Artificial Intelligence in Banking Market Size, Trends and Insights By Component (Service, Solution), By Application (Fraud Detection and Prevention, Transaction Monitoring, Identity Verification, Customer Service, Virtual Assistants, Automated Customer Support, Risk Management, Credit Scoring, Market Risk Analysis, Personalized Banking, Customer Recommendations, Targeted Marketing, Compliance and Regulatory Reporting, Anti-Money Laundering (AML), Know Your Customer (KYC), Others), By Technology (Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Natural Language Processing (NLP), Text Analysis, Speech Recognition, Chatbots and Virtual Assistants, Robotic Process Automation (RPA), Process Automation, Workflow Automation, Predictive Analytics, Risk Management, Customer Insights), By Enterprise Size (Large Enterprise, SMEs), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033
Reports Description
The CMI Team’s most recent market research predicts that from 2024 to 2033, global artificial intelligence in banking will grow at a CAGR of 22.5%. In 2024, the market size is projected to reach a valuation of USD 22,688.7 Million. By 2033, the valuation is anticipated to reach USD 140,940.1 Million.
VC investments in AI by country
| Country | VC Investment |
| US | 54,836 |
| China | 18,270 |
| EU | 7,921 |
Source: OECD
Artificial Intelligence transforms the way banking institutions work. It helps improve customer experience, automate business processes, and make better decisions. Its applications range from chatbots to fraud detection, credit scoring, and risk management. All these applications help banks optimize their operations and reduce costs.
With AI-driven data analytics, banks can offer personalized services, anticipate customer needs, and, most importantly, improve security. Artificial intelligence in banking is rapidly expanding. Various factors could contribute to this, including higher digitization, regulatory support, and emerging fintech innovation. Higher demands for smarter financial solutions to achieve greater operational efficiency and customer engagement fuel such an expansion.
Artificial Intelligence in Banking Market – Significant Growth Factors
The Artificial Intelligence in Banking Market presents significant growth opportunities due to several factors:
- Improved Customer Experience: AI-based technologies such as chatbots and personalized recommendations enhance customer interaction, responses are immediate, and financial services seem more tailored to improve total satisfaction and engagement.
- Fraud Detection and Prevention: The AI system monitors the pattern of transactions and reports any anomaly in real time. Hence, fraud and security risks are reduced, improving the security aspects of finance and trust.
- Operational Efficiency: Processes in which automation is implemented via AI and RPA are usually completed with a few mistakes and less costs.
- Advanced Analytics: Thanks to advanced AI-enhanced data analytics, improvements in risk analytics, portfolio management, and decision-making processes are possible, allowing banks to maximize their performance and efficiency.
- AI-Driven: Personalization: The bank could personalize and deliver more relevant products and services to specific customer needs and wants using AI. This would help with acquisition and retention.
- AI in Banking: Foraying into New Markets—The Use of AI in banking enables banks to explore newer opportunities in emerging markets, as rapid digitalization offers banks the opportunity to introduce smart technologies to new segments of customers.
Artificial Intelligence in Banking Market – Key Developments
Artificial intelligence in the banking market has grown significantly, with key players attempting to consolidate the market through mergers and acquisitions. Some notable examples include:
- In October 2024, Intellect Global Transaction Banking announced the launch of eMACH.ai Cloud for Wholesale Banking, a pioneering platform that provides wholesale banks with closely linked services.
- In 2023, Amazon Web Services (AWS) announced that Krungsri is using AWS to boost customer experiences and financial inclusion. AWS supports Krungsri’s innovative culture and collaboration among the Bank of Ayudhya.
- In 2023, Temenos, Switzerland-based software company, partnered with AWS to deliver core banking solutions via Software-as-a-Service (SaaS).
These important parts brought about diversification to some players, strengthened their positioning or market stand, and effectively tapped into new opportunists in Artificial Intelligence in the Banking Market. Thus, the trend is expected to persist, with more firms endeavoring to gain territorial advantage in the market.
COMPARATIVE ANALYSIS OF THE RELATED MARKET
| Artificial Intelligence in Banking Market | Data Center Precision Air Conditioning Market | Intelligent Building Management Systems Market |
| CAGR 22.5% (Approx) | CAGR 4.78% (Approx) | CAGR 8.3% (Approx) |
| USD 140,940.1 Million by 2033 | USD 37,627 Million by 2033 | USD 2,46.764 Million by 2033 |
Artificial Intelligence in Banking Market – Significant Threats
Threats to Artificial Intelligence in Banking Market include:
- Cybersecurity Risks: Banks are very vulnerable to dangerous cybersecurity dangers when relying much of their operations on AI systems. Therefore, complex cyber threats and malicious attacks endanger valuable data leakage, which tests the security components of AI technologies built into Foster’s system.
