Fintech has revolutionized the way people access, manage, and grow their money, making financial services more accessible, convenient, and efficient than ever before. With new technologies emerging constantly, the possibilities in fintech seem almost limitless!

But what’s even more exciting is the role that artificial intelligence (AI) is playing in this evolution. The fintech AI market, valued at a whopping $7.91 billion in 2020, is expected to soar to an incredible $26.67 billion by 2026, growing at a compound annual growth rate (CAGR) of 23.17%. This explosive growth showcases the immense potential AI holds in transforming the financial landscape, and the world is taking notice.

Imagine credit scoring models that are more accurate than entire teams of analysts, chatbots that offer personalized financial advice at any time of day, and algorithms that detect fraudulent transactions faster than ever before. All of this—and much more—is being made possible by the incredible advancements in AI.

As AI continues to weave itself into the fabric of fintech, the benefits are clear. Enhanced efficiency, greater personalization, reduced costs, and improved customer experiences. But like any powerful tool, AI also brings challenges and risks that need careful consideration. As we navigate the future of fintech, understanding both the opportunities and pitfalls will be crucial for leveraging AI's full potential responsibly and effectively.

Ready to explore the fascinating world of AI in fintech? Let’s dive in!

History and evolution of AI in fintech

The journey of AI in fintech is like an action-packed story, filled with breakthroughs, unexpected twists, and groundbreaking developments that have reshaped the entire financial industry. While AI might feel like a buzzword of the 21st century, its roots in the fintech sector trace back several decades.

It all began in the 1980s, when early adopters in finance began exploring AI's potential for automating routine tasks and optimizing complex decision-making processes. One of the first notable uses of AI in finance was the introduction of expert systems. These were software programs designed to mimic the decision-making ability of a human expert in fields like credit scoring and loan underwriting. Although primitive by today’s standards, these systems laid the groundwork for the more sophisticated AI applications we see today.

In the 1990s, the rise of the internet catalyzed a wave of digital transformation across industries, including finance. AI technologies like neural networks started gaining traction, primarily for fraud detection in banking. Banks and financial institutions began employing AI to detect unusual patterns in transaction data, effectively flagging potential fraudulent activities faster and more accurately than human analysts ever could.

The real game-changer, however, came in the 2010s with the advent of big data and the proliferation of digital devices. As the amount of data generated by digital transactions exploded, AI found new life in machine learning and data analytics. Fintech companies began leveraging these technologies to enhance predictive analytics, offering more personalized financial services based on individual behavior, habits, and preferences. The likes of robo-advisors, which provide automated, algorithm-driven financial planning services, started to make their mark during this period.

Today, AI in fintech is not just a tool but a critical enabler of innovation. Advances in natural language processing (NLP), computer vision (CV), and deep learning have unlocked even more sophisticated applications—from AI-powered chatbots that handle customer inquiries 24/7 to complex algorithms that predict stock market trends and optimize investment portfolios.

Now, fintech companies use AI not only to automate and enhance existing processes but also to develop entirely new business models. Digital banks, cryptocurrency trading platforms, and decentralized finance (DeFi) ecosystems are all leveraging AI to offer cutting-edge financial products that were unimaginable a decade ago.

The evolution of AI in fintech is far from over. As new technologies emerge and evolve, AI's role in finance will only continue to expand, driving further innovation and transformation in ways we can hardly predict today. So, let’s explore how these advancements are being applied and what the future might hold for this exciting convergence of finance and technology!

Major applications of AI in fintech with examples

AI is making waves across the fintech world, transforming how financial services are delivered, managed, and experienced. Here are ten game-changing applications of AI in fintech, with real-life examples to bring them to life:

Risk assessment and management

AI has completely changed how financial institutions assess and manage risk, using vast datasets to offer real-time insights and smarter decision-making. Traditional methods rely on narrow sets of data, but AI brings a more comprehensive view, analyzing everything from transaction patterns to real-time behaviors. Companies like Upstart, for example, are leveraging AI to build more accurate credit models, extending credit to more people while keeping risk under control.

• Credit scoring and underwriting. AI goes beyond traditional credit scores, using diverse data points to provide more precise credit assessments.
• Fraud detection and prevention. AI’s ability to monitor vast amounts of data in real time makes it highly effective at catching fraudulent activities before they escalate.
• Behavioral risk assessment. AI analyzes real-time financial behavior, improving the accuracy of risk assessments.
• Algorithmic risk management. AI simulates various scenarios to help institutions manage risk in unpredictable market environments.

