It is impossible to ignore the profound effect of AI on finance. AI adoption will let FinTech organizations grow three times faster compared to traditional banks between 2023 and 2028 according to McKinsey research.

A well-thought-out plan combined with competence in AI development and implementation is the key to high-quality growth.

This article provides insights into the state of AI in FinTech, discussing transformational opportunities, challenges, trends to follow, and inspiring use cases to consider when planning your next AI solution.

1 AI in FinTech: Market Overview

The financial industry has always been on the cutting edge of technological progress. Back in the 1960s, financial companies installed mainframes to boost operations by decreasing calculation times. They also introduced the first ATMs.

Electronic card payments came about in 1979 to reduce checkout times and increase customer convenience, and they became popular in the 1980s. 

Online banking and payments were introduced in the 1990s, enabling users to make fast purchases — including, with the advent of mobile internet and smartphones in the late 2000s, when on the go.

Now, it is AI’s turn to be the next technological value driver. 

Mordor Intelligence forecasts that the value of AI in FinTech will be $44.08B in 2024. Despite challenges, it is expected to grow at a CAGR of 2.91%, reaching $50.87B in 2029.

North America dominates the FinTech market, accounting for the biggest share of market value, while the Asia–Pacific region is expected to experience the highest pace of market growth between 2024 and 2029.

FinTech software takes advantage of machine learning (ML) in addition to “traditional” and generative artificial intelligence (AI) technologies. They are used across departments to cope with the growing number and complexity of tasks. McKinsey estimates the total potential value of AI for the banking industry to be up to $1T. Of this, $660.9B stems from traditional AI, advanced AI, and machine learning, while generative AI may provide up to $361.5B in value.

AI in FinTech

Machine learning and traditional AI are already deeply embedded in banking operations, helping to detect malicious activity, forecast risks, and streamline operations. 

Today, applications of AI in banking are growing alongside generative AI opportunities.

The five key domains where AI creates the most value in FinTech products include:

  • Decision support – understand client needs and optimize financial offers, rates, and services based on new insights.
  • Risk management – identify fraud and money laundering schemes, identify possible defaults, and optimize planning as well as management processes.
  • Background checks – perform security checks for selected candidates and identify threats that could lead to breaches related to human factors.
  • Document processing – improve document recognition; automate data extraction and sorting to decrease transaction times.
  • Client support – ensure basic support and help clients receive instant answers to frequently asked questions without navigating lengthy wikis.

The introduction of advanced and generative AI elevates the tech potential of financial services:

  • Generative AI lets businesses reinvent processes, enhancing cybersecurity, risk management, score calculations, and more. 
  • Generative AI boosts the efficiency of knowledge workers by helping with extracting, structuring, and summarizing knowledge. 

Large language models (LLMs) revolutionize client support by handling requests 24/7 in each client’s native language while providing consistent responses.

ai in fintech industry

2 Use Cases and Top Six Examples of AI in FinTech

Let’s discuss in detail use cases where AI can enhance a FinTech company’s processes, along with noteworthy AI-powered products.

FinTech and AI Use Cases

AI can improve all aspects of financial workflows where humans face repetitive and intensive but low-value-added operations. Here are the most significant:

  • Personalized plans and offers

Algorithms can analyze typical client behavior and regular activities to build a custom plan or offer an alternative that better matches clients’ needs than the plan they are currently using.

  • Improved client service

AI can provide instant and efficient client support 24/7. AI assistants can respond to queries in plain language and collect client feedback, which businesses can use to improve their services.

  • Credit scoring

AI has been used for credit assessment for a long time. AI algorithms accurately track standard client information plus alternative data, which helps financial institutions understand a client’s level of financial responsibility and potentially offer better financing terms.

  • Robo-advising

The market for robo-advisors is booming — it is anticipated to grow from $7.39B in 2023 to $9.5B in 2024 according to Polaris research. AI-powered robo-advisors facilitate automated investments and help with preparing financial strategies based on a client’s preferences and future objectives.

