AI in banking market Outlook - 2030
The global AI in banking market size was valued at $3.88 billion in 2020, and is projected to reach $64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030.
Artificial Intelligence (AI) brings the advantage of digitization to banks and helps them meet the competition posed by FinTech players. For instance, according to joint research conducted by the National Business Research Institute and Narrative Science in 2020, about 32% of banks are already using AI technologies such as predictive analytics, voice recognition, and various others, to have a competitive advantage in the market.
Furthermore, AI also enables banks to manage huge volumes of data at record speed to derive valuable insights and develops a better understanding of customers and their behavior. This enables banks to customize financial products and services by adding personalized features and intuitive interactions to deliver meaningful customer engagement and build strong relationships with its customers.
Improvement in data collection technology among the banks and financial institutions positively impacts the AI in banking market growth. In addition, increase in investment by banks in AI and rise in customer preferences for personalized financial services boost the growth of the market across the globe.
However, factors such as higher deployment cost of AI and advanced machine learning and lack of skilled labor are limiting the growth of the AI in banking market.
On the contrary, AI in banking market is anticipated to offer numerous opportunities for new players in the market. Rise in adoption of AI-powered chatbots, utilizing natural language processing, assist customers with basic banking queries, transaction issues, and troubleshooting around the clock, offers quick responses and eases the burden on human agents. In addition, AI is being used to analyze customer data to provide personalized financial advice, product recommendations, and targeted services tailored to individual customer profiles and behaviors. Furthermore, AI in banking is used to analyze transaction patterns in real time to detect anomalies indicative of fraudulent activity, significantly reducing false positives and minimizing the risk of financial crimes. In addition, AI systems can monitor user behavior, such as typing patterns and device usage, to identify suspicious activity and prevent unauthorized access.
Moreover, AI-powered bots can automate repetitive tasks like data entry, processing loan applications, and handling customer onboarding through Robotic Process Automation (RPA), streamlining operations and reducing human errors. In addition, AI extracts, analyzes, and categorizes data from documents such as loan agreements and contracts, enabling faster processing and decision-making. These factors are expected to offer remunerative opportunities for the growth of AI in banking market.
Furthermore, the integration of AI with blockchain technology presents significant AI in banking market opportunity, enhancing security, transparency, and efficiency in banking transactions. For instance, AI algorithms can optimize smart contract execution and predict blockchain transaction outcomes. In addition, the surge in use of AI in banking enhances operational efficiency by optimizing internal processes such as human resources, procurement, and IT management, making banks more efficient and cost-effective. Furthermore, AI can improve supply chain management in areas like payments and settlement processing, reducing operational costs and increasing transparency.
Moreover, the surge in the demand for AI in banking offers predictive analytics to predict customer behavior, like the chances of loan defaults, customer churn, or higher spending. This helps banks customize services and improve customer loyalty, helping banks spot market changes, adjust investment plans, and manage risks better. This is expected to offer lucrative opportunities for market growth.
Market Trends Insights
The AI in banking market is expected to witness several noteworthy trends reshaping the financial industry. Rise in the adoption of AI-powered banking solutions are revolutionizing customer interactions by providing personalized services, including chatbots and virtual assistants, enhancing user experience and streamline operations. In addition, there is growing trend toward the use of fraud detection AI. Machine learning algorithms are being deployed to analyze transaction data in real-time, enabling banks to swiftly identify and prevent fraudulent activities, thereby protecting customer assets. Furthermore, the shift in preference toward predictive analytics in finance is gaining momentum, allowing banks to forecast customer behavior, market trends, and credit risks with greater accuracy, thereby enabling more informed decision-making in lending and investment processes.
Another notable trend in the market is the surge in the use of broader application of artificial intelligence in banking. The use of AI and banking is transforming traditional business models, optimizing everything from loan origination to regulatory compliance checks. In addition, there is a growing trend toward the adoption of generative AI in banking, where AI models simulate financial scenarios to assist in risk management, strategy testing, and even product innovation, all without exposing banks to real-world risks. These AI in banking market trends is expected to drive the growth of the AI in banking market in the upcoming years.