Personalized financial services

AI enables financial institutions to deliver more personalized services, tailored to individual needs and behaviors. Unlike traditional, one-size-fits-all models, AI systems can dig deep into user data, preferences, and behaviors to offer truly personalized advice. Wealthfront, for example, uses AI to craft tailored investment strategies that align with each client’s financial goals and risk tolerance.

• Customized investment advice. AI-powered platforms create personalized investment strategies based on user data and real-time market insights.
• Tailored product recommendations. AI analyzes spending habits and preferences to recommend financial products that suit the individual’s needs.
• Personalized savings and budgeting plans. AI-driven assistants suggest savings plans and budgeting strategies tailored to personal spending habits.
• Enhanced customer engagement. AI personalizes customer interactions, making service more relevant and engaging.

Fraud detection and prevention

AI has become a key player in combating fraud, especially as financial transactions become more digital and more complex. Traditional fraud detection systems often trigger too many false positives, but AI’s ability to learn and adapt to new data makes it far more accurate. Large financial institutions like Visa and Mastercard use AI to monitor thousands of transactions per second, catching fraud in real time with remarkable precision.

• Real-time transaction monitoring. AI analyzes financial transactions as they occur, instantly identifying suspicious activity.
• Adaptive fraud detection models. AI learns from new data, constantly refining its ability to detect and prevent fraud.
• Context-aware anomaly detection. AI distinguishes between legitimate unusual transactions and actual fraud.
• Customer authentication. AI enhances security with biometric authentication systems, such as voice or facial recognition.

Algorithmic and high-frequency trading (HFT)

In the world of trading, speed and data are everything. AI has become the backbone of high-frequency trading, where algorithms analyze market data and execute trades in fractions of a second. Firms like Two Sigma rely on AI to develop complex trading strategies that take advantage of tiny market fluctuations, executing thousands of trades in the blink of an eye.

• Data-driven trading strategies. AI analyzes vast datasets to identify profitable trading opportunities in real time.
• High-frequency trading (HFT). AI enables trades to happen in milliseconds, optimizing for tiny price movements.
• Market trend analysis. AI predicts market trends by constantly analyzing incoming data and adjusting strategies.
• Risk management in trading. AI systems help traders balance risk and reward, adjusting strategies as market conditions change.

Automated customer support and AI chatbots

AI-powered chatbots are now a staple in customer support, providing instant responses and resolving common issues without human intervention. Banks like Bank of America have successfully implemented AI chatbots to handle customer inquiries, provide personalized advice, and complete transactions around the clock. These systems continue to learn from each interaction, getting better at helping customers over time.

• 24/7 customer support. AI chatbots provide round-the-clock service, answering customer queries and resolving issues in real time.
• Personalized customer interaction. AI systems tailor responses based on previous conversations and user data, making customer interactions more relevant.
• Continuous learning and improvement. Chatbots get smarter with every interaction, continuously improving their ability to assist customers.
• Cost reduction. AI helps reduce the costs of customer service by automating routine inquiries, freeing up human agents for more complex tasks.

Anti-money laundering (AML) compliance

Staying compliant with anti-money laundering regulations is a massive task for financial institutions, but AI is making it easier and more effective. Traditional AML processes are slow and prone to error, but AI-driven systems can monitor transactions in real time and flag suspicious behavior much faster. Major banks like HSBC have adopted AI to automate much of their AML processes, reducing false positives and improving compliance accuracy.

• Transaction analysis. AI scans transaction data to detect unusual patterns that may indicate money laundering.
• Behavioral analysis. AI monitors customer behavior to identify potentially illicit activity.
• Reduced false positives. AI systems reduce the number of false alarms, allowing compliance teams to focus on real threats.
• Efficient compliance reporting. AI automates the generation of compliance reports, improving efficiency and accuracy.

Portfolio management and optimization

AI has brought portfolio management into the future by enabling real-time, data-driven adjustments to investment strategies. Platforms like BlackRock’s Aladdin system monitor financial markets continuously, making adjustments to client portfolios based on current conditions and predictions. AI-driven wealth management tools can now provide a level of customization and responsiveness that was previously impossible, offering clients more value.

• Real-time portfolio adjustments. AI tracks market conditions in real time and adjusts portfolios automatically.
• Personalized investment strategies. AI designs investment strategies tailored to individual goals and market trends.
• Predictive market analysis. AI predicts future market movements based on historical and current data, enabling smarter investment decisions.
• Cost-effective wealth management. AI democratizes access to wealth management services by making sophisticated strategies more affordable and widely available.

