AI in FinTech in 2024: 7 Ways AI is Revolutionizing the Industry

AI in FinTech in 2024: 7 Ways AI is Revolutionizing the Industry - 1
Paul Francis

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    Financial technology (FinTech) and artificial intelligence (AI) are two cutting-edge innovations that have recently altered the financial sector. AI can boost economic growth by 26% and financial services revenue by 34%. It is pivotal for FinTech’s rapid advancements, enabling financial institutions and businesses to analyze vast amounts of data, identify patterns, and make data-driven decisions efficiently.

    Artificial intelligence has carved a niche for itself in the world of fintech, leveraging its prowess to perform tasks like crunching financial data, enhancing customer service, streamlining supply chain management, dispensing shrewd trading counsel, and much more. When it comes to best practices in applying AI within the banking sector, looking at case studies is a smart move. Among these examples, it’s evident that chatbots are at the forefront, holding the reins on the customer-facing front.

    In this article, you will learn about the impact of artificial intelligence on FinTech and how it transforms the industry in 2023. We will share the expertise of Uvik, a staff augmentation company with proven expertise in AI/ML development, to review AI in the FinTech market size, seven ways these technologies revolutionize the industry and the future of AI in the sector. If you are still determining if your FinTech project requires AI, keep reading as we highlight the value of artificial intelligence for FinTech.

    AI in the FinTech Industry Market Size

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    The market for AI in FinTech is anticipated to be worth $42.83 billion in 2023 and grow to $49.43 billion by 2028, with a CAGR of 2.91% over the forecast period (2023-2028). It is segmented by type (solutions and services), deployment type (cloud and on-premise), application type (chatbots, credit scoring, quantitative and asset management, and fraud detection), and geography (North America, Europe, Asia-Pacific, and Rest of the World).

    The solution segment dominates the market, accounting for 77.5% of the global revenue. These include applications for mobile banking, digital loans, insurance, credit scores, buying and selling activities, and asset management. North America leads the market for AI in the Fintech due to prominent AI software and system vendors, combined financial institution investment in AI projects, and widespread adoption of AI in FinTech solutions.

    The benefits of AI in fintech

    Artificial intelligence in FinTech redefines the industry, revolutionizing traditional financial practices and paving the way for innovative advancements. Using artificial intelligence for fintech reshapes the sector, creating a more efficient, secure, and inclusive financial landscape for businesses and customers. This section will highlight seven uses of AI in FinTech in 2024.

    1. AI-Driven Customer Service and Engagement

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    AI-driven chatbots and virtual assistants have become game-changers in the financial industry, improving customer engagement and satisfaction. They provide instant and personalized customer support, handle routine inquiries, offer product recommendations, and assist with account management. AI-powered client service is available 24/7, allowing customers to access support anytime.

    AI systems also analyze customer data to understand preferences and behaviors. AI-powered chatbots and virtual assistants enable FinTech companies to provide personalized advice and aid customers in making crucial financial decisions. This level of customized attention dramatically enhances the client experience and fosters business growth.

    ChatGPT is one of the most potent chatbot technologies. Debuting in 2022, it soon gained popularity for its natural conversational flow and well-structured responses to user prompts. The company that developed ChatGPT, OpenAI, expects the technology to generate $1 billion in revenue by the end of 2023. ChatGPT runs on a “pre-trained transformer language model” that uses a “reinforcement learning from human feedback” (RLHF) method to produce human-like interactions.

    2. Strengthening Cybersecurity and Fraud Detection

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    The rise of digital transactions and online financial services has increased the number of cyber threats and fraud attempts. AI in payments & FinTech promotes cybersecurity and fraud detection efforts. Businesses will save more than $10.4 billion by 2027 due to the use of AI-powered fraud detection and prevention systems, according to a Juniper Research analysis.

    AI algorithms can analyze vast amounts of data in real time, continuously monitoring transactions to recognize patterns and identify anomalies, potential security threats, and vulnerabilities. These sophisticated systems distinguish legitimate customer behaviors from fraudulent activities, preventing unauthorized access and financial loss.

    AI enhances security procedures to protect financial institutions, secure client data, and increase customer trust. By integrating AI-driven predictive analytics into their fraud detection systems, financial businesses can significantly lower the number of false positives (transactions that are mistakenly flagged as fraudulent).

    Financial service providers can better detect and prevent fraud by having more control over the data. AI-powered large language models (LLMs) also process delicate or private information within their infrastructure. It decreases dependency on third-party systems, lowers privacy issues, and guarantees the security of sensitive data.

    3. Predictive Analysis

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    Financial organizations’ success relies mainly on their ability to forecast future issues and assess existing business trends. Traditional predictive analysis models often have limitations due to the complexity and volatility of financial markets. Predictive forecasting requires more adaptable technologies for data consolidation, planning, budgeting, and scenario evaluation.

