AI in FinTech: Prominent Use Cases, Benefits and Real-life Examples

Today, financial institutions and investors are increasingly seeking reliable ways to maximize their profits, reduce losses, and stay secure. This is why they turn to AI. Artificial intelligence is a breakthrough technology that transforms how companies manage finances, operations, customer interactions and far beyond these.

As the financial sector and client needs expand, plus technology becomes more advanced, AI adoption becomes inevitable. Hence, the question arises, “which financial AI technologies should you invest in to succeed in the future? ” In this article, we’ll discuss AI in FinTech, focusing on the most prominent AI apps, benefits, barriers to adoption, and real-life examples of successful financial reshaping.

An overview of AI in FinTech: numbers and facts

First, let’s define AI in FinTech. Artificial intelligence is used in FinTech to automate and enhance various financial services and operations. This includes the adoption of machine learning, natural language processing, predictive analytics, and other AI techniques. By doing so, financial companies can enhance efficiency, accuracy, and speed in their processes, ultimately delivering better customer service. 

Now, artificial intelligence is used in FinTech for:

• Fraud detection and prevention;
• Trading and investment;
• Customer service; 
• Robo-advisors;
• Personalized financial service;
• Regulatory compliance;
• Credit scoring and risk management;
• Financial forecasting and planning.

Before we go into details about each use case of AI in FinTech, let’s consider some impressive statistics associated with AI usage:

• The market size of AI in fintech was estimated at $42.83 billion in 2023, rising to $44.08 billion in 2024 (Statista);
• Over 40% of financial institutions already use generative AI (Statista);
• According to a survey conducted in 2024, 43% of respondents indicated that AI led to more operational efficiencies in financial services (Statista).

The statistics indicate growth and significant adoption of AI in the fintech sector. AI technologies are becoming integral to financial operations. This was largely due to the COVID-19 pandemic outbreak, which has been accelerating the change in how people interact with financial services. Also, as data becomes large and deep, advancements in technology have increased the use of artificial intelligence in the finance sector. That is what it has come to.

The importance of AI in FinTech: benefits 

Today, financial companies of all sizes rely on digital solutions to optimize business processes. Below are the key points why it is worth paying attention to AI:

• Better customer experience: AI-powered solutions offer personalized customer support, quicker response times, and tailored financial recommendations, resulting in a better customer experience;
• Better analytics: the use of well-trained ML models enables businesses to obtain deeper insights from their data for predictive analytics, revealing hidden patterns and trends for informed financial decisions;
• Better security: AI detects unusual patterns, anomalies, and security threats, ensuring that customer data and internal systems are protected;
• Cot-saving: by automating financial processes and optimizing resource allocation, AI helps reduce operational costs and improve overall profitability;
• Improved risk management: AI analyzes big datasets in real-time for risk assessment, enhancing decision-making and regulatory compliance for financial stability.
• Automation of financial processes: AI automates traditionally manual banking processes, improving efficiency, accuracy, and speed of operations;
• Market innovation: competition drives continuous innovation in AI technologies, leading to market growth and enabling advanced fintech solutions.

Despite these promising benefits, AI in FinTech faces some barriers when it comes to its implementation and integration.

Reasons that prevent AI adoption in FinTech

Since AI in FinTech continues to prosper, it is crucial to recognize and tackle the potential risks associated with it. Here are some of them:

Data privacy and protection: handling sensitive financial data requires stringent security measures to prevent breaches or unauthorized access. Managing and processing data with AI systems becomes more challenging as they must comply with regulations like GDPR and CCPA.

Solution:

• You can use advanced firewalls and diverse cybersecurity measures at every step of AI adoption to monitor and prevent unauthorized access;
• Conduct regular security audits to identify and fix vulnerabilities;
• You must ensure that AI systems comply with relevant data protection laws, such as GDPR in Europe and CCPA in the US. You also need to conduct regular audits to ensure ongoing compliance with data protection laws.

Ethical and legal issues: AI models are trained on many diverse data sets, so they can generate biases, untruths, or misleading information, resulting in unfair outcomes.