Real-time payment processing

AI is dramatically improving payment processing systems by making transactions faster, more secure, and easier to manage. Companies like Stripe have integrated AI to streamline tasks like transaction monitoring and fraud detection. AI can even provide dynamic pricing models, offering real-time exchange rates or pricing for customers, all while improving security through biometric payment options.

• Automated payment workflows. AI automates transaction monitoring and reconciliation, reducing errors and speeding up the payment process.
• Dynamic pricing models. AI analyzes real-time data to offer the most accurate and competitive pricing for services like foreign exchange.
• Biometric payment security. AI-driven biometric systems enhance security, making payments faster and more secure with facial or fingerprint recognition.

Regulatory compliance and RegTech

AI is revolutionizing regulatory compliance by automating complex monitoring and reporting tasks. With regulations constantly changing, fintech companies rely on AI to ensure they stay compliant. AI-powered systems like those used by ComplyAdvantage track regulatory changes and automatically apply them to internal compliance processes. This not only reduces operational costs but also ensures institutions remain on the right side of the law.

• Automated monitoring and reporting. AI monitors transactions and generates real-time compliance reports, ensuring adherence to regulations.
• Dynamic regulatory updates. AI systems adapt to new regulations automatically, keeping financial institutions compliant with evolving rules.
• Data analysis for compliance. AI analyzes large datasets to detect potential regulatory breaches early on.
• Reduced operational costs. By automating compliance tasks, AI reduces the cost of staying compliant, allowing teams to focus on higher-level challenges.

Financial forecasting and decision-making

AI takes financial forecasting beyond historical data, providing businesses with predictive insights based on vast amounts of information. For example, KPMG has developed an Intelligent Forecasting platform that uses AI and advanced analytics to provide businesses with accurate financial predictions, helping them set data-driven financial targets, improve profitability, and identify key business drivers.

• Revenue and expense forecasting. AI analyzes past performance to provide accurate predictions of future financial outcomes.
• Market trend analysis. AI continuously monitors and analyzes market data to predict trends and inform strategy.
• Customer behavior forecasting. AI identifies patterns in customer behavior, helping businesses anticipate needs and offer relevant products or services.
• Strategic decision support. AI transforms data into actionable insights, helping companies make more informed, strategic decisions.

AI is undeniably changing the game in fintech. As the technology matures, we’re seeing more customer-centric, efficient, and secure systems taking shape, creating real value for both businesses and users.

But with all these advancements come new challenges. In the next section, we’ll dive into these challenges, exploring what fintech companies are up against and how they’re working to overcome these obstacles.

Nothing is perfect. What are the challenges of using AI in fintech?

While AI is making fintech smarter, faster, and more personalized, it’s not without its hiccups. For every advantage, there's a challenge lurking around the corner. Here are some of the biggest hurdles AI in fintech needs to clear:

Bias in AI Systems

AI is only as good as the data you feed it. If that data has biases baked in—like historical prejudices, incomplete datasets, or systemic inequalities—AI can end up making biased decisions. This can affect everything from credit scores to loan approvals, unfairly disadvantageing certain groups, like minorities or low-income individuals.

Imagine a credit scoring AI that unintentionally favors applicants from well-off areas because the data it's trained on links those zip codes with lower default rates. That could mean fewer opportunities for people in less affluent neighborhoods who are just as creditworthy.

Poor data quality

AI thrives on data—clean, accurate, and up-to-date data. But the financial world is filled with messy, unstructured data like emails, contracts, or transaction descriptions. If the data is bad, the AI’s output will be, too. Inaccurate data can lead to faulty models, which in turn make poor decisions.

A fraud detection AI trained on outdated data might miss new types of fraud, while improperly cleaned data could lead to a cascade of errors that mess up both the business and the customer experience.

Lack of transparency (the black-box problem)

AI models, especially the super complex ones like deep neural networks, can be mysterious. They often work as "black boxes," where even the developers can't fully explain how they arrive at a specific decision. This lack of transparency can be a big problem in fintech, where regulations require clear explanations for decisions, such as why a loan was denied or an account was flagged.

Say a bank uses an AI model to approve or deny loans, but can't explain why it denied someone. Not only could this violate regulations, but it also erodes trust with customers who feel unfairly treated.

Regulatory compliance and legal challenges

Navigating the tangled web of financial regulations is tricky enough, but AI adds a new level of complexity. Different countries have different rules on data privacy, anti-money laundering (AML), and fraud detection, and many of these require AI models to be explainable, auditable, and compliant. Keeping AI in line with these rules can be a headache, especially for companies operating across borders.

An AI-driven credit scoring tool in Europe needs to comply with GDPR, which includes the right for people to understand how their data is used. If the AI isn’t transparent, it could breach these rules, leading to hefty fines.