    AI-driven predictive analytics uses machine learning algorithms to identify patterns and trends in historical data and make more accurate forecasts. By employing AI in predictive analysis, financial institutions can make informed decisions on investments, risk management, and market trends, leading to improved portfolio performance and better risk mitigation.

    AI-powered large language models help analyze financial data and forecast future investment patterns, empowering investors to make well-informed choices that maximize ROIs. An LLM can undergo specialized training to serve a particular subject or sector. Customization modifies the model to produce precise results based on a domain, offering effectively targeted information.

    4. Data-Driven Decision-Making

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    Data-driven decision-making implies using facts, metrics, and statistics to make strategic business decisions that align with your company’s objectives and initiatives. It has several benefits over traditional decision-making processes:

    • Objectivity and accuracy: Data-driven decision-making relies on specific data and facts. It eliminates biases and subjectivity in human decision-making;
    • Identifying patterns and trends: It excels at identifying patterns within large datasets, unveiling valuable correlations that might go unnoticed through traditional means;
    • Improved predictions: It leverages historical data and advanced algorithms to make more accurate predictions about future events;
    • Personalization and customization: It can access individuals’ data to enable personalized experiences and enhance customer satisfaction and loyalty.
    • Cost and time efficiency: Streamlined decision-making reduces the time and effort required for analysis and enhances efficiency, leading to cost savings and resource optimization.
    • Scalability: It can be scaled effectively to handle large datasets and complex scenarios and is adaptable to different business sizes and industries.
    • Risk mitigation: It identifies potential risks and vulnerabilities, allowing businesses to proactively address challenges before they escalate.

    AI technologies facilitate data extraction, analysis, and utilization for informed decision-making, enhancing efficiency and reducing the time and effort required for traditional manual analysis. AI-powered systems analyze enormous volumes of data from multiple sources, such as social media, market trends, and consumer behavior, to find relevant insights and opportunities.

    For instance, LLMs can analyze news and social media information to determine public opinion on financial products. An LLM can be taught using a dataset specific to FinTech to foster a thorough understanding of specialized topics. Compared to general-purpose language models, responses based on domain-specific knowledge are more accurate and contextually appropriate.

    FinTech companies can leverage AI-driven insights to improve their marketing and business decisions. They can provide individualized products and services, optimize marketing plans, and create cutting-edge solutions that cater to the unique demands of their customers thanks to data-driven decision-making enabled by AI technologies.

    5. Automated Virtual Personal Assistants

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    AI technology in finance has paved the way for the development of automated virtual personal assistants, providing an enhanced customer experience. These assistants can handle a wide range of tasks, from providing real-time financial updates to executing transactions on behalf of customers. AI-powered assistance systems can track bond and stock price trends and provide instantaneous advice to traders.

    AI-enabled robo-advisors offer game-changing trading methods to novice and experienced stock market participants. Financial assistants’ AI capabilities expand beyond trading. LLMs can also enhance financial literacy, assist in developing tailored financial plans for clients based on their spending patterns and financial goals, and promote better financial decision-making.

    Automated virtual assistants powered by AI and natural language processing (NLP) transform how businesses interact with clients and enhance overall customer experiences. They offer several significant advantages over traditional customer service approaches:

    • 24/7 Availability: Automated virtual assistants operate round-the-clock without human intervention, ensuring customers receive prompt responses to their queries outside regular business hours.
    • Instant Responses: Virtual assistants handle multiple customer queries simultaneously and respond instantly, reducing wait times and providing quick resolutions.
    • Cost-Effectiveness: Automated virtual assistants are a cost-effective solution compared to maintaining large customer service teams. Once set up, they require minimal maintenance and handle numerous interactions simultaneously.
    • Consistency: Virtual assistants provide consistent responses and service quality, ensuring all customers receive the same level of support regardless of the time of day or the agent handling the query. It helps build trust and credibility with customers.
    • Multilingual Support: Virtual assistants can be programmed to understand and respond in multiple languages, eliminating the need for language-specific agents.
    • Personalization: Advanced virtual assistants analyze customer data and preferences to provide personalized recommendations and responses. It makes customers feel valued and understood.
    • Learning and Improvement: AI-powered virtual assistants continuously learn from interactions, improving their responses and problem-solving capabilities. The iterative learning process ensures they become more efficient in addressing customer needs.
    • Integration with Various Channels: Virtual assistants can seamlessly integrate with multiple communication channels, such as websites, mobile apps, and social media platforms.

    6. AI-Driven Process Automation

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    Based on Mordor Intelligence data, process automation is one of the main drivers of AI across financial institutions. FinTech players use AI technologies to automate repetitive corporate procedures, replace manual labor, and increase effectiveness. Automation powered by AI streamlines operations and reduces manual effort.