  • Algorithmic trading

Successful algorithmic trading requires complex automated trading strategies. AI can analyze vast quantities of historical data and current trends, identifying emerging opportunities and reacting to price changes faster than humans.

  • Enhanced security

AI-based security tools can monitor user operations, detect unusual patterns or suspicious behavior, and prevent unauthorized access to customers’ money in real time, sending abnormal activities to a human support team for verification. AI also helps financial organizations anticipate trends and adapt to emerging cyber threats, including AI-based.

  • Productivity

AI decreases the need for manual labor. For instance, AI can enhance self-service by extracting information from a financial client’s documents (ID cards, printed receipts, orders, etc.) and automatically adding it to the client’s profile, saving hours of work for both clients and managers.

  • Software transformation

Legacy software has become a bottleneck for financial organizations. Many have used the same software for decades without substantial reengineering, resulting in limited scalability, a lack of modern features, and increasing security threats.

Financial organizations can address the problem of legacy software with the help of AI. For example, a bank might use AI to decrease the costs of replacing an old Cobol system. AI can be used to translate source code into a modern programming language, address technical debt, or increase test coverage.

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How AI Is Used in FinTech: Five Best Apps

Leading examples of artificial intelligence in FinTech include:



Kasisto offers a secure generative AI platform that has been built specifically to create personalized and engaging experiences for the financial industry. Financial organizations can seamlessly integrate the Kasisto platform into their digital systems and tailor it to their specific needs.



Adyen uses AI in various services and products, including for fraud detection, payment routing, real-time insights, risk management, and client services, helping financial organizations build robust financial products.



Featurespace focuses on behavioral analytics and advanced AI/ML algorithms to identify and prevent various types of fraud in real time. It provides antifraud systems as both complementary and standalone solutions.



Wealthfront offers AI-based robo-advisory services to help clients automatically build their investment portfolios while staying diversified and maximizing returns.

Zest AI

Zest AI

Zest AI provides a fully featured platform for effective credit model management. The software fits the needs of credit organizations of any size, and the company helps with its implementation.

3 Benefits of AI in FinTech

The key drivers impacting the widespread use of AI across the FinTech industry include:

  • Client service improvements

If your clients complain about slow support, AI can be a solution. You can set up AI-based help, including chatbots, virtual assistants, or robo-advisors that never tire of answering the same question, saving employees from burnout. 

What is more important is that AI solutions in FinTech can learn from client interactions, improving the accuracy of their answers over time.

  • Unprecedented analytics capabilities

AI has become a top tool for collecting, normalizing, and making sense of data for clients and employees. It can efficiently process vast amounts of data, beyond what a human would be capable of.

In turn, an organization’s managers can quickly prepare an overview of market trends, customer sentiment, emerging risks, or detailed performance indicators based on AI-generated reports.

For instance, a GPT-based app can search through internally available information and generate context-aware answers with references.

  • Productivity boost

Generative AI alone could result in productivity growth between 0.1 and 0.6 percentage points per year through 2040 across different industries, including finance and banking. Even more, companies could increase productivity between 0.5 and 3.4 percentage points annually between 2024 and 2040 when combining AI with other digital technologies.

AI in FinTech

Using generative AI, FinTech organizations can cope with the shrinking number of available skilled workers. Sustainable business development becomes possible when implementing artificial intelligence in FinTech.

  • Accelerated innovation

Generative AI enables financial organizations to move faster. It shortens innovation cycles, as managers can extract knowledge and turn insights into actionable plans in less time than they could previously without AI. 

Organizations can create and tune specialized models for financial forecasting, tax optimization, accounting, and more. This ensures that each AI model is adapted to a specific department’s needs and generates relevant results.

  • Fraud prevention

AI’s impact on fraud prevention should be neither underestimated nor ignored. Both security departments and cybercriminals use AI for their own purposes. Hence, when security does not integrate AI, criminals get a serious advantage.

