AI In Fintech Market Size (2024 - 2029)

AI In Fintech Market Analysis

The AI In Fintech Market size is estimated at USD 14.79 billion in 2024, and is expected to reach USD 43.04 billion by 2029, growing at a CAGR of 23.82% during the forecast period (2024-2029).

• Payment- and wealth-focused fintech companies have focused on bolstering their existing infrastructure by investing in new resources or expanding their capacity to withstand the stress to their systems from higher transaction volumes. Though it initially seemed challenging for fintech companies, such actions have provided a significant need for AI solutions as these companies depend on transaction volumes for revenue. Such factors are expected to spearhead the demand for AI solutions in the fintech market.
• Financial firms have been the early adopters of the mainframe computer and relational database. They eagerly wait for the next level of computational power. Artificial intelligence (AI) improves results by applying methods derived from the aspects of human intelligence at a broader scale. The computational arms race for past years has revolutionized fintech companies. Technologies such as machine learning, AI, neural networks, Big Data Analytics, and evolutionary algorithms have allowed computers to crunch huge, varied, diverse, and deeper datasets than ever before.
• Moreover, AI and machine learning have benefited banks and fintech as they can process vast amounts of information about customers. This data and information are then compared to obtain results about timely services/products that customers want, which has aided, essentially, in developing customer relations.
• Additionally, machine learning is being adopted at unprecedented rates, specifically to create propensity models. Banks and insurance companies are introducing machine learning-based solutions for web and mobile applications. This has further enhanced real-time target marketing by predicting the product propensity of customers based on behavioral data in real time.
• AI-driven chatbots and virtual assistants are revolutionizing the financial industry, enhancing customer engagement and satisfaction. They deliver immediate, tailored customer support, manage routine queries, suggest products, and aid in account management. These AI-driven services operate round the clock, ensuring customers can seek assistance at any hour. Furthermore, AI platforms analyze customer data, enabling a deeper understanding of preferences and behaviors.
• Moreover, several credit card companies implement predictive analytics into their existing fraud detection workflows to reduce false positives. The market is further gaining traction, with several players offering AI-based anti-money laundering (AML) and fraud detection solutions for credit card companies and other financial institutions.
• AI-ready infrastructure should be capable of efficient data management, have enough processing power, be agile, flexible, and scalable, and have the capacity to accommodate different volumes of data. Therefore, it would be more challenging for fintech small businesses to assemble the necessary hardware and software elements to support AI. Moreover, as the democratization of AI and deep learning applications expands, they are now becoming viable for small and medium-sized businesses and are not limited to only tech giants. The demand for AI professionals to do the work has ballooned as well, and the scarcity of trained resources is the major challenge for AI in fintech.

AI In Fintech Industry Segmentation

Data analysis using AI data mining tools assists fintech organizations in gathering numerous angles of information and leads to data silos. AI and ML assist organizations in gathering numerous facets of data and in ingesting, analyzing, cleaning, and archiving the data by revealing useful information.

The AI in fintech market is segmented by type into solutions and services. By deployment, the market is segmented into cloud and on-premise. By application, the market is segmented into chatbots, credit scoring, quantitative and asset management, fraud detection, and other applications. By geography, the market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa. The market sizes and forecasts are provided in terms of value (USD) for all the above segments.