    Automation of customer onboarding, document verification, and loan processing leads to significant time and cost savings for financial institutions. AI-driven automation ensures greater accuracy and compliance with regulatory requirements. As a result, financial institutions can allocate their human resources more effectively to manage higher-value tasks that require human expertise.

    AI-powered chatbots like ChatGPT facilitate business processes, creating outlines that teams can use to develop comprehensive solutions. It can offer a financial investment structure based on goals and priorities, analyze service feedback to better understand client feelings, or recommend new financial instruments and investment vehicles.

    Additionally, large language models can save time and effort by summarizing lengthy financial papers, such as financial reports and contracts. LLMs can be trained to follow specifications for responses in a specific format. AI-powered process automation is used in many areas, including sales, call centers, accounting, and learning and development.

    7. Analysis of Credit Worthiness

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    Traditional creditworthiness analysis relies primarily on historical financial data, determining a credit risk only partially. On the other hand, AI-driven creditworthiness analysis considers a broader set of factors, including social behavior, online presence, and transaction history. It offers a more holistic and accurate credit risk assessment.

    Financial companies can efficiently analyze enormous amounts of consumer data using AI for precise credit evaluations. Today’s AI systems can assess clients’ investment, cash, and credit accounts to assess their financial health. By analyzing customer data, AI can expedite account support and help banks keep up with real-time developments.

    AI compares and analyzes data to assess if a customer is eligible for the products or services. Banking organizations can use a large language model (LLM) to analyze client data and determine risk factors. FinTech firms can also customize loans and insurance policies depending on client profiles and demands.

    AI in Fintech: Use Cases and Examples

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    The role of AI in FinTech industry is revolutionizing the sector by enhancing the speed, precision, and efficiency of financial services. Innovative AI solutions improve consumer experiences, save costs, and drive FinTech companies’ growth. This section will summarize potential trends and the future of AI in finance.

    Significant Fraud Detection Growth

    According to Mordor Intelligence, AI-enabled fraud detection will experience significant growth in the nearest future. It is because AI technologies enable quick and accurate detection of financial fraud and malpractice. They will process massive datasets, providing rapid computation, understanding user patterns, and real-time deviation detection. AI will decrease false positives and enable specialists to concentrate on more complicated problems.

    AI Will Drive Customer Experience Improvement

    Based on McKinsey research, customers will demand a personalized and seamless experience. Excellent customer service and personalization are the cornerstones of enhancing client satisfaction. AI-powered virtual assistants and chatbots will provide more customized advice and 24/7 customer service, increasing client happiness and cutting expenses. AI will also evaluate client data to develop individualized financial plans, promote products and services, and enhance the customer experience.

    Enhancing Regulatory Compliance and Reporting Processes

    According to Financial Action Task Force research, FinTech poses significant risks to the financial system in money laundering, financing of terrorism, financial crime, and fraud.
    AI will streamline regulatory compliance and reporting processes in FinTech by automating data collection and analysis. NLP will interpret complex regulations, ensure transparency with audit trails, deliver timely regulatory alerts, and facilitate cross-border compliance.

    Accelerate The Power of AI in FinTech with Uvik

    AI is a transformative force everywhere, and the FinTech industry is no exception. It revolutionizes processes, enhances customer experiences, and drives innovation. The seven ways described in the article are just the beginning of AI’s impact on finance and its potential for further advancement. Staying ahead in the dynamic FinTech sector will require embracing AI and its future applications.

    If you’re interested in further exploring the possibilities of AI in the finance industry, our staff augmentation company is here to guide and support you. Uvik will help you to locate and hire AI/ML developers with solid expertise who fit your business needs and budget in just a week. We ensure your data privacy with corresponding NDA documentation.

    Contact us for a deeper consultation on how AI can revolutionize your FinTech endeavors.


    What ethical considerations should be addressed when implementing AI in FinTech?

    AI should address transparency, unbiased decision-making, data privacy, and security to protect sensitive customer information from potential breaches.

    How does AI impact job roles in the financial industry?

    AI automates routine tasks, such as data entry and processing, leading to a shift in the focus of human workers toward more strategic and analytical roles. New job roles related to AI maintenance will emerge, requiring a workforce with expertise in both finance and technology.

    How can AI-powered automation benefit financial institutions?

    AI-driven automation can significantly benefit financial institutions by streamlining operations, reducing manual efforts, and improving efficiency. Additionally, automation ensures greater accuracy and compliance.

    What role does AI play in strengthening cybersecurity in FinTech?

    AI plays a critical role in strengthening cybersecurity in FinTech by continuously monitoring for potential security threats and anomalies. AI can identify patterns indicative of cyberattacks, protecting customers and financial institutions from potential breaches.

    How can AI transform creditworthiness analysis in the financial industry?

    AI can revolutionize creditworthiness analysis by incorporating broader data points beyond traditional financial records. Considering factors such as social behavior, online presence, and transaction history, AI can provide a more comprehensive credit risk assessment.

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