Mint automatically categorizes transactions from credit and debit cards connected to the system and tracks them against a budget that can be adjusted and adapted to user's needs. Goodbudget, on the other hand, is mainly dedicated to couples - it is possible to share and fully synchronize the budget with another person in both iOS and Android.

Tandem and natural competition

Despite all the turmoil, the post-pandemic outlook for the coming months seems stable, although not as promising as previously expected. According to Ron Shevlin, Managing Director of Fintech Research at Cornerstone Advisors, the era of fintech experimentation is slowly coming to an end. The indicators that will gain in importance are primarily the number of accounts funded and their percentage in relation to the total number of application downloads. In his opinion, in the case of mainly B2B-oriented fintechs, the crucial benchmarks will be more operational, such as improved speed, cycle time and lower costs.

Moreover, there is a large disparity within the banking sector environment itself. There is continued optimism among the largest fintechs. By February 2020, Revolut already had less than 11 million users. According to the owners' forecasts, the number of users is expected to reach 13.07 million by the end of June, and then increase by about 20%, to reach 16.45 million by December 2020. The second largest player, N26, has already exceeded 5 million users in January, thus maintaining almost exponential growth and significantly exceeding the company's forecasts.

The situation is different for traditional banks, whose financial situation has often deteriorated. According to analyses of the International Monetary Fund, in addition to the immediate challenges posed by the COVID-19 outbreak, the relentless period of low interest rates may put further pressure on bank profitability in the forthcoming years. This may be a cause for concern, mainly due to the fact that it is the constant development of both traditional and modern banking that may be the key to recover from the crisis. A unique banking tandem also guarantees a greater choice of available services for the customer, and thus more competition and increased innovation in the fight for each costumer. 

What is more, smaller fintechs also face considerable problems. According to the latest CB Insights report, the value of contracts signed by fintech in Q1 2020 decreased by as much as 35% compared to Q4 2019. Better-invested and profitable fintechs are in a much better position, especially in the context of depletion of investment funds and hence increased competition in the fight for any funds for further development. The problems of some may paradoxically become a pain for others, thus worsening the situation of the sector and, consequently, often of the entire economy. 

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Monte Carlo Simulations in Tokenomics

Kajetan Olas

01 May 2024
Monte Carlo Simulations in Tokenomics

As the web3 field grows in complexity, traditional analytical tools often fall short in capturing the dynamics of digital markets. This is where Monte Carlo simulations come into play, offering a mathematical technique to model systems fraught with uncertainty.

Monte Carlo simulations employ random sampling to understand probable outcomes in processes that are too complex for straightforward analytic solutions. By simulating thousands, or even millions, of scenarios, Monte Carlo methods can provide insights into the likelihood of different outcomes, helping stakeholders make informed decisions under conditions of uncertainty.

In this article, we will explore the role of Monte Carlo simulations within the context of tokenomics.  illustrating how they are employed to forecast market dynamics, assess risk, and optimize strategies in the volatile realm of cryptocurrencies. By integrating this powerful tool, businesses and investors can enhance their analytical capabilities, paving the way for more resilient and adaptable economic models in the digital age.

Understanding Monte Carlo Simulations

The Monte Carlo method is an approach to solving problems that involve random sampling to understand probable outcomes. This technique was first developed in the 1940s by scientists working on the atomic bomb during the Manhattan Project. The method was designed to simplify the complex simulations of neutron diffusion, but it has since evolved to address a broad spectrum of problems across various fields including finance, engineering, and research.

Random Sampling and Statistical Experimentation

At the heart of Monte Carlo simulations is the concept of random sampling from a probability distribution to compute results. This method does not seek a singular precise answer but rather a probability distribution of possible outcomes. By performing a large number of trials with random variables, these simulations mimic the real-life fluctuations and uncertainties inherent in complex systems.

Role of Randomness and Probability Distributions in Simulations

Monte Carlo simulations leverage the power of probability distributions to model potential scenarios in processes where exact outcomes cannot be determined due to uncertainty. Each simulation iteration uses randomly generated values that follow a specific statistical distribution to model different outcomes. This method allows analysts to quantify and visualize the probability of different scenarios occurring.