- Compliance Issues: Products based on artificial intelligence imply that compliance difficulties will be reinforced in areas corresponding to regulating regimes’ quickly changing and exceedingly complex character. Neglecting any data protection or financial law leads to possible judicial problems, fines, and even harm to your reputation.
- Implementation Costs: Implementing a developed AI system requires a lot of investment. The high costs of acquiring, using, and developing technology mean institutions, especially small ones, may be left with feeble veins.
- Data Privacy Issues: Customers actively share personal and financial information with AI-based systems, and using such data often causes several data privacy problems. Customer data goes to the wrong people and can lead to a violation of private data and loss of consumer trust.
- Ethics and Bias Problem: In its operation, AI may reproduce conditioning based on historical data, which can lead to what is considered discriminatory judgment. Multi-biased AI and ethical usage of AI would be required to achieve fairness and transparency in banks’ operations.
Category-Wise Insights
By Component
- Service: The service for AI in the banking market includes consulting, implementation, and support in AI technologies. They include designing, integrating, and maintaining AI systems for optimizing banking operations and interactions between banks and customers. The demand for AI services is on the rise; more banks are seeking special consulting and support to individualize the solutions offered through AI. This would include training, system integration, and subsequent maintenance towards its effective deployment and adaptation.
- Solutions: Solutions in AI in the banking market, referred to as AI-related technologies and software, that solve specific banking needs like fraud detection, customer service automation, and predictive analytics. The current trend is to embrace top-end AI solutions for customizing customer experience, real-time fraud detection, and better data analytics. Banks invest in complete AI platforms to integrate various functionalities to work more efficiently and make better decisions.
By Application
- Fraud Detection and Prevention: AI-based fraud detection uses machine learning algorithms to track suspicious activities and transactions in real time, preventing financial crimes. AI has been used more for real-time monitoring of transactions and detecting anomalies, improving fraud prevention.
- Customer Support: Customer support AI consists of virtual assistants or chatbots and their automated systems that try to answer customers’ requests, offer support, and make the service delivery process even more efficient.
- Risk Management: AI applications in risk management include predictive analytics and machine learning to assess, analyse, and mitigate various financial risks, such as credit, market, and operation risks.
- AI-driven personalized banking: Customized banking uses data analysis and artificial intelligence to tailor banking products, services, and advice to individual client choices and usage.
- Other: This category discusses other diversified uses of AI in banking that are not captured in the others, including portfolio management, loan making, and customer behavioural patterns. This type of AI is now expanding to robots in advisory, specifically in investment and the automatic approval of loan facilities.
By Technology
- Machine Learning: algorithms that make AI systems learn from available data to enhance the outcome of their performance over time without explicit programming. Use in banking: efficiency through improved fraud detection, personalization of customer engagement, and optimization of trading strategies. For financial services, there is a concentration on more complex models that will unleash additional insights, more accurate predictive capacities for innovation, and more efficient cost-invoice flows.
- Natural Language Processing (NLP): Natural Language Processing (NLP) is the technology that gives machines the understanding and ability to work with human language. Banks applied NLP through chatbots, virtual assistants, and sentiment analysis. Current study research is improving conversational AI and assisting in customer service, automating documentation processing, and drawing actionable insights from customer interactions, thus engrossing and enhancing operational efficiency.
- Robotic Process Automation (RPA): RPA is software robots that automate rule-based repeated tasks. Banking with RPA streamlines efforts to apply processes, including data entry, transaction processing, and compliance reporting. Currently, the trend is to marry RPA with AI; this will increase the scope for automation, handling more tasks, operational efficiency, cost reduction, and reductions in manual error.
- Predictive Analytics: Predictive analytics uses algorithms and machine learning techniques to predict future trends and sort out their possibilities using past data. Some recent applications of predictive analytics in banking are credit scoring and risk management, where this model is used for forecasting customer behaviour.
