As per Intent Market Research, the Artificial Intelligence (AI) in Banking Market was valued at USD 17.6 billion in 2023 and will surpass USD 105.8 billion by 2030; growing at a CAGR of 29.2% during 2024 - 2030.
The AI in Banking market is evolving rapidly as banks and financial institutions increasingly integrate artificial intelligence technologies into their operations. AI solutions in banking are being used to enhance customer experiences, optimize processes, reduce operational costs, and improve decision-making. Among the key technologies driving this transformation are Machine Learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA), each contributing to different aspects of banking operations, from fraud detection to customer service. The market is also benefiting from the growing need for banks to stay competitive by embracing digital transformation, thus improving operational efficiencies and service offerings. As AI continues to mature, it is expected to play a pivotal role in reshaping the future of banking.
Machine Learning Segment is Largest Owing to Increased Automation in Banking
Among the various AI technologies used in banking, Machine Learning (ML) stands out as the largest segment. This is due to its broad application across multiple banking functions, including fraud detection, credit scoring, and customer behavior prediction. ML algorithms can analyze vast amounts of data, recognizing patterns and predicting trends that would be nearly impossible for humans to detect manually. Banks are increasingly relying on machine learning to optimize their operations and improve customer experiences by offering personalized financial products, automated loan approvals, and better risk management. Machine learning also supports real-time decision-making, which is essential in fraud prevention and regulatory compliance.
The ability of machine learning systems to adapt and improve over time through continuous learning makes them highly valuable to banks, driving widespread adoption. As data volumes grow and transaction complexities increase, machine learning provides a scalable solution that can handle vast amounts of information, ensuring accurate and efficient banking operations. This segment is expected to continue expanding as financial institutions seek to leverage automation for reducing operational costs and enhancing decision-making processes.
Fraud Detection & Prevention Application is Fastest Growing Owing to Rising Cybersecurity Concerns
Fraud Detection & Prevention is currently the fastest-growing application within AI in banking. As digital transactions increase, so does the risk of fraud. Banks are adopting AI-driven fraud detection systems to safeguard against increasingly sophisticated fraudulent activities. Machine learning models and AI algorithms analyze transactional data in real-time, identifying anomalies and preventing fraud before it occurs. This proactive approach is vital as the banking sector becomes more digitized, with cyberattacks and financial fraud being constant threats. AI in fraud detection can reduce both false positives and the financial loss from fraud, making it a top priority for banks globally.
As online banking and mobile payment systems continue to gain popularity, the need for more robust security measures becomes paramount. AI's ability to quickly detect and mitigate fraud has made it an indispensable tool in maintaining trust in the financial system. This growing focus on fraud prevention is fueling the rise of AI in banking and driving the growth of the fraud detection and prevention segment.
Retail Banks End-User Segment is Largest Owing to Widespread Adoption of AI Solutions
Retail Banks represent the largest end-user segment in the AI in banking market. These banks are the primary adopters of AI technologies for improving customer experiences and streamlining operations. AI-powered chatbots, virtual assistants, and machine learning models are extensively used by retail banks to automate customer service, personalize recommendations, and enhance engagement. Retail banks are leveraging AI for a variety of functions, such as automating loan approval processes, offering personalized financial products, and improving fraud detection.
The consumer-centric nature of retail banking makes it a prime sector for AI integration, as customers expect more efficient, personalized, and secure services. Retail banks have made significant investments in AI to meet these demands, creating a robust market for AI applications. This segment will likely continue to lead the AI adoption curve as banks increasingly focus on digital transformation to enhance their competitive positioning in a rapidly evolving market.
Cloud-based Deployment Mode is Fastest Growing Owing to Scalability and Flexibility
In terms of deployment mode, Cloud-based solutions are experiencing the fastest growth in the AI in banking market. Cloud technology offers scalability, flexibility, and cost efficiency, making it an ideal choice for financial institutions. Cloud-based AI solutions enable banks to handle massive datasets, perform complex analyses, and deploy AI applications without the need for expensive on-premise infrastructure. This is particularly attractive to smaller banks and fintech companies that seek advanced AI capabilities but lack the resources for large-scale on-premise systems.
The shift to cloud-based deployment also aligns with the broader trend of digital transformation in banking, where cloud services allow for faster implementation and continuous updates. As more banks embrace cloud computing to facilitate AI adoption, this segment will continue to expand rapidly. The ability to scale AI services on-demand, combined with lower upfront costs, makes cloud-based solutions the go-to option for many financial institutions.
North America Region is Largest Owing to Advanced Technological Infrastructure and Strong Adoption
The North American region is the largest market for AI in banking, primarily due to its advanced technological infrastructure and early adoption of AI solutions. The United States, in particular, is home to many of the world's leading banks and fintech companies, which are quick to embrace AI technologies. North American financial institutions are heavily investing in AI to enhance customer experiences, streamline operations, and improve compliance with regulatory standards. Moreover, the region’s strong focus on cybersecurity and data privacy has led to a surge in the adoption of AI-driven fraud detection and prevention systems.