The strength of Monte Carlo simulations lies in the insight they offer into potential risks. They allow modelers to see into the probabilistic "what-if" scenarios that more closely mimic real-world conditions.

Monte Carlo Simulations in Tokenomics

Monte Carlo simulations are instrumental tool for token engineers. They're so useful due to their ability to model emergent behaviors. Here are some key areas where these simulations are applied:

Pricing and Valuation of Tokens

Determining the value of a new token can be challenging due to the volatile nature of cryptocurrency markets. Monte Carlo simulations help by modeling various market scenarios and price fluctuations over time, allowing analysts to estimate a token's potential future value under different conditions.

Assessing Market Dynamics and Investor Behavior

Cryptocurrency markets are influenced by a myriad of factors including regulatory changes, technological advancements, and shifts in investor sentiment. Monte Carlo methods allow researchers to simulate these variables in an integrated environment to see how they might impact token economics, from overall market cap fluctuations to liquidity concerns.

Assesing Possible Risks

By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks. This is perhaps the most important function of Monte Carlo Process, since these risks can’t be assessed any other way.

Source: How to use Monte Carlo simulation for reliability analysis?

Benefits of Using Monte Carlo Simulations

By generating a range of possible outcomes and their probabilities, Monte Carlo simulations help decision-makers in the cryptocurrency space anticipate potential futures and make informed strategic choices. This capability is invaluable for planning token launches, managing supply mechanisms, and designing marketing strategies to optimize market penetration.

Using Monte Carlo simulations, stakeholders in the tokenomics field can not only understand and mitigate risks but also explore the potential impact of different strategic decisions. This predictive power supports more robust economic models and can lead to more stable and successful token launches. 

Implementing Monte Carlo Simulations

Several tools and software packages can facilitate the implementation of Monte Carlo simulations in tokenomics. One of the most notable is cadCAD, a Python library that provides a flexible and powerful environment for simulating complex systems. 

Overview of cadCAD configuration Components

To better understand how Monte Carlo simulations work in practice, let’s take a look at the cadCAD code snippet:

sim_config = {

    'T': range(200),  # number of timesteps

    'N': 3,           # number of Monte Carlo runs

    'M': params       # model parameters

}

Explanation of Simulation Configuration Components

T: Number of Time Steps

  • Definition: The 'T' parameter in CadCAD configurations specifies the number of time steps the simulation should execute. Each time step represents one iteration of the model, during which the system is updated. That update is based on various rules defined by token engineers in other parts of the code. For example: we might assume that one iteration = one day, and define data-based functions that predict token demand on that day.

N: Number of Monte Carlo Runs

  • Definition: The 'N' parameter sets the number of Monte Carlo runs. Each run represents a complete execution of the simulation from start to finish, using potentially different random seeds for each run. This is essential for capturing variability and understanding the distribution of possible outcomes. For example, we can acknowledge that token’s price will be correlated with the broad cryptocurrency market, which acts somewhat unpredictably.

M: Model Parameters

  • Definition: The 'M' key contains the model parameters, which are variables that influence system's behavior but do not change dynamically with each time step. These parameters can be constants or distributions that are used within the policy and update functions to model the external and internal factors affecting the system.

Importance of These Components

Together, these components define the skeleton of your Monte Carlo simulation in CadCAD. The combination of multiple time steps and Monte Carlo runs allows for a comprehensive exploration of the stochastic nature of the modeled system. By varying the number of timesteps (T) and runs (N), you can adjust the depth and breadth of the exploration, respectively. The parameters (M) provide the necessary context and ensure that each simulation is realistic.

Messy graph representing Monte Carlo simulation, source: Bitcoin Monte Carlo Simulation

Conclusion

Monte Carlo simulations represent a powerful analytical tool in the arsenal of token engineers. By leveraging the principles of statistics, these simulations provide deep insights into the complex dynamics of token-based systems. This method allows for a nuanced understanding of potential future scenarios and helps with making informed decisions.