Report Scope
| Feature of the Report | Details |
| Market Size in 2024 | USD 22,688.7 Million |
| Projected Market Size in 2033 | USD 140,940.1 Million |
| Market Size in 2023 | USD 18,521.4 Million |
| CAGR Growth Rate | 22.5% CAGR |
| Base Year | 2023 |
| Forecast Period | 2024-2033 |
| Key Segment | By Component, Application, Technology, Enterprise Size and Region |
| Report Coverage | Revenue Estimation and Forecast, Company Profile, Competitive Landscape, Growth Factors and Recent Trends |
| Regional Scope | North America, Europe, Asia Pacific, Middle East & Africa, and South & Central America |
| Buying Options | Request tailored purchasing options to fulfil your research requirements. |
Artificial Intelligence in Banking Market: Regional Analysis
The Artificial Intelligence in Banking Market is segmented into regions: North America, Europe, Asia-Pacific, and LAMEA. Here is a brief overview of each region:
- North America: North America consists of the U.S. and Canada, where technologically advanced banking systems and technology integration are prevalent. There has been a significant emphasis on adopting innovative AI technologies to encourage customer experiences, fraud detection, and regulation adherence. Banks are investing heavily in AI-based innovation like predictive analytics and personalized financial services to stay abreast of the fast-changing dynamics due to regulations.
- Europe: Europe consists of many countries, and the extent of AI implementation in banks differs between these countries. European banks have considered AI to be developed because it is a strict regulatory compliance measure to abide by laws like GDPR. Here, AI is widely accepted for risk management and AML operations. In addition, AI is integrated into the institutions of Europe, which further assists in open banking initiatives to enhance financial transparency and innovation.
- Asia-Pacific: Asia-Pacific consists of robust growth economies and geographically diverse banking markets, with widely varied technological adoption. Asia-Pacific is quickly transforming digitally, focusing on AI for mobile banking and even customer service automation. Banks use AI to support financial inclusion and offer the growing middle class innovative products. A lot of investment is also put into AI for better efficiency in operations and handling big transaction data.
- LAMEA: The LAMEA region is also an emerging market with challenges and opportunities regarding banking technology. In LAMEA, the adoption of AI is more focused on improving financial inclusion and enhancing the accessibility of banking services. Banks use AI for fraud detection, credit scoring, and customer service in regions with different economic conditions. Cost-effective AI solutions are meant to benefit the regions, focusing on developing financial literacy and inclusion.
Competitive Landscape: Artificial Intelligence in Banking Market
The Artificial Intelligence in Banking Market is highly competitive, with many service providers globally. Some of the key players in the market include:
- IBM Corporation
- Microsoft Corporation
- Google LLC
- Amazon Web Services (AWS)
- Salesforce.com Inc.
- SAS Institute Inc.
- Oracle Corporation
- SAP SE
- NVIDIA Corporation
- Cognizant Technology Solutions Corporation
- Accenture plc
- Infosys Limited
- TIBCO Software Inc.
- H2O.ai
- ThoughtSpot Inc.
- Others
These companies operate in the market through various strategies such as innovation, mergers and acquisitions, and partnerships.
“Innovations like advanced machine learning, or rather, bringing industries on board like H2O.ai and ThoughtSpot, have also given these players access to the AI in banking services,’ This is because they aim at offering complex, easy-to-use services to meet the changing demands.
Key players such as IBM, Microsoft, and Google are all-pervasive in the market thanks to their huge investments and modern technology, proving hard for any competitor to decide into the market. They help the banking sector worldwide by deploying powerful platforms geared towards AI, control over predictive analytics tools, and the ability to carry out extensive and complex integrations that streamline operations, enhance customer service, and improve the security of the banking sector.
The Artificial Intelligence in Banking Market is segmented as follows:
By Component
- Service
- Solution
By Application
- Fraud Detection and Prevention
- Transaction Monitoring
- Identity Verification
- Customer Service
- Virtual Assistants
- Automated Customer Support
- Risk Management
- Credit Scoring
- Market Risk Analysis
- Personalized Banking
- Customer Recommendations
- Targeted Marketing
- Compliance and Regulatory Reporting
- Anti-Money Laundering (AML)
- Know Your Customer (KYC)
- Others
By Technology
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Natural Language Processing (NLP)
- Text Analysis
- Speech Recognition
- Chatbots and Virtual Assistants
- Robotic Process Automation (RPA)
- Process Automation
- Workflow Automation
- Predictive Analytics
- Risk Management
- Customer Insights
By Enterprise Size
- Large Enterprise
- SMEs
Regional Coverage:
North America
- U.S.
- Canada
- Mexico
- Rest of North America
Europe
- Germany
- France
- U.K.