The presence of major technology players like IBM, Microsoft, and Google also contributes to the region's dominance, as they provide AI solutions tailored to the banking sector. As financial institutions in North America continue to invest in AI and integrate these technologies across their operations, the region is expected to maintain its leadership position in the AI in banking market.
Leading Companies and Competitive Landscape
The competitive landscape of the AI in Banking market is dominated by a mix of established technology giants, such as IBM, Microsoft, and Google, as well as fintech companies that specialize in AI solutions for the banking sector. These companies provide a wide range of AI-powered products and services, from fraud detection tools to customer service chatbots, driving innovation in the industry. In addition, many banks are collaborating with AI startups to enhance their AI capabilities and improve operational efficiencies.
The competitive dynamics of the market are also shaped by the increasing number of partnerships, acquisitions, and collaborations between banks, fintech companies, and technology providers. As the demand for AI solutions grows, companies are focusing on expanding their product portfolios, improving AI algorithms, and offering tailored solutions to meet the specific needs of financial institutions. With AI becoming a key differentiator in the banking industry, companies that can deliver scalable, secure, and innovative AI solutions are well-positioned to lead the market in the coming years.
List of Leading Companies:
- IBM
- Microsoft Corporation
- Google (Alphabet Inc.)
- SAS Institute Inc.
- Accenture
- AWS (Amazon Web Services)
- Oracle Corporation
- NVIDIA Corporation
- DataRobot
- Palantir Technologies
- Infosys
- Cognizant Technology Solutions
- Wipro Limited
- TCS (Tata Consultancy Services)
- Capgemini
Recent Developments:
- IBM partnered with Bank of America to enhance its AI-powered banking solutions. The collaboration focuses on expanding AI-based customer service capabilities and improving fraud detection systems.
- Google Cloud launched a new suite of AI tools tailored for the banking industry, designed to streamline regulatory compliance, enhance security, and enable more personalized customer experiences.
- Microsoft introduced new AI-powered tools for financial institutions that focus on automating back-office processes and improving decision-making in loan origination and risk management.
- Accenture acquired an AI technology company specializing in deep learning to expand its AI-driven services for financial institutions, focusing on customer experience and automation.
- SAS Institute signed a strategic partnership with a leading European bank to integrate advanced AI analytics for enhancing risk management and regulatory reporting processes.
Report Scope:
Report Features |
Description |
Market Size (2023) |
USD 17.6 Billion |
Forecasted Value (2030) |
USD 105.8 Billion |
CAGR (2024 – 2030) |
29.2% |
Base Year for Estimation |
2023 |
Historic Year |
2022 |
Forecast Period |
2024 – 2030 |
Report Coverage |
Market Forecast, Market Dynamics, Competitive Landscape, Recent Developments |
Segments Covered |
|
Regional Analysis |
North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, and Rest of Europe), Asia-Pacific (China, Japan, South Korea, Australia, India, and Rest of Asia-Pacific), Latin America (Brazil, Argentina, and Rest of Latin America), Middle East & Africa (Saudi Arabia, UAE, Rest of Middle East & Africa) |
Major Companies |
IBM, Microsoft Corporation, Google (Alphabet Inc.), SAS Institute Inc., Accenture, AWS (Amazon Web Services), Oracle Corporation, NVIDIA Corporation, DataRobot, Palantir Technologies, Infosys, Cognizant Technology Solutions, Wipro Limited, TCS (Tata Consultancy Services), Capgemini |
Customization Scope |
Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements |
1. Introduction |
1.1. Market Definition |
1.2. Scope of the Study |
1.3. Research Assumptions |
1.4. Study Limitations |
2. Research Methodology |
2.1. Research Approach |
2.1.1. Top-Down Method |
2.1.2. Bottom-Up Method |
2.1.3. Factor Impact Analysis |
2.2. Insights & Data Collection Process |
2.2.1. Secondary Research |
2.2.2. Primary Research |
2.3. Data Mining Process |
2.3.1. Data Analysis |
2.3.2. Data Validation and Revalidation |
2.3.3. Data Triangulation |
3. Executive Summary |
3.1. Major Markets & Segments |
3.2. Highest Growing Regions and Respective Countries |
3.3. Impact of Growth Drivers & Inhibitors |
3.4. Regulatory Overview by Country |
4. Artificial Intelligence (AI) in Banking Market, by Technology Type (Market Size & Forecast: USD Million, 2022 – 2030) |
4.1. Machine Learning |
4.2. Natural Language Processing (NLP) |
4.3. Robotic Process Automation (RPA) |
4.4. Expert Systems |
4.5. Others |
5. Artificial Intelligence (AI) in Banking Market, by Application (Market Size & Forecast: USD Million, 2022 – 2030) |
5.1. Fraud Detection & Prevention |
5.2. Customer Service & Chatbots |
5.3. Risk & Compliance Management |
5.4. Loan Underwriting & Credit Scoring |
5.5. Process Automation |
5.6. Others |
6. Artificial Intelligence (AI) in Banking Market, by End-User (Market Size & Forecast: USD Million, 2022 – 2030) |