Security of financial data

AI is a double-edged sword when it comes to security. It can improve fraud detection, but it also brings new vulnerabilities. Cybercriminals are getting smarter, targeting AI systems, and looking for ways to manipulate data inputs to trick the algorithms (think of this as adversarial attacks). Protecting these systems from such clever tactics is a growing concern.

Hackers could subtly alter the data fed into an AI fraud detection system, confusing it into letting fraudulent transactions slip through while flagging legitimate ones as suspicious.

Costs of implementing AI in fintech

AI is powerful, but it doesn’t come cheap. Building, training, and maintaining AI models requires a lot of data, specialized talent, and continuous updates. For smaller fintech companies and startups, these costs can be a major barrier, making it tough to compete with the big players.

A startup wanting to use AI for personalized financial advice might find the costs of acquiring data, developing models, and meeting regulatory requirements overwhelming.

Ethical concerns

AI in fintech raises ethical questions, especially around data privacy, consent, and fairness. Financial decisions—like who gets a loan or a credit line—can have a massive impact on people's lives. If AI isn’t used responsibly, it could lead to discrimination or even financial exclusion for some groups.

An AI that unknowingly uses biased historical data could deny loans more often to minority groups, reinforcing existing inequalities and sparking serious ethical concerns.

Customer trust and acceptance

AI can be incredibly powerful, but if customers don’t trust it, it won't get far. Many people are still wary of AI, especially when it involves their money. Concerns over privacy, data security, and the fear that an algorithm might make a mistake or be biased can slow down adoption.

A customer might be reluctant to use a robo-advisor for investments, worrying that an algorithm won’t understand their unique financial goals or could make a mistake that costs them money.

Integration with legacy systems

Many banks and financial institutions still run on legacy systems that weren’t designed to handle AI. Integrating new AI technologies with these old systems can be a nightmare, often requiring significant overhauls or costly replacements.

A traditional bank might want to use an AI-powered fraud detection system, but its outdated core banking software can’t support the new technology without major upgrades.

The rapid pace of technological change

AI technology is advancing at lightning speed. Keeping up with the latest innovations can feel like a race where the finish line keeps moving. Fintech companies need to invest continuously in research, development, and training to stay ahead of the curve.

A fintech company could spend big on an AI platform only to find a newer, better technology emerging soon after, forcing them to invest more to stay competitive.

Yes, AI in fintech brings its share of challenges—from concerns about data privacy and fairness to regulatory hurdles and ethical questions. But these challenges are not insurmountable. The industry is actively working on solutions to make AI safer, more transparent, and fairer. With better regulations, ongoing improvements in model training, and increasing efforts toward AI explainability, the future of AI in fintech remains bright and full of potential.

But these are merely words. How do we, as an AI development company, contribute to making routine financial operations more efficient and convenient? 

Data extraction: The backbone of AI in fintech

When it comes to fintech, data is like the fuel in the tank, and without it, you’re not going anywhere. But here’s the catch. getting that data—clean, accurate, and ready to use—is a monumental task. Let’s break down why data extraction with intelligent document processing (IDP) in fintech is such a big deal and why it’s easier said than done.

Data deluge

Fintech companies are drowning in data. Every single transaction, account opening, loan application, and customer inquiry generates mountains of data. This data isn't just numbers on a spreadsheet; it’s a mishmash of formats—PDFs, emails, images, handwritten forms, scanned documents, web data, mobile app logs, and more. This “data deluge” creates a huge challenge for AI, which needs structured and clean data to function properly.

Unstructured data: A tough nut to crack

A huge chunk of data in financial services is unstructured, meaning it doesn’t fit neatly into rows and columns. Think of things like customer emails, PDFs of loan agreements, transaction notes, or social media mentions. Unstructured data is like the wild west for AI. full of potential but incredibly tricky to tame.

Data quality and consistency issues

Even when data is extracted, its quality can be all over the place. Inconsistent formats, missing fields, outdated information, and errors introduced during manual entry are common problems that can throw a wrench in AI algorithms. If the data is junk, the insights the AI provides will be junk, too.

Manual effort

Ironically, getting AI systems to work often requires a lot of manual effort. Humans are needed to label data, correct errors, and ensure the extracted information is accurate. This labor-intensive process can slow down the development and deployment of AI solutions, driving up costs and delaying benefits.

The “almost, but not quite” issue: Technological limitations

Even the best AI tools for data extraction aren’t perfect. Optical character recognition (OCR) technology, which is often used to extract text from images, can struggle with poor-quality scans, handwritten notes, or unusual fonts. Natural language processing (NLP) models may misinterpret context or fail to understand industry-specific jargon, leading to errors.