- Russia
- Italy
- Spain
- Netherlands
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- New Zealand
- Australia
- South Korea
- Taiwan
- Rest of Asia Pacific
The Middle East & Africa
- Saudi Arabia
- UAE
- Egypt
- Kuwait
- South Africa
- Rest of the Middle East & Africa
Latin America
- Brazil
- Argentina
- Rest of Latin America
Table of Contents
- Chapter 1. Preface
- 1.1 Report Description and Scope
- 1.2 Research scope
- 1.3 Research methodology
- 1.3.1 Market Research Type
- 1.3.2 Market Research Methodology
- Chapter 2. Executive Summary
- 2.1 Global Artificial Intelligence in Banking Market, (2024 – 2033) (USD Million)
- 2.2 Global Artificial Intelligence in Banking Market: snapshot
- Chapter 3. Global Artificial Intelligence in Banking Market – Industry Analysis
- 3.1 Artificial Intelligence in Banking Market: Market Dynamics
- 3.2 Market Drivers
- 3.2.1 Improved Customer Experience
- 3.2.2 Fraud Detection and Prevention
- 3.2.3 Operational Efficiency
- 3.2.4 Advanced Analytics
- 3.2.5 AI-Driven
- 3.2.6 AI in Banking.
- 3.3 Market Restraints
- 3.4 Market Opportunities
- 3.5 Market Challenges
- 3.6 Porter’s Five Forces Analysis
- 3.7 Market Attractiveness Analysis
- 3.7.1 Market Attractiveness Analysis By Component
- 3.7.2 Market Attractiveness Analysis By Application
- 3.7.3 Market Attractiveness Analysis By Technology
- 3.7.4 Market Attractiveness Analysis By Enterprise Size
- Chapter 4. Global Artificial Intelligence in Banking Market- Competitive Landscape
- 4.1 Company market share analysis
- 4.1.1 Global Artificial Intelligence in Banking Market: Company Market Share, 2023
- 4.2 Strategic development
- 4.2.1 Acquisitions & mergers
- 4.2.2 New Product launches
- 4.2.3 Agreements, partnerships, collaboration, and joint ventures
- 4.2.4 Research and development and Regional expansion
- 4.3 Price trend analysis
- 4.1 Company market share analysis
- Chapter 5. Global Artificial Intelligence in Banking Market – Component Analysis
- 5.1 Global Artificial Intelligence in Banking Market Overview: By Component
- 5.1.1 Global Artificial Intelligence in Banking Market Share, By Component, 2023 and 2033
- 5.2 Service
- 5.2.1 Global Artificial Intelligence in Banking Market by Service, 2024 – 2033 (USD Million)
- 5.3 Solution
- 5.3.1 Global Artificial Intelligence in Banking Market by Solution, 2024 – 2033 (USD Million)
- 5.1 Global Artificial Intelligence in Banking Market Overview: By Component
- Chapter 6. Global Artificial Intelligence in Banking Market: Application Analysis
- 6.1 Global Artificial Intelligence in Banking Market Overview: By Application
- 6.1.1 Global Artificial Intelligence in Banking Market Share, By Application, 2023 and 2033
- 6.2 Fraud Detection and Prevention
- 6.2.1 Global Artificial Intelligence in Banking Market by Fraud Detection and Prevention, 2024 – 2033 (USD Million)
- 6.3 Transaction Monitoring
- 6.3.1 Global Artificial Intelligence in Banking Market by Transaction Monitoring, 2024 – 2033 (USD Million)
- 6.4 Identity Verification
- 6.4.1 Global Artificial Intelligence in Banking Market by Identity Verification, 2024 – 2033 (USD Million)
- 6.5 Customer Service
- 6.5.1 Global Artificial Intelligence in Banking Market by Customer Service, 2024 – 2033 (USD Million)
- 6.6 Virtual Assistants
- 6.6.1 Global Artificial Intelligence in Banking Market by Virtual Assistants, 2024 – 2033 (USD Million)
- 6.7 Automated Customer Support
- 6.7.1 Global Artificial Intelligence in Banking Market by Automated Customer Support, 2024 – 2033 (USD Million)
- 6.8 Risk Management
- 6.8.1 Global Artificial Intelligence in Banking Market by Risk Management, 2024 – 2033 (USD Million)
- 6.9 Credit Scoring
- 6.9.1 Global Artificial Intelligence in Banking Market by Credit Scoring, 2024 – 2033 (USD Million)
- 6.10 Market Risk Analysis
- 6.10.1 Global Artificial Intelligence in Banking Market by Market Risk Analysis, 2024 – 2033 (USD Million)