By component, the solution segment is expected to garner a significant AI in banking market share during the forecast period owing to managing the massive volume of data being generated for meaningful insights and better-informed decisions. Moreover, companies majorly focus on creating novel opportunities for growth and revenue generation, thereby increasing the preference for AI and advance machine learning algorithms across industries. However, the service segment is expected to witness growth at the highest rate during the forecast period, owing to surge in demand for cloud-based AI services among the end users.
Type Insights
Artificial Intelligence is transforming the banking industry by delivering insightful analysis that reshapes the operations of financial institutions and enhances customer service. A major advantage of AI is its capacity to examine customer behavior. By evaluating extensive datasets, AI can forecast spending trends, savings behaviors, and investment choices, allowing banks to provide tailored financial products and services.
Moreover, AI enhances risk assessment by considering a broader range of factors beyond traditional credit scores, leading to more accurate lending decisions and reducing the likelihood of defaults. In terms of operational efficiency, AI automates routine tasks such as loan processing and fraud detection, streamlining workflows, cutting costs, and improving service delivery. In addition, AI-powered predictive tools help banks stay ahead of market trends by providing valuable insights into financial markets and investment opportunities, which are crucial for wealth management and advisory services.
Regional Review
Region wise, the AI in banking market was dominated by North America in 2020 owing to increase in demand for modernizing banks and legacy business systems across the region. In addition, surge in demand for conducting hassle-and risk-free digital transformation among financial institutes is anticipated to boost the growth of the market. However, Asia- Pacific is expected to witness significant growth during the forecast period, owing to surge in need to monitor growing number of financial violations and offences.
The report focuses on growth prospects, restraints, and trends of the AI in banking market analysis. The study provides Porter’s five forces analysis to understand impact of various factors, such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers, on the AI in banking market.
Segment review
The global AI in banking market is segmented on the basis of component, enterprise size, application, technology and region. Depending on component, the market is segregated into solution and service. Depending on enterprise size, it is fragmented into large enterprises and SMEs. Based on application, the market is divided into risk management compliance & security, customer service, back office/operations, financial advisory and others. On the basis of technology, it is categorized into machine learning & deep learning, natural language processing (NLP), computer vision and others. Region wise, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
By Component
Solution is segment is projected as one of the most lucrative segments.
Competitive Analysis
The key players profiled in the AI in banking market are Amazon Web Services Inc., BigML, Inc, Cisco Systems, Inc., Fair Isaac Corporation, Hewlett Packard Enterprise Development LP, International Business Machines Corporation, Microsoft Corporation, RapidMiner, Inc., SAP SE and SAS Institute Inc. These key players have adopted various strategies, such as product portfolio expansion, mergers & acquisitions, agreements, geographical expansion, and collaborations, to increase their market penetration and strengthen their foothold in the AI in banking market.
Key Industry Developments
On May 13, 2024, Temenos launched its Responsible Generative AI solutions as part of its AI-infused banking platform. These secure solutions integrated with Temenos Core and Financial Crime Mitigation (FCM), revolutionizing data interaction, boosting productivity, and profitability. The new Generative AI allowed users to engage in natural language queries, generating insights and reports quickly. This transparency and explainability enabled users and regulators to verify results.
On February 10, 2025, UBS, Switzerland's leading universal bank, expanded its AI use with Microsoft Azure AI solutions across five divisions to enhance client advisory services and operational efficiency. They developed UBS Red, an AI assistant powered by Azure AI Search and Azure OpenAI Service, providing advisors with instant access to investment insights and market intelligence. A significant milestone was the digitization of 60,000 investment documents, making them easily searchable for client advisors. UBS also introduced an AI Hub and a data mesh framework to support broader AI implementation across its operations.