Overcoming the data extraction challenge

Instead of just improving OCR, we reimagined the entire process of extracting and understanding document data. Our solution was developed with advanced machine learning techniques, allowing it to not only recognize text but also grasp the structure of documents. Using Named Entity Recognition (NER), our model identifies and categorizes key fields like invoice numbers, dates, and company names, minimizing manual corrections.

For our client Flexidea, this meant automating invoice processing across multiple formats and languages, including Latvian and English. By training specialized models for language nuances, we created a system that handles poor-quality images and non-readable PDFs with ease.

The result? Flexidea drastically reduced errors, improved speed, and freed up employees to focus on higher-value tasks. With scalable capabilities and multi-language support, our solution goes beyond simple text extraction—it adapts, learns, and grows alongside business needs.

With this solution in place, Flexidea can now expand its reach and further streamline operations, reinforcing the backbone of their fintech platform.

We can do the same for you

Whether you deal with invoices, forms, or any other document types, we can tailor our ML-powered solution to your specific needs. Tensorway offers custom model training to handle your unique document formats and business data, ensuring accurate and efficient data extraction. Get in touch with us to explore how we can help automate your document processes and take your operations to the next level.

Future trends of AI in fintech. What’s next?

As AI continues to weave itself into the fabric of fintech, the future looks both promising and challenging. The road ahead is filled with innovation, and we can expect to see AI driving even more profound changes across the financial landscape. Here are some of the key trends that are shaping the future of AI in fintech:

More personalized and predictive financial services

AI’s ability to analyze vast datasets in real time will lead to even more personalized and predictive financial services. Imagine a world where your bank knows what you need before you do—offering customized investment options, savings plans, or credit products just as you begin considering them. AI will drive hyper-personalization, turning financial services into tailored experiences that anticipate customer needs and preferences at every turn.

Autonomous finance. A new era of automation

We are moving towards a future where AI could take over many financial decisions and tasks currently handled by humans. Autonomous finance, where intelligent systems manage everything from budgeting to investments and bill payments, is becoming a reality. These systems will act like personal CFOs, constantly learning and adapting to optimize individual financial health and security.

Enhanced security and fraud prevention

As cyber threats become more sophisticated, AI will play an even greater role in securing financial systems. Expect to see more advanced AI models that detect fraud, prevent cyberattacks, and ensure data privacy, all while reducing false positives that frustrate customers. AI will not only respond to threats but predict them, shifting the security focus from reactive to proactive.

Explainable and transparent AI models

The "black box" problem of AI will become less of an issue as the demand for transparency grows. Fintech companies will need to make their AI models more explainable and understandable to regulators, customers, and stakeholders. This trend towards “explainable AI” will help build trust, ensure compliance, and make it easier for businesses to adopt AI without fear of regulatory backlash.

Regulatory technology (RegTech) evolves with AI

Regulatory compliance is complex and costly, but AI-driven RegTech solutions will become even more sophisticated, automating compliance processes, detecting anomalies, and ensuring adherence to evolving regulations worldwide. As regulatory scrutiny increases, fintech firms will lean more heavily on AI to manage compliance efficiently and effectively.

AI-powered financial inclusion

AI has the potential to bridge gaps in financial inclusion by providing credit scoring models that rely on alternative data, offering micro-loans to underserved communities, and enabling digital banking services in remote areas. The future of fintech will see AI breaking down traditional barriers to financial access, reaching those who have been left out of the financial system.

Wrapping up: The path forward

The future of AI in fintech is bright, but it’s also filled with challenges that need to be addressed. As AI continues to mature, it will drive unprecedented levels of personalization, security, and efficiency in financial services. But with great power comes great responsibility. Fintech companies will need to navigate issues like data privacy, bias, and regulatory compliance carefully to build trust and drive adoption.

The next decade will be pivotal for AI in fintech. We’re on the brink of an era where AI doesn’t just support financial services—it fundamentally transforms them. From providing seamless customer experiences to powering autonomous financial decisions, AI will redefine what is possible in finance.

For businesses, the path forward will require a balanced approach. embracing AI’s potential while being mindful of its risks. Those who can master this balancing act will be well-positioned to lead the way in the future of finance, delivering smarter, faster, and more inclusive financial services to everyone.

So, whether you’re a fintech startup looking to innovate or an established bank aiming to stay relevant, the message is clear. The time to invest in AI is now. The journey might be challenging, but the destination promises a revolution in how we think about money, markets, and the future of finance.