- 6.11 Personalized Banking
- 6.11.1 Global Artificial Intelligence in Banking Market by Personalized Banking, 2024 – 2033 (USD Million)
- 6.12 Customer Recommendations
- 6.12.1 Global Artificial Intelligence in Banking Market by Customer Recommendations, 2024 – 2033 (USD Million)
- 6.13 Targeted Marketing
- 6.13.1 Global Artificial Intelligence in Banking Market by Targeted Marketing, 2024 – 2033 (USD Million)
- 6.14 Compliance and Regulatory Reporting
- 6.14.1 Global Artificial Intelligence in Banking Market by Compliance and Regulatory Reporting, 2024 – 2033 (USD Million)
- 6.15 Anti-Money Laundering (AML)
- 6.15.1 Global Artificial Intelligence in Banking Market by Anti-Money Laundering (AML), 2024 – 2033 (USD Million)
- 6.16 Know Your Customer (KYC)
- 6.16.1 Global Artificial Intelligence in Banking Market by Know Your Customer (KYC), 2024 – 2033 (USD Million)
- 6.17 Others
- 6.17.1 Global Artificial Intelligence in Banking Market by Others, 2024 – 2033 (USD Million)
- 6.1 Global Artificial Intelligence in Banking Market Overview: By Application
- Chapter 7. Global Artificial Intelligence in Banking Market: Technology Analysis
- 7.1 Global Artificial Intelligence in Banking Market Overview: By Technology
- 7.1.1 Global Artificial Intelligence in Banking Market Share, By Technology, 2023 and 2033
- 7.2 Machine Learning
- 7.2.1 Global Artificial Intelligence in Banking Market by Machine Learning, 2024 – 2033 (USD Million)
- 7.3 Supervised Learning
- 7.3.1 Global Artificial Intelligence in Banking Market by Supervised Learning, 2024 – 2033 (USD Million)
- 7.4 Unsupervised Learning
- 7.4.1 Global Artificial Intelligence in Banking Market by Unsupervised Learning, 2024 – 2033 (USD Million)
- 7.5 Reinforcement Learning
- 7.5.1 Global Artificial Intelligence in Banking Market by Reinforcement Learning, 2024 – 2033 (USD Million)
- 7.6 Natural Language Processing (NLP)
- 7.6.1 Global Artificial Intelligence in Banking Market by Natural Language Processing (NLP), 2024 – 2033 (USD Million)
- 7.7 Text Analysis
- 7.7.1 Global Artificial Intelligence in Banking Market by Text Analysis, 2024 – 2033 (USD Million)
- 7.8 Speech Recognition
- 7.8.1 Global Artificial Intelligence in Banking Market by Speech Recognition, 2024 – 2033 (USD Million)
- 7.9 Chatbots and Virtual Assistants
- 7.9.1 Global Artificial Intelligence in Banking Market by Chatbots and Virtual Assistants, 2024 – 2033 (USD Million)
- 7.10 Robotic Process Automation (RPA)
- 7.10.1 Global Artificial Intelligence in Banking Market by Robotic Process Automation (RPA), 2024 – 2033 (USD Million)
- 7.11 Process Automation
- 7.11.1 Global Artificial Intelligence in Banking Market by Process Automation, 2024 – 2033 (USD Million)
- 7.12 Workflow Automation
- 7.12.1 Global Artificial Intelligence in Banking Market by Workflow Automation, 2024 – 2033 (USD Million)
- 7.13 Predictive Analytics
- 7.13.1 Global Artificial Intelligence in Banking Market by Predictive Analytics, 2024 – 2033 (USD Million)
- 7.14 Risk Management
- 7.14.1 Global Artificial Intelligence in Banking Market by Risk Management, 2024 – 2033 (USD Million)
- 7.15 Customer Insights
- 7.15.1 Global Artificial Intelligence in Banking Market by Customer Insights, 2024 – 2033 (USD Million)
- 7.1 Global Artificial Intelligence in Banking Market Overview: By Technology
- Chapter 8. Global Artificial Intelligence in Banking Market: Enterprise Size Analysis
- 8.1 Global Artificial Intelligence in Banking Market Overview: By Enterprise Size
- 8.1.1 Global Artificial Intelligence in Banking Market Share, By Enterprise Size, 2023 and 2033
- 8.2 Large Enterprise
- 8.2.1 Global Artificial Intelligence in Banking Market by Large Enterprise, 2024 – 2033 (USD Million)
- 8.3 SMEs
- 8.3.1 Global Artificial Intelligence in Banking Market by SMEs, 2024 – 2033 (USD Million)
- 8.1 Global Artificial Intelligence in Banking Market Overview: By Enterprise Size