AI technologies analyze millions of routine operations in real time, detecting suspicious activities immediately based on fraud patterns and sending them to the support or security department – depending on the severity – for extra checks that let financial institutions avoid costly breaches and losses.

  • Regulatory compliance

Compliance can quickly become a major issue for companies operating internationally. Laws across jurisdictions can contradict each other, while violating them can lead to legal consequences and big fines.

AI solutions can help the legal department monitor regulatory updates and changes, compare the company’s data policy to applicable compliance requirements, or alert on upcoming legal changes in a particular region.

4 Challenges of AI in FinTech

Now, let’s consider challenges and limitations of using AI in FinTech:

  • Costs

Both AI development and maintenance can be expensive. Generative AI engines are based on highly complex models and process vast amounts of data. For instance, GPT-4 was trained on an estimated 100 trillion parameters.

Therefore, it is crucial to consider working with AI experts who can determine a cost-effective data set and implementation path.

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  • Workforce concerns

AI-powered transformation can affect your team members disproportionately. It will accelerate demand for new skills and make some existing roles unnecessary due to increased process automation.

Your organization will have to invest in upskilling to help employees adapt. Retraining could be required for those whose roles are eliminated through AI automation.

  • Rules and regulations

A lack of common standards and regulations for AI use imposes barriers to further adoption. For instance, AI-generated reports and analytics are not accepted for regulatory reporting because providers of AI engines do not disclose their propensity models, and, thus, cannot ensure transparency of results.

While AI providers do not disclose specific algorithms that power their models, concerns remain that unknown technology will corrupt the industry, leading to misuse or law breaking.

  • Data privacy

AI needs vast amounts of data for accurate results. However, this data may contain sensitive information that could be used to identify users. 

Businesses must be extra careful with integrating AI solutions. They need to be clear with end users about the scope of data collection and who will have access to and process collected data in order to avoid situations when a third party receives and processes sensitive client information without a user’s consent. 

Untuned AI models can also produce algorithmic bias, resulting in undesired or unlawful discrimination. The team implementing an AI model must check carefully what categories of information the AI processes and control how this information is used.

  • Bias

AI decisions can lead to unfair treatment or discrimination based on historical data and wrong assumptions. 

This is why regularly and carefully calibrating all coefficients and parameters of an AI model is crucial. Also, it is wise to ensure that negative decisions are checked by a human.

  • Talent scarcity

AI is a new technology that continues to gain ground in finance. The skills and knowledge required to develop it include data science, ML algorithms, software engineering, and more. This makes the search for specialists difficult. The number of engineering talents capable of implementing AI solutions is limited, and competition for top AI experts is high.

5 Future of AI in FinTech

AI is reshaping the future of FinTech through its immense transformational potential. It could add $200B to $340B in value in the banking industry alone, which equals 9% to 15% of operating profits across various segments and functions:

AI in FinTech AI in banking

Here are the key trends financial organizations should consider when reassessing their digital strategies:

AI-based anti-fraud systems

Preventing fraud is one of the top reasons for adopting AI for FinTech solutions, as it provides immediate value.

While analyzing user behavior in real time based on a wide variety of parameters, AI can distinguish patterns characteristic of fraudulent activities. AI helps both to prevent malicious operations and reduce the number of false positive alerts.

Forecasts and risk management

AI algorithms collect and process market data, which they compare to historical data and use to generate reports and analytics on market dynamics. This increases accuracy when forecasting emerging trends or periods of volatility.

Organizations can adapt their portfolios and reconsider investment strategies, reducing the risk of market disruptions going unnoticed.

Robotic process automation

The market for robotic process automation is expected to grow at a compound annual growth rate (CAGR) of 39.4% from 2023 to 2030. The key drivers of this growth include skyrocketing operating costs, a limited number of talents available for hire, and the need to increase productivity under a growing workload.