We encourage all stakeholders in the blockchain and cryptocurrency space to consider implementing Monte Carlo simulations. The insights gained from such analytical techniques can lead to more effective and resilient economic models, paving the way for the sustainable growth and success of digital currencies.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What is a Monte Carlo simulation in tokenomics context?

  • It's a mathematical method that uses random sampling to predict uncertain outcomes.

What are the benefits of using Monte Carlo simulations in tokenomics?

  • These simulations help foresee potential market scenarios, aiding in strategic planning and risk management for token launches.

Why are Monte Carlo simulations unique in cryptocurrency analysis?

  • They provide probabilistic outcomes rather than fixed predictions, effectively simulating real-world market variability and risk.

Behavioral Economics in Token Design

Kajetan Olas

22 Apr 2024
Behavioral Economics in Token Design

Behavioral economics is a field that explores the effects of psychological factors on economic decision-making. This branch of study is especially pertinent while designing a token since user perception can significantly impact a token's adoption.

We will delve into how token design choices, such as staking yields, token inflation, and lock-up periods, influence consumer behavior. Research studies reveal that the most significant factor for a token's attractiveness isn’t its functionality, but its past price performance. This underscores the impact of speculative factors. Tokens that have shown previous price increases are preferred over those with more beneficial economic features.

Understanding Behavioral Tokenomics

Understanding User Motivations

The design of a cryptocurrency token can significantly influence user behavior by leveraging common cognitive biases and decision-making processes. For instance, the concept of "scarcity" can create a perceived value increase, prompting users to buy or hold a token in anticipation of future gains. Similarly, "loss aversion," a foundational principle of behavioral economics, suggests that the pain of losing is psychologically more impactful than the pleasure of an equivalent gain. In token design, mechanisms that minimize perceived losses (e.g. anti-dumping measures) can encourage long-term holding.

Incentives and Rewards

Behavioral economics also provides insight into how incentives can be structured to maximize user participation. Cryptocurrencies often use tokens as a form of reward for various behaviors, including mining, staking, or participating in governance through voting. The way these rewards are framed and distributed can greatly affect their effectiveness. For example, offering tokens as rewards for achieving certain milestones can tap into the 'endowment effect,' where people ascribe more value to things simply because they own them.

Social Proof and Network Effects

Social proof, where individuals copy the behavior of others, plays a crucial role in the adoption of tokens. Tokens that are seen being used and promoted by influential figures within the community can quickly gain traction, as new users emulate successful investors. The network effect further amplifies this, where the value of a token increases as more people start using it. This can be seen in the rapid growth of tokens like Ethereum, where the broad adoption of its smart contract functionality created a snowball effect, attracting even more developers and users.

Token Utility and Behavioral Levers

The utility of a token—what it can be used for—is also crucial. Tokens designed to offer real-world applications beyond mere financial speculation can provide more stable value retention. Integrating behavioral economics into utility design involves creating tokens that not only serve practical purposes but also resonate on an emotional level with users, encouraging engagement and investment. For example, tokens that offer governance rights might appeal to users' desire for control and influence within a platform, encouraging them to hold rather than sell.

Understanding Behavioral Tokenomics

Intersection of Behavioral Economics and Tokenomics

Behavioral economics examines how psychological influences, various biases, and the way in which information is framed affect individual decisions. In tokenomics, these factors can significantly impact the success or failure of a cryptocurrency by influencing user behavior towards investment

Influence of Psychological Factors on Token Attraction

A recent study observed that the attractiveness of a token often hinges more on its historical price performance than on intrinsic benefits like yield returns or innovative economic models. This emphasizes the fact that the cryptocurrency sector is still young, and therefore subject to speculative behaviors

The Effect of Presentation and Context

Another interesting finding from the study is the impact of how tokens are presented. In scenarios where tokens are evaluated separately, the influence of their economic attributes on consumer decisions is minimal. However, when tokens are assessed side by side, these attributes become significantly more persuasive. This highlights the importance of context in economic decision-making—a core principle of behavioral economics. It’s easy to translate this into real-life example - just think about the concept of staking yields. When told that the yield on e.g. Cardano is 5% you might not think much of it. But, if you were simultaneously told that Anchor’s yield is 19%, then that 5% seems like a tragic deal.