- Chapter 9. Artificial Intelligence in Banking Market – Regional Analysis
- 9.1 Global Artificial Intelligence in Banking Market Regional Overview
- 9.2 Global Artificial Intelligence in Banking Market Share, by Region, 2023 & 2033 (USD Million)
- 9.3. North America
- 9.3.1 North America Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.3.1.1 North America Artificial Intelligence in Banking Market, by Country, 2024 – 2033 (USD Million)
- 9.3.1 North America Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.4 North America Artificial Intelligence in Banking Market, by Component, 2024 – 2033
- 9.4.1 North America Artificial Intelligence in Banking Market, by Component, 2024 – 2033 (USD Million)
- 9.5 North America Artificial Intelligence in Banking Market, by Application, 2024 – 2033
- 9.5.1 North America Artificial Intelligence in Banking Market, by Application, 2024 – 2033 (USD Million)
- 9.6 North America Artificial Intelligence in Banking Market, by Technology, 2024 – 2033
- 9.6.1 North America Artificial Intelligence in Banking Market, by Technology, 2024 – 2033 (USD Million)
- 9.7 North America Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033
- 9.7.1 North America Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033 (USD Million)
- 9.8. Europe
- 9.8.1 Europe Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.8.1.1 Europe Artificial Intelligence in Banking Market, by Country, 2024 – 2033 (USD Million)
- 9.8.1 Europe Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.9 Europe Artificial Intelligence in Banking Market, by Component, 2024 – 2033
- 9.9.1 Europe Artificial Intelligence in Banking Market, by Component, 2024 – 2033 (USD Million)
- 9.10 Europe Artificial Intelligence in Banking Market, by Application, 2024 – 2033
- 9.10.1 Europe Artificial Intelligence in Banking Market, by Application, 2024 – 2033 (USD Million)
- 9.11 Europe Artificial Intelligence in Banking Market, by Technology, 2024 – 2033
- 9.11.1 Europe Artificial Intelligence in Banking Market, by Technology, 2024 – 2033 (USD Million)
- 9.12 Europe Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033
- 9.12.1 Europe Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033 (USD Million)
- 9.13. Asia Pacific
- 9.13.1 Asia Pacific Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.13.1.1 Asia Pacific Artificial Intelligence in Banking Market, by Country, 2024 – 2033 (USD Million)
- 9.13.1 Asia Pacific Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.14 Asia Pacific Artificial Intelligence in Banking Market, by Component, 2024 – 2033
- 9.14.1 Asia Pacific Artificial Intelligence in Banking Market, by Component, 2024 – 2033 (USD Million)
- 9.15 Asia Pacific Artificial Intelligence in Banking Market, by Application, 2024 – 2033
- 9.15.1 Asia Pacific Artificial Intelligence in Banking Market, by Application, 2024 – 2033 (USD Million)
- 9.16 Asia Pacific Artificial Intelligence in Banking Market, by Technology, 2024 – 2033
- 9.16.1 Asia Pacific Artificial Intelligence in Banking Market, by Technology, 2024 – 2033 (USD Million)
- 9.17 Asia Pacific Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033
- 9.17.1 Asia Pacific Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033 (USD Million)
- 9.18. Latin America
- 9.18.1 Latin America Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.18.1.1 Latin America Artificial Intelligence in Banking Market, by Country, 2024 – 2033 (USD Million)
- 9.18.1 Latin America Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.19 Latin America Artificial Intelligence in Banking Market, by Component, 2024 – 2033
- 9.19.1 Latin America Artificial Intelligence in Banking Market, by Component, 2024 – 2033 (USD Million)
- 9.20 Latin America Artificial Intelligence in Banking Market, by Application, 2024 – 2033
- 9.20.1 Latin America Artificial Intelligence in Banking Market, by Application, 2024 – 2033 (USD Million)