6.1. Retail Banks |
6.2. Commercial Banks |
6.3. Investment Banks |
6.4. Insurance Companies |
6.5. Others |
7. Artificial Intelligence (AI) in Banking Market, by Deployment Mode (Market Size & Forecast: USD Million, 2022 – 2030) |
7.1. On-premise |
7.2. Cloud-based |
8. Regional Analysis (Market Size & Forecast: USD Million, 2022 – 2030) |
8.1. Regional Overview |
8.2. North America |
8.2.1. Regional Trends & Growth Drivers |
8.2.2. Barriers & Challenges |
8.2.3. Opportunities |
8.2.4. Factor Impact Analysis |
8.2.5. Technology Trends |
8.2.6. North America Artificial Intelligence (AI) in Banking Market, by Technology Type |
8.2.7. North America Artificial Intelligence (AI) in Banking Market, by Application |
8.2.8. North America Artificial Intelligence (AI) in Banking Market, by End-User |
8.2.9. North America Artificial Intelligence (AI) in Banking Market, by Deployment Mode |
8.2.10. By Country |
8.2.10.1. US |
8.2.10.1.1. US Artificial Intelligence (AI) in Banking Market, by Technology Type |
8.2.10.1.2. US Artificial Intelligence (AI) in Banking Market, by Application |
8.2.10.1.3. US Artificial Intelligence (AI) in Banking Market, by End-User |
8.2.10.1.4. US Artificial Intelligence (AI) in Banking Market, by Deployment Mode |
8.2.10.2. Canada |
8.2.10.3. Mexico |
*Similar segmentation will be provided for each region and country |
8.3. Europe |
8.4. Asia-Pacific |
8.5. Latin America |
8.6. Middle East & Africa |
9. Competitive Landscape |
9.1. Overview of the Key Players |
9.2. Competitive Ecosystem |
9.2.1. Level of Fragmentation |
9.2.2. Market Consolidation |
9.2.3. Product Innovation |
9.3. Company Share Analysis |
9.4. Company Benchmarking Matrix |
9.4.1. Strategic Overview |
9.4.2. Product Innovations |
9.5. Start-up Ecosystem |
9.6. Strategic Competitive Insights/ Customer Imperatives |
9.7. ESG Matrix/ Sustainability Matrix |
9.8. Manufacturing Network |
9.8.1. Locations |
9.8.2. Supply Chain and Logistics |
9.8.3. Product Flexibility/Customization |
9.8.4. Digital Transformation and Connectivity |
9.8.5. Environmental and Regulatory Compliance |
9.9. Technology Readiness Level Matrix |
9.10. Technology Maturity Curve |
9.11. Buying Criteria |
10. Company Profiles |
10.1. IBM |
10.1.1. Company Overview |
10.1.2. Company Financials |
10.1.3. Product/Service Portfolio |
10.1.4. Recent Developments |
10.1.5. IMR Analysis |
*Similar information will be provided for other companies |
10.2. Microsoft Corporation |
10.3. Google (Alphabet Inc.) |
10.4. SAS Institute Inc. |
10.5. Accenture |
10.6. AWS (Amazon Web Services) |
10.7. Oracle Corporation |
10.8. NVIDIA Corporation |
10.9. DataRobot |
10.10. Palantir Technologies |
10.11. Infosys |
10.12. Cognizant Technology Solutions |
10.13. Wipro Limited |
10.14. TCS (Tata Consultancy Services) |
10.15. Capgemini |
11. Appendix |
A comprehensive market research approach was employed to gather and analyze data on the Artificial Intelligence in Banking Market. In the process, the analysis was also done to analyze the parent market and relevant adjacencies to measure the impact of them on the Artificial Intelligence in Banking Market. The research methodology encompassed both secondary and primary research techniques, ensuring the accuracy and credibility of the findings.
Secondary Research
Secondary research involved a thorough review of pertinent industry reports, journals, articles, and publications. Additionally, annual reports, press releases, and investor presentations of industry players were scrutinized to gain insights into their market positioning and strategies.
Primary Research
Primary research involved conducting in-depth interviews with industry experts, stakeholders, and market participants across the E-Waste Management ecosystem. The primary research objectives included:
- Validating findings and assumptions derived from secondary research
- Gathering qualitative and quantitative data on market trends, drivers, and challenges
- Understanding the demand-side dynamics, encompassing end-users, component manufacturers, facility providers, and service providers
- Assessing the supply-side landscape, including technological advancements and recent developments
Market Size Assessment
A combination of top-down and bottom-up approaches was utilized to analyze the overall size of the Artificial Intelligence in Banking Market. These methods were also employed to assess the size of various subsegments within the market. The market size assessment methodology encompassed the following steps:
- Identification of key industry players and relevant revenues through extensive secondary research
- Determination of the industry's supply chain and market size, in terms of value, through primary and secondary research processes
- Calculation of percentage shares, splits, and breakdowns using secondary sources and verification through primary sources
Data Triangulation
To ensure the accuracy and reliability of the market size, data triangulation was implemented. This involved cross-referencing data from various sources, including demand and supply side factors, market trends, and expert opinions. Additionally, top-down and bottom-up approaches were employed to validate the market size assessment.
NA