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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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!