- 9.21 Latin America Artificial Intelligence in Banking Market, by Technology, 2024 – 2033
- 9.21.1 Latin America Artificial Intelligence in Banking Market, by Technology, 2024 – 2033 (USD Million)
- 9.22 Latin America Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033
- 9.22.1 Latin America Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033 (USD Million)
- 9.23. The Middle-East and Africa
- 9.23.1 The Middle-East and Africa Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.23.1.1 The Middle-East and Africa Artificial Intelligence in Banking Market, by Country, 2024 – 2033 (USD Million)
- 9.23.1 The Middle-East and Africa Artificial Intelligence in Banking Market, 2024 – 2033 (USD Million)
- 9.24 The Middle-East and Africa Artificial Intelligence in Banking Market, by Component, 2024 – 2033
- 9.24.1 The Middle-East and Africa Artificial Intelligence in Banking Market, by Component, 2024 – 2033 (USD Million)
- 9.25 The Middle-East and Africa Artificial Intelligence in Banking Market, by Application, 2024 – 2033
- 9.25.1 The Middle-East and Africa Artificial Intelligence in Banking Market, by Application, 2024 – 2033 (USD Million)
- 9.26 The Middle-East and Africa Artificial Intelligence in Banking Market, by Technology, 2024 – 2033
- 9.26.1 The Middle-East and Africa Artificial Intelligence in Banking Market, by Technology, 2024 – 2033 (USD Million)
- 9.27 The Middle-East and Africa Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033
- 9.27.1 The Middle-East and Africa Artificial Intelligence in Banking Market, by Enterprise Size, 2024 – 2033 (USD Million)
- Chapter 10. Company Profiles
- 10.1 IBM Corporation
- 10.1.1 Overview
- 10.1.2 Financials
- 10.1.3 Product Portfolio
- 10.1.4 Business Strategy
- 10.1.5 Recent Developments
- 10.2 Microsoft Corporation
- 10.2.1 Overview
- 10.2.2 Financials
- 10.2.3 Product Portfolio
- 10.2.4 Business Strategy
- 10.2.5 Recent Developments
- 10.3 Google LLC
- 10.3.1 Overview
- 10.3.2 Financials
- 10.3.3 Product Portfolio
- 10.3.4 Business Strategy
- 10.3.5 Recent Developments
- 10.4 Amazon Web Services (AWS)
- 10.4.1 Overview
- 10.4.2 Financials
- 10.4.3 Product Portfolio
- 10.4.4 Business Strategy
- 10.4.5 Recent Developments
- 10.5 Salesforce.com Inc.
- 10.5.1 Overview
- 10.5.2 Financials
- 10.5.3 Product Portfolio
- 10.5.4 Business Strategy
- 10.5.5 Recent Developments
- 10.6 SAS Institute Inc.
- 10.6.1 Overview
- 10.6.2 Financials
- 10.6.3 Product Portfolio
- 10.6.4 Business Strategy
- 10.6.5 Recent Developments
- 10.7 Oracle Corporation
- 10.7.1 Overview
- 10.7.2 Financials
- 10.7.3 Product Portfolio
- 10.7.4 Business Strategy
- 10.7.5 Recent Developments
- 10.8 SAP SE
- 10.8.1 Overview
- 10.8.2 Financials
- 10.8.3 Product Portfolio
- 10.8.4 Business Strategy
- 10.8.5 Recent Developments
- 10.9 NVIDIA Corporation
- 10.9.1 Overview
- 10.9.2 Financials
- 10.9.3 Product Portfolio
- 10.9.4 Business Strategy
- 10.9.5 Recent Developments
- 10.10 Cognizant Technology Solutions Corporation
- 10.10.1 Overview
- 10.10.2 Financials
- 10.10.3 Product Portfolio
- 10.10.4 Business Strategy
- 10.10.5 Recent Developments
- 10.11 Accenture plc
- 10.11.1 Overview
- 10.11.2 Financials
- 10.11.3 Product Portfolio
- 10.11.4 Business Strategy
- 10.11.5 Recent Developments
- 10.12 Infosys Limited
- 10.12.1 Overview
- 10.12.2 Financials
- 10.12.3 Product Portfolio
- 10.12.4 Business Strategy
- 10.12.5 Recent Developments
- 10.13 TIBCO Software Inc.
- 10.13.1 Overview
- 10.13.2 Financials
- 10.13.3 Product Portfolio
- 10.13.4 Business Strategy
- 10.13.5 Recent Developments
- 10.14 H2O.ai
- 10.14.1 Overview
- 10.14.2 Financials
- 10.14.3 Product Portfolio
- 10.14.4 Business Strategy
- 10.14.5 Recent Developments
- 10.15 ThoughtSpot Inc.
- 10.15.1 Overview
- 10.15.2 Financials
- 10.15.3 Product Portfolio
- 10.15.4 Business Strategy
- 10.15.5 Recent Developments
- 10.16 Others.