Solution:

• Develop and implement ethical guidelines for AI usage.
• Conduct regular audits and monitoring of AI systems for ethical compliance.
• Ensure transparency and explainability of AI decisions.

Lack of customer trust: AI models often operate as “black boxes,” making understanding how they arrive at specific decisions challenging. In finance, explaining AI-driven choices to customers, regulators, and stakeholders is critical to building trust.

Solution:

• Use techniques such as Local Interpretable Model-agnostic Explanations (LIME) or SHapley Additive exPlanations (SHAP) to generate explanations for individual predictions or decisions of a model;
• Provide comprehensive documentation on the AI model’s architecture, data sources, preprocessing steps, and evaluation metrics;
• Regularly provide reports summarizing accuracy metrics, bias assessments, and updates or changes to AI models.

Lack of skills and resources: implementing and managing AI systems requires strong expertise, and without it, finance companies cannot effectively deploy and advance AI technologies.

Solution:

• You need to invest in training and reskilling workers;
• You can hire short-term experts and consultants for specific projects;
• Use automated machine learning (AutoML) tools and frameworks that allow coworkers with limited AI expertise to build and deploy machine learning models.

High implementation costs: AI technologies and infrastructure require significant investments, making them too expensive for most companies.

Solution:

• Start with small-scale projects to test the feasibility and effectiveness of AI solutions before committing to large-scale investments;
• Use cloud-based AI services and platforms (such as AWS Google Cloud) to reduce on-premise hardware and infrastructure costs;
• Consider outsourcing AI development companies that can provide expertise and resources at a lower cost than building in-house capabilities.

A well-developed strategy for solving one of these problems will help financial services integrate AI technologies more effectively and fully exploit their potential.

Prominent AI applications in financial services

Despite these obstacles, AI continues to transform the financial industry, offering prominent opportunities for business growth – let’s consider them.

Fraud detection systems

In view of the continuous growth of cyber threats, fraud detection is a top priority for banks and financial institutions. Since finance companies store a large amount of critical data online, the risk of security breaches increases. Consequently, many businesses, such as Feedzai and Kount, are implementing AI solutions to improve security measures and prevent fraud more effectively.

 AI-driven fraud detection systems analyze massive amounts of financial data, such as historical transactions and customer patterns, at speeds far beyond human capabilities. They can detect suspicious activity or anomalies, such as sudden large withdrawals or atypical spending patterns, before they happen. Moreover, AI fraud detection systems are highly effective in adapting to new fraud techniques, which people often remain uninformed about, helping them stay ahead of hackers.

Thus, fraud prevention operations with AI become faster, more accurate, and more cost-effective than ever before. It doesn’t mean that AI fraud detection systems will replace traditional security ones; instead, they will complement each other for a superior level of systems security.

Algorithmic trading 

Traditionally, investment strategies were mainly based on human intuition, experience, and market analysis. However, today, AI is rapidly advancing in trading, particularly in high-frequency trading (HFT). It is a type of algorithmic trading that relies on execution speed to profit from small price fluctuations. AI-based algorithms analyze enormous amounts of financial data and execute trades in real-time with incredible speed and accuracy. 

These systems are designed to respond to market changes, news releases, or other data inputs affecting the market and execute trades accordingly. This automated approach mitigates the risks associated with human emotions in trading, ensuring that financial decisions are based purely on analytical insights. As a result, financial companies and banks can maximize their returns and minimize losses.

Credit scoring and risk management

AI-based credit scoring (willingness to repay debts) is one of the most promising and relevant applications of AI in the finance sector. According to McKinsey, AI has significantly enhanced credit-approval turnaround times and application approval rates. Financial institutions will save $31 billion by 2030 using AI in their underwriting systems.

Traditional credit scoring methods rely heavily on the collection of historical financial data, such as credit history, income level, and existing debts. They often take two to three weeks to complete and require the effort of many specialists. At the same time, AI-based credit scoring goes beyond these methods.