On October 10, 2023, SBS (formerly Sopra Banking Software) launched its next-generation, modular, real-time, fully cloud-native core banking platform. This significant milestone unlocked the potential of cloud technologies and AI for SBS’s large customer base. After years of investment and an 18-month beta launch, the new SBP Core Platform became available to all customers. With its open architecture, high scalability, and integrated AI capabilities, it was anticipated to be a game changer for banks. Delivered as Software-as-a-Service, it offered cost efficiency, high security, and seamless product updates.
On February 26, 2025, OpenAI launched a multiyear agreement with BNY, the oldest bank in the United States, to enhance BNY's AI platform, Eliza, using OpenAI's tools like Deep Research. OpenAI aimed to gain insights into real-world applications of its models. The banking sector, early adopters of AI, has been filing numerous AI patents and recruiting top AI talent. OpenAI's COO, Brad Lightcap, noted the high demand for AI to simplify complex workflows, with similar partnerships already in place with Morgan Stanley and Klarna. The BNY partnership provided an opportunity to collaborate with a company already advanced in its AI efforts, as BNY had debuted Eliza the previous year.
COVID-19 impact analysis
The COVID-19 outbreak has a significant impact on growth of the AI in banking market, mainly owing to rise in digitization among both the financial institutes and end users, the demand for advanced AI technology increased to reduce the load on the banking servers and reduce transaction delay with rising digital transaction during the pandemic. For instance, according to the survey of Prudential Regulation Authority (PRA) by Bank of England in August 2020, around 40% of respondents reported an increase in the importance of AI and data science for critical financial operation. Furthermore, around 35% of banks reported that AI and data science had a positive impact on technologies that support remote working among employees and on their overall security provided for AI projects. In addition, the pandemic has accelerated the use of AI-powered tools to manage a sudden increase in customer enquiries. Thus, a number of such developments across the globe are anticipated to provide lucrative opportunities for the expansion of the market.
By Enterprise Size
Large Enterprises is projected as one of the most lucrative segments.
Top impacting factors
Increase in investment by banks in AI
Banks are increasing investment in AI solutions to transform the management process of FinTech and to provide better services to end users. In addition, with the increase in complexity and competition in the banking sector, the demand for industry-specific solutions increased to meet its goals. Thus, to meet the requirements of customers, various banking institutes and FinTech are investing in AI solutions, which, in turn, drives the market growth. Furthermore, AI can assist financial institutes at various stages of risk management process ranging from identifying risk exposure, measuring, estimating, and assessing its effects. In addition, banks are adopting and developing AI techniques to analyze large volumes of data and to deliver valuable insights to customers. Moreover, the increase in investments in AI by FinTech & banks to enhance the automation process and to offer more streamlined and personalized customer experience propels the market growth. In addition, major financial institutes such as Bank of America, JPMorgan, and Morgan Stanley, are investing heavily in AI technology to develop automated investment advisors and train systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring, thus augmenting the growth of the AI in banking market revenue.
Rise in preference for personalized financial services
End users are increasingly preferring personalized financial services, owing to surge in adoption of chatbots among banks and increase in competition among the banks for garnering maximum market share. Various banks are providing budget management apps powered by AI technology, which help customers to achieve their financial targets and improve their money management process, thus driving the growth of the market. Furthermore, robo-advisors are one of the other rapidly emerging trends in personalized financial services, as they specifically target investors with limited resources such as individuals and small- to medium-sized businesses for managing their funds. In addition, AI-based robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, and retirement plans for their users. Moreover, with rise of usage-based loans AI technologies are helping to calculate the interest suitable for each individual, which, in turn, propels the growth of the AI in banking market.
Government Initiatives:
Favorable government policies and increase in awareness for the need for digital transformation are shaping the demand for AI solutions in the banking sector globally. Governments worldwide are increasingly recognizing the potential of AI to enhance financial services, improve customer experience, and boost the efficiency of banking operations. As part of these efforts, several governments have introduced strategic initiatives to encourage AI adoption in banking, particularly to ensure better financial inclusion, enhance regulatory compliance, and reduce operational risks.