For instance, robotic process automation enables organizations to overcome such challenges as intensifying competition, disconnected workflows, and inaccurate reporting.

Financial inclusion

Besides enhanced client support, AI can help increase client awareness in financial management. AI-based apps can help clients track expenditures, advise on budget planning and investment, and actively assist in reaching financial goals.

The aim is to help clients understand how to efficiently handle various financial tasks while encouraging them to use banking products regularly and improve their economic decisions.

Talent retention

AI enables HR departments to analyze vast amounts of data related to market conditions, employee performance, and business metrics to justify compensation packages.

It can also help organizations gain insights on employees’ personal development (considering personal values and goals), preventing disengagement and maintaining productivity.

6 Accelerate the Power of AI in FinTech with IT Craft

The success of using artificial intelligence in FinTech solutions largely depends on the quality of the FinTech development services provided.

To grow your FinTech businesses, you must ensure five-star client experiences, which is only possible when your software is fast and secure while generating relevant results.

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IT Craft engineers can help you with this. At IT Craft, you will find a wide variety of AI development services to add AI to your processes, including:

  • AI consulting
  • Full-cycle AI product development
  • Smart chatbots and assistants
  • Generative AI integration
  • AIOps solutions
  • AI security
  • Analytics and forecasting
about it craft

The IT Craft team will quickly take control of the tech part of your product, letting you move forward with:

An AI pilot

While AI has a big impact on the financial industry, an AI project in FinTech should start small by examining how a specific technology fits your use case and your organization’s context.

You need a team that lets your organization gain practical experience cost-effectively, as 90% of pilot projects will never move into production. We will allow you to move forward efficiently after you discover your success formula.

Product implementation and scaling

Once the AI pilot has proven its value, moving to a full-scale project remains challenging. There is no common recipe for success.

This is why you need experts in custom AI implementation who can guide you through the required steps, risks, and pitfalls. Without expertise, implementation costs can be higher than anticipated, risking the success of the project launch.

Updates and maintenance

You need someone to oversee software infrastructure and source code enhancements, ensuring the product is available 24/7. A maintenance team also helps with fixes if something stops working or if accuracy decreases after implemented AI technology is updated. They should also look after software security.

! Conclusion

AI is reshaping the FinTech landscape. Successful adoption of AI technologies will have a decisive impact on the growth of financial organizations in 2024. Those who are able to transform the ways both individuals and organizations handle financial information will win. 

AI can already help with various activities, including financial advising, automated budgeting and investing, fraud and money laundering prevention, and beyond.

AI in FinTech brings significant advancements, focusing on self-service experiences tailored to a specific client’s goals and needs.

Choosing the right AI development technologies will play a key role in the successful integration of AI into an organization’s processes.

Financial organizations should seek an AI development team that can identify challenges and opportunities and focus on long-term goals, helping them reach sustainable results — all while keeping legal compliance and organizational limitations in mind.


How is AI transforming the FinTech industry?

There are several ways in which AI is transforming the FinTech industry: 

  • Improving customer support services
  • Personalizing financial products and services
  • Improving forecasting by collecting, extracting, and making sense of vast amounts of data
  • Detecting and preventing malicious activities
  • Increasing productivity and efficiency of an organization’s workforce
How many FinTech companies use AI?

According to a survey by Moody’s Analytics, 18% of respondents operating in FinTech use AI tools, which is twice the average across all industries.

What are the challenges of AI in FinTech?

Here are the main challenges of using AI for FinTech apps:

  • Implementation and maintenance costs
  • Scarcity of skilled talents
  • Impact of rules and regulations
  • Concerns over the collection and processing of user-related data
What are examples of AI use cases?

The most common FinTech AI use cases include: 

  • robotic process automation
  • virtual client assistants and chatbots
  • automated credit scoring
  • algorithmic trading
  • robo-advisors 
  • digital security and fraud detection
  • reports and forecasting