AI-based credit scoring apps can analyze huge datasets, including social media activity, transaction patterns, total income, credit history, work experience, and more. By doing so, ML models build a more extensive and detailed profile of a borrower. Based on these profiles, ML models identify patterns that may indicate creditworthiness or potential credit risk. Thus, AI-based credit scores let companies assess potential borrowers quickly and accurately, reduce loan risks, and even provide tailored loan options.

Accounting and reporting

One of the main use cases of AI in FinTech is in accounting and reporting. AI-enhanced solutions, particularly those powered by robotic process automation (RPA), significantly improve the efficiency and accuracy of financial tasks, such as:

• Document processing and data entry: AI-powered tools can process large volumes of documents much faster than humans, with near-zero errors, speeding up routine tasks;
• Profit and loss (P&L) statements: AI systems can automatically compile P&L statements by summarizing a company’s revenues and expenses;
• Accounts payable and receivable: AI systems can process invoices, send payment reminders, and validate financial data, speeding up transactions and minimizing errors.

Beyond that, AI-enhanced RPA solutions can understand the context and handle exceptions in reports. For example, if financial reports contain inconsistencies, AI systems can detect and address these anomalies more effectively. This ensures that the financial data remains accurate and reliable. To sum up, AI helps finance companies manage their finances more efficiently, accurately, and cost-effectively. Its ability to streamline a wide range of financial processes makes it a valuable tool for modern financial institutions.

Regulatory compliance

Financial institutions must comply with numerous regulations and policies to ensure the integrity of their systems and to protect their customers. Here, AI-driven systems also come into play. This technology can automate compliance processes, monitor transactions, and detect potential violations. Also, ML models help them identify regulatory changes quickly, interpret their implications, and adjust compliance processes accordingly. 

Apart from that, AI enhances efficiency in compliance processes by automating tasks like Know Your Customer (KYC) and Anti-Money Laundering (AML). In KYC, machine learning models analyze customer profiles, behavior, and transaction history to assess risk levels and detect potentially suspicious activities. In AML, ML models monitor transactions in real-time to identify unusual patterns, anomalies, and potential money laundering activities. 

Chatbots for customer service

The adoption of chatbots by banks and financial organizations isn’t merely a trend; it’s a strategic move to offer more user-friendly and hyper-personalized services. FinTech companies leverage chatbots to provide round-the-clock customer support, addressing queries ranging from budgeting tips to fee reduction recommendations. These interactions are conducted swiftly, accurately, and without human intervention, leading to significant savings in operational expenses.

Moreover, AI-based chatbots assist customers in navigating a bank’s offerings and deliver targeted calls to action. This optimizes efforts to attract new customers and reduces churn rates. Additionally, AI chatbots find applications in voice-based or computer vision face authentication. Users can seamlessly integrate them into home banking or trading apps to validate transactions or access sensitive data. Overall, chatbots based on AI help FinTech companies save costs and deliver personalized, efficient customer service driving loyalty among users. 

Robo-advisors

Robo-advisory in FinTech is an online platform that provides financial and investment planning services through automated algorithms with no human intervention. Simply put, it is a financial advisor that uses an algorithm to automatically select investments for you. Also, robo-advisors are less expensive than traditional financial advisors. 

The robo-advisor gathers information about a client through an online survey and automatically invests for the client based on that data. It assesses the user’s financial goals, level of risks, and investment capabilities to build a personalized portfolio. Users enter their investment goals, and the system automatically determines the most profitable investments. Then, robo-advisors continuously monitor the portfolio and rebalance it to maintain the desired asset allocation. 

For example, an individual at age 30 wants to save $500,000 by retirement. He inputs this goal into a robo-advisor app. The platform divides its investments among assets like stocks, bonds, and real estate to help reach this goal. After that, a robo-advisor adjusts the investment strategy based on current market trends to find the best mix for long-term growth.

Some examples of the best robo-advisors in 2024:

• Wealthfront: a prominent robo-advisor that offers automated investment management, financial planning tools, and tax-efficient investing strategies;
• Betterment: one of the most well-known robo-advisors for experienced and beginner investors, offering goal-based investment tax-saving solutions with multiple portfolio choices (income, crypto, and cash accounts);
• M1 Finance: it is a one-of-a-kind investment portal suitable for strategic investors who want access to customized expert theme-focused strategy portfolios;
• Acorns: it focuses on micro-investing by rounding up everyday purchases to the nearest dollar and investing the difference into a diversified portfolio.

