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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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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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