- 10.16.1 Overview
- 10.16.2 Financials
- 10.16.3 Product Portfolio
- 10.16.4 Business Strategy
- 10.16.5 Recent Developments
- 10.1 IBM Corporation
List Of Figures
Figures No 1 to 52
List Of Tables
Tables No 1 to 102
Report Methodology
In order to get the most precise estimates and forecasts possible, Custom Market Insights applies a detailed and adaptive research methodology centered on reducing deviations. For segregating and assessing quantitative aspects of the market, the company uses a combination of top-down and bottom-up approaches. Furthermore, data triangulation, which examines the market from three different aspects, is a recurring theme in all of our research reports. The following are critical components of the methodology used in all of our studies:
Preliminary Data Mining
On a broad scale, raw market information is retrieved and compiled. Data is constantly screened to make sure that only substantiated and verified sources are taken into account. Furthermore, data is mined from a plethora of reports in our archive and also a number of reputed & reliable paid databases. To gain a detailed understanding of the business, it is necessary to know the entire product life cycle and to facilitate this, we gather data from different suppliers, distributors, and buyers.
Surveys, technological conferences, and trade magazines are used to identify technical issues and trends. Technical data is also gathered from the standpoint of intellectual property, with a focus on freedom of movement and white space. The dynamics of the industry in terms of drivers, restraints, and valuation trends are also gathered. As a result, the content created contains a diverse range of original data, which is then cross-validated and verified with published sources.
Statistical Model
Simulation models are used to generate our business estimates and forecasts. For each study, a one-of-a-kind model is created. Data gathered for market dynamics, the digital landscape, development services, and valuation patterns are fed into the prototype and analyzed concurrently. These factors are compared, and their effect over the projected timeline is quantified using correlation, regression, and statistical modeling. Market forecasting is accomplished through the use of a combination of economic techniques, technical analysis, industry experience, and domain knowledge.
Short-term forecasting is typically done with econometric models, while long-term forecasting is done with technological market models. These are based on a synthesis of the technological environment, legal frameworks, economic outlook, and business regulations. Bottom-up market evaluation is favored, with crucial regional markets reviewed as distinct entities and data integration to acquire worldwide estimates. This is essential for gaining a thorough knowledge of the industry and ensuring that errors are kept to a minimum.
Some of the variables taken into account for forecasting are as follows:
• Industry drivers and constraints, as well as their current and projected impact
• The raw material case, as well as supply-versus-price trends
• Current volume and projected volume growth through 2033
We allocate weights to these variables and use weighted average analysis to determine the estimated market growth rate.
Primary Validation
This is the final step in our report’s estimating and forecasting process. Extensive primary interviews are carried out, both in-person and over the phone, to validate our findings and the assumptions that led to them.
Leading companies from across the supply chain, including suppliers, technology companies, subject matter experts, and buyers, use techniques like interviewing to ensure a comprehensive and non-biased overview of the business. These interviews are conducted all over the world, with the help of local staff and translators, to overcome language barriers.
Primary interviews not only aid with data validation, but also offer additional important insight into the industry, existing business scenario, and future projections, thereby improving the quality of our reports.
All of our estimates and forecasts are validated through extensive research work with key industry participants (KIPs), which typically include:
• Market leaders
• Suppliers of raw materials
• Suppliers of raw materials
• Buyers.
The following are the primary research objectives:
• To ensure the accuracy and acceptability of our data.
• Gaining an understanding of the current market and future projections.
Data Collection Matrix
| Perspective | Primary research | Secondary research |
| Supply-side |
|
|
| Demand-side |
|
|
Market Analysis Matrix
| Qualitative analysis | Quantitative analysis |
|
|
FAQs
The key factors driving the Market are Improved Customer Experience, Fraud Detection and Prevention, Operational Efficiency, Advanced Analytics, AI-Driven, AI in Banking.
The “Fraud Detection and Prevention” had the largest share in the global market for Artificial Intelligence in Banking.
The “Machine Learning” category dominated the market in 2023.
The key players in the market are IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services (AWS), Salesforce.com Inc., SAS Institute Inc., Oracle Corporation, SAP SE, NVIDIA Corporation, Cognizant Technology Solutions Corporation, Accenture plc, Infosys Limited, TIBCO Software Inc., H2O.ai, ThoughtSpot Inc., Others.
“North America” had the largest share in the Artificial Intelligence in Banking Market.
The global market is projected to grow at a CAGR of 22.5% during the forecast period, 2024-2033.
The Artificial Intelligence in Banking Market size was valued at USD 22,688.7 Million in 2024.