Machine Learning in the Financial Services Market Report 2026

The machine learning in the financial services market size has grown exponentially in recent years. It will grow from $5.24 billion in 2025 to $7.13 billion in 2026 at a compound annual growth rate (CAGR) of 36%. The growth in the historic period can be attributed to growth of digital banking services, rising transaction volumes, increasing complexity of financial products, expansion of online financial platforms, improved availability of structured financial data.

The machine learning in the financial services market size is expected to see exponential growth in the next few years. It will grow to $24.17 billion in 2030 at a compound annual growth rate (CAGR) of 35.7%. The growth in the forecast period can be attributed to increasing investments in advanced ai models, rising demand for real-time financial insights, expansion of automated decision-making systems, growing regulatory focus on explainable ai, increasing integration of ml with cloud platforms. Major trends in the forecast period include increasing deployment of ai-based fraud detection systems, rising adoption of machine learning for credit scoring, growing use of predictive analytics in risk management, expansion of algorithmic trading applications, enhanced focus on personalized financial services.

The growing preference for cloud-based solutions is expected to drive the growth of machine learning in the financial services market in the coming years. Cloud-based solutions are services or tools accessed over the internet, allowing users to operate them without installing or managing systems on local computers. Their adoption is increasing due to the need for remote access, enabling individuals and organizations to use essential tools and data from any location without dependence on physical infrastructure. The use of cloud-based solutions supports machine learning in financial services by providing flexible and scalable infrastructure, allowing institutions to process large volumes of data in real time, deploy machine learning models more rapidly, and seamlessly integrate analytics into operations to improve decision-making and risk management. For example, in December 2023, Eurostat, a Luxembourg-based governmental statistical agency, reported that in 2023, 42.5% of enterprises in the EU utilized cloud computing services, mainly for email, file storage, and office software. Consequently, the increasing reliance on cloud-based solutions is fueling the expansion of machine learning in the financial services market.

Major companies operating in the machine learning in the financial services market are increasingly adopting advanced machine learning and generative AI platforms to enhance operational efficiency, automate complex processes, and deliver more personalized customer experiences. Advanced machine learning and generative AI platforms enable financial institutions to modernize legacy infrastructure, accelerate model deployment, and integrate innovative AI-driven capabilities across business functions. For example, in April 2025, Lloyds Banking Group, a UK-based financial services provider, implemented a new machine learning and generative AI platform built on Google Cloud’s Vertex AI to strengthen its data science and AI operations. This transition involved migrating 15 modeling systems and hundreds of machine learning models from on-premises infrastructure, reducing operational emissions by 27 tonnes of CO₂ and enabling the rapid development of advanced machine learning applications. The platform has already supported more than 80 new machine learning use cases and over 18 generative AI systems across the organization, including an algorithm that reduces the income verification step in mortgage applications from days to seconds. Lloyds is also collaborating with Google Cloud to develop an agentic AI system aimed at further transforming customer interactions and delivering more intelligent and responsive financial services.

In December 2024, Mastercard Inc., a US-based credit card company, acquired Recorded Future for an undisclosed amount. This acquisition seeks to strengthen Mastercard’s cybersecurity and fraud detection capabilities by incorporating Recorded Future’s machine learning-powered threat intelligence platform. The integration enables financial institutions and digital businesses to proactively detect, evaluate, and address cyber threats, thereby enhancing trust and security across Mastercard’s global payment ecosystem. Recorded Future Inc., based in the US, specializes in cybersecurity and threat intelligence solutions designed for the financial services industry.

Major companies operating in the machine learning in the financial services market are Amazon Web Services Inc., Microsoft Corporation, Intel Corporation, Accenture Public Limited Company, International Business Machines Corporation, Oracle Corporation, SAP Societas Europaea, Salesforce Inc., NVIDIA Corporation, SAS Institute Inc., Palantir Technologies Inc., Fair Isaac Corporation, HighRadius Corporation, Upstart Holdings Inc., DataRobot Inc., Ocrolus Inc., Feedzai Inc., H2O.ai Inc., ZestFinance Inc., Overbond Ltd.

North America was the largest region in the machine learning in the financial services market in 2025. The regions covered in the machine learning in the financial services market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning in the financial services market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

The machine learning in the financial services market consists of revenues earned by entities by providing services such as financial forecasting, regulatory compliance support, portfolio optimization, and transaction monitoring. Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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