Furthermore, many regional governments are collaborating with financial institutions and technology companies to develop and implement AI-driven solutions that improve the efficiency and security of banking systems. In addition, governments are focusing on the development of regulations and frameworks to ensure the ethical and secure use of AI technologies. With the rise of data privacy concerns, regulations such as the General Data Protection Regulation (GDPR) in Europe are shaping how AI applications can be developed and deployed in the banking sector to protect customer data and promote transparency.
Moreover, the surge in the adoption of AI in the banking sector is significantly driven by the upsurge in use of automation and machine learning. These technologies help manage large volumes of financial transactions, prevent fraud, and offer personalized financial products. According to the AI in Banking Market Report, banks are increasingly investing in AI-driven tools to enhance customer service, improve operational efficiency, and reduce costs, thereby fueling market growth.
By Region
Asia-Pacific would exhibit the highest CAGR of 34.3% during 2021-2030.
Key Benefits For Stakeholders
The study provides an in-depth analysis of the AI in banking market forecast along with the current AI in banking market trends and future estimations to explain the imminent investment pockets.
Information about key drivers, restraints, & opportunities and their impact analysis on the global AI in banking market is provided in the report.
Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
The quantitative analysis of the market from 2021 to 2030 is provided to determine the AI in banking market potential.
Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
AI in Banking Market Report Highlights
| Aspects | Details |
 By COMPONENT |
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 By ENTERPRISE SIZE |
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 By APPLICATION |
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 By TECHNOLOGY |
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 By Region |
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 Key Market Players | SAP SE, Amazon Web Services, Inc., .BigML, Inc., RapidMiner, Inc., HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP, SAS INSTITUTE INC., FAIR ISAAC CORPORATION, INTERNATIONAL BUSINESS MACHINES CORPORATION, Cisco System Inc., MICROSOFT CORPORATION |
Analyst Review
The adoption of AI in banking solutions has increased over the years to help organizations monitor production processes and to provide enhanced customer services. In addition, AI in banking technology is being used across a number of applications to help drive productivity, improve efficiency, and save people time and organizational funds. Furthermore, AI in banking tools is used to clean data sets, give predictions, improve decision-making, and to respond to customer service needs, which are expected to fuel the market growth. In addition, surge in adoption of cloud as well as mobile applications is expected to drive the growth of the market.
Key providers of AI in banking market such as SAP SE, International Business Machines Corporation, and Microsoft Corporation account for a significant share in the market. For instance, Saxo Bank, a leading Danish investment bank, managed to drastically reduce the time it takes to onboard new customers and get them trading on its platform, owing to investments in data science and advanced AI technologies by automating repeating and time-consuming tasks and provide better time management of workers.
Furthermore, financial institutes are collaborating with AI companies to enhance their existing AI system for better and secure systems. For instance, in August 2021, RBL Bank partnered with Amazon Web Services (AWS) to strengthen its AI-powered banking solutions and drive digital transformation at the bank, adding significant value to the bank’s innovative offerings, saving costs, and tightening risk controls. In addition, it is leveraging Amazon Textract, a machine learning service that automatically extracts text, handwriting, and data from scanned documents, across the bank’s risk and operations divisions to analyze documents such as financial statements, stock statements, and stock audit reports to predict default risk.
Moreover, many open banking platforms are leveraging solutions from financial technology providers to improve their management technology. For instance, in September 2020, open banking platform, Tink leveraged open banking technology from Enel to develop digital financial solutions for its clients in Italy and Europe. With the agreement, Tink will be able to support its clients in the daily management of their finances, with an innovative and engaging solution that uses machine learning to provide tailored and personalized advice. Thus, such developments across the globe drive the growth of the market.
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