Implications for Token Designers

The application of behavioral economics to the design of cryptocurrency tokens involves leveraging human psychology to encourage desired behaviors. Here are several core principles of behavioral economics and how they can be effectively utilized in token design:

Leveraging Price Performance

Studies show clearly: “price going up” tends to attract users more than most other token attributes. This finding implies that token designers need to focus on strategies that can showcase their economic effects in the form of price increases. This means that e.g. it would be more beneficial to conduct a buy-back program than to conduct an airdrop.

Scarcity and Perceived Value

Scarcity triggers a sense of urgency and increases perceived value. Cryptocurrency tokens can be designed to have a limited supply, mimicking the scarcity of resources like gold. This not only boosts the perceived rarity and value of the tokens but also drives demand due to the "fear of missing out" (FOMO). By setting a cap on the total number of tokens, developers can create a natural scarcity that may encourage early adoption and long-term holding.

Initial Supply Considerations

The initial supply represents the number of tokens that are available in circulation immediately following the token's launch. The chosen number can influence early market perceptions. For instance, a large initial supply might suggest a lower value per token, which could attract speculators. Data shows that tokens with low nominal value are highly volatile and generally underperform. Understanding how the initial supply can influence investor behavior is important for ensuring the token's stability.

Managing Maximum Supply and Inflation

A finite maximum supply can safeguard the token against inflation, potentially enhancing its value by ensuring scarcity. On the other hand, the inflation rate, which defines the pace at which new tokens are introduced, influences the token's value and user trust.

Investors in cryptocurrency markets show a notable aversion to deflationary tokenomics. Participants are less likely to invest in tokens with a deflationary framework, viewing them as riskier and potentially less profitable. Research suggests that while moderate inflation can be perceived neutrally or even positively, high inflation does not enhance attractiveness, and deflation is distinctly unfavorable.

Source: Behavioral Tokenomics: Consumer Perceptions of Cryptocurrency Token Design

These findings suggest that token designers should avoid high deflation rates, which could deter investment and user engagement. Instead, a balanced approach to inflation, avoiding extremes, appears to be preferred among cryptocurrency investors.

Loss Aversion

People tend to prefer avoiding losses to acquiring equivalent gains; this is known as loss aversion. In token design, this can be leveraged by introducing mechanisms that protect against losses, such as staking rewards that offer consistent returns or features that minimize price volatility. Additionally, creating tokens that users can "earn" through participation or contribution to the network can tap into this principle by making users feel they are safeguarding an investment or adding protective layers to their holdings.

Social Proof

Social proof is a powerful motivator in user adoption and engagement. When potential users see others adopting a token, especially influential figures or peers, they are more likely to perceive it as valuable and trustworthy. Integrating social proof into token marketing strategies, such as showcasing high-profile endorsements or community support, can significantly enhance user acquisition and retention.

Mental Accounting

Mental accounting involves how people categorize and treat money differently depending on its source or intended use. Tokens can be designed to encourage specific spending behaviors by being categorized for certain types of transactions—like tokens that are specifically for governance, others for staking, and others still for transaction fees. By distinguishing tokens in this way, users can more easily rationalize holding or spending them based on their designated purposes.

Endowment Effect

The endowment effect occurs when people value something more highly simply because they own it. For tokenomics, creating opportunities for users to feel ownership can increase attachment and perceived value. This can be done through mechanisms that reward users with tokens for participation or contribution, thus making them more reluctant to part with their holdings because they value them more highly.

Conclusion

By considering how behavioral factors influence market perception, token engineers can create much more effective ecosystems. Ensuring high demand for the token, means ensuring proper funding for the project in general.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

How does the initial supply of a token influence its market perception?

  • The initial supply sets the perceived value of a token; a larger supply might suggest a lower per-token value.

Why is the maximum supply important in token design?

  • A finite maximum supply signals scarcity, helping protect against inflation and enhance long-term value.

How do investors perceive inflation and deflation in cryptocurrencies?

  • Investors generally dislike deflationary tokens and view them as risky. Moderate inflation is seen neutrally or positively, while high inflation is not favored.