Real-life examples of artificial intelligence in FinTech

Here are some real and quite successful examples of the integration of artificial intelligence in a financial business;

Feedzai: a fraud detection platform

Feedzai leads the market in tackling financial crime through AI technology. The company introduced the RiskOps platform, which uses AI and ML models to detect fraudulent activities and suspicious anomalies for banks, retailers, and payment providers worldwide.

Moreover, Feedzai has introduced the groundbreaking Fairband framework to address AI bias and ensure fairness in AI models. This framework features a patent-pending AutoML (Automated Machine Learning) algorithm that discovers less biased machine learning models without requiring any additional model training cost. On average, Feedzai Fairband enhances model fairness by up to 93%. This improvement is crucial for ensuring that AI systems don’t discriminate unfairly based on factors such as age, gender, ethnicity, or location.

Axyon AI: an investment platform 

Axyon AI is an Italian FinTech company that leverages AI to enhance investment management. One of its key offerings is IRIS, an investment management platform that utilizes advanced ML algorithms to improve investment decision-making.

IRIS analyzes historical data, market trends, and other relevant factors to provide predictive insights into asset performance, risks, and potential opportunities. The platform suggests optimal asset allocations based on risk tolerance, return objectives, and market conditions. Axyon AI partners with industry giants like Microsoft, IBM, and Nvidia, providing its services to commodity traders and asset managers.

Kasisto: a conversation AI platform

Kasisto is a company that specializes in conversational AI solutions for the banking and finance industry. One of its impressive products is KAI-GPT  – the first banking industry-specific generative large language model. With natural language processing and advanced ML algorithms, KAI-GPT delivers intuitive, personalized, 24/7 customer support, helping customers make informed financial decisions.

This conversational AI platform is widely adopted in the banking sector to elevate client experiences and deliver personalized financial services. By offering customers self-service alternatives and tailored solutions, KAI allows banks to reduce the load on contact centers while simultaneously improving customer service.

Upstart: a credit scoring platform 

Upstart operates as an AI lending platform, partnering with over 100 banks and credit unions to predict consumer creditworthiness using non-traditional variables. The platform uses predictive modeling and ML algorithms to analyze thousands of factors to predict a borrower’s probability of default, providing lenders with more accurate credit scoring.

This predictive modeling allows Upstart to approve more borrowers while maintaining the same loss rates. It also helps reduce fraudulent applications. By leveraging AI, Upstart improves credit decision accuracy, allowing it to lend deeper down the credit spectrum without significantly increasing losses.

Conclusion 

The financial domain is experiencing unprecedented growth and development thanks to the AI technology. As you can see, it brings a wide range of solutions to FinTech, including but not limited to fraud prevention, credit scoring, algorithmic trading, risk assessment, and personalized custom service. This proves once again that the future of AI in FinTech holds immense promise and potential.

However, implementing AI technologies requires careful planning, skilled ML and data scientist professionals, robust infrastructure, seamless integration, deployment, optimization, and many more. As a software development company with extensive experience in developing AI solutions, SoftTeco provides ML development services to help financial companies address unique business issues in a professional manner. For this, our specialists will define the best ML strategy, determine feasibility, and help you select the most effective tools and technologies for your customized AI in FinTech development strategy.

Expert Opinion

AI is making significant improvements in FinTech by enhancing fraud detection, credit scoring, algorithmic trading, and customer service through chatbots and robo-advisors. These advancements lead to better efficiency and customer experiences, indeed.

However, challenges like data privacy concerns, ethical issues, skill shortages, and high implementation costs remain. Even with these obstacles, AI’s advantages—such as increased accuracy and improved service—make it a definitely valuable investment for the future of financial technology.

Head of Data Science and Machine Learning Department at SoftTeco

Alexander Gedranovich