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Artificial Intelligence (AI) in Banking Market - Forecasts from 2022 to 2027

Published: | Knowledge Sourcing Intelligence | 125 Pages | Delivery time: 1-2 business days


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Artificial Intelligence (AI) in Banking Market - Forecasts from 2022 to 2027
Published: May 13, 2022
Knowledge Sourcing Intelligence
Content info: 125 Pages
Delivery time: 1-2 business days
  • Description
  • Table of Contents

The global AI in banking market size was valued at US$4.104 billion in 2020 and is projected to grow at a CAGR of 36.31% during the forecast period to reach US$35.884 billion by 2027.

The increasing adaptation of advanced technologies such as AI-based accounting software for retail and commercial banks has increased the demand for hassle-free online and mobile banking services. This trend of offering user-friendly services will drive the growth of the market from 2021 to 2027.

By investing in artificial intelligence (AI) with banks' coherent technology, banks can gain digital advantages and compete with FinTech players. Artificial intelligence is the future of banks as it provides the power of advanced data analysis to combat fraudulent transactions and improve compliance. The AI algorithm performs money-laundering prevention activities in seconds. Otherwise, it will take hours to days. With AI, banks can manage large amounts of data at record speed and drive valuable insights from them. Features such as AI bots, digital payment advisors, and biometric fraud detection mechanisms enable a higher quality of service across a large customer base. All of this leads to higher revenue, lower costs, and high profits.

The Advantages of Global AI in the Banking Industry Because artificial intelligence has become an integral part of people's lives in the modern era of development, banks have begun integrating AI-based technology with their existing technology to meet end-user demand. The major developments in the artificial intelligence field are:

  • Cyber Security and Fraud Detection: A large number of day-to-day transactions on various online media and apps occur digitally. For this purpose, banks need to push up their cyber security and fraud detection capabilities. This is where AI comes into play, assisting banks in filling gaps in their security systems, mitigating risk, and managing online transactions smoothly.
  • Chabot's: Chabot's are one of the best examples of artificial intelligence in the banking industry. Once the bots are positioned, they can work for 24*7 unlike humans, who have fixed timings to work on.
  • Customer Experience: Consumers demand convenience and a user-friendly experience. ATMs are a huge success because of their ease of access. Customers can withdraw money at their own convenience. This led to the innovation of bringing AI into the banking sector to enhance this experience so a customers can access all the advanced services from the ease of their home.
  • Risk Management: External global factors such as currency fluctuations, natural disasters, and political instability have serious implications for the banking and financial industries. In these volatile times, it is important to be extra careful when making business decisions. The AI-driven analysis provides a much clearer outlook for the future, allowing you to be ready and make timely decisions.
  • Regulatory Compliance: Around the globe, banks are one of the most regulated sectors. Globally, governments have set up regulatory agencies to ensure that bank customers do not use banks to commit financial crimes and that banks have an acceptable risk profile to avoid large-scale defaults. To read new compliance requirements, AI uses deep learning and NLP, which makes the work of compliance analysts faster and easier.

Challenges in AI in the Banking Market Globally

Implementing cutting-edge technologies such as artificial intelligence on a global scale will not be easy. . From security issues to lack of credible and quality data, there are a lot more challenges that are faced by banks adapting to artificial intelligence technology. One of the major challenges is the large amount of sensitive information that is collected in a large amount of data that requires security measures to be implemented. So, for this, getting the right technology partner to provide data security is crucial. Banks need structured, high-quality data for training and validation before deploying a comprehensive AI-based banking solution. High-quality data is required to be able to apply the algorithm to real-time situations.

Key Development in AI in the Banking Market Globally

  • Tenet Fintech Group acquired AI software provider Cubeler Inc.
  • Square acquired the Australian firm Afterpay.
  • Ocrolus and Blend Announce Partnership
  • DataRobots acquired ML Ops Space Algorithmia

Covid Impact

The COVID-19 pandemic has led companies to embrace the culture of working from home, and the banking sector is rapidly adopting AI and machine learning tools. The burgeoning of COVID-19 is expected to drive AI in the banking market as the pandemic increases the demand for money-laundering prevention (AML) and fraud detection solutions. Advances in digitalization have required AI technology to reduce the load on bank servers. The pandemic has created a need for AI-powered tools to handle the surge in customer demand.

Regional Analysis of the Global AI in Banking Market

North America is expecting growth due to the increasing use of rapidly evolving digital technologies such as data analytics, AI, blockchain, IoT, cloud computing, and all Internet-based services in the region. It is expected to dominate the global AI of the banking industry. According to the latest report from the United Nations Conference on Trade and Development, IoT devices are estimated to grow from 9.9 billion in 2019 to 21.5 billion in 2025, with the United States accounting for about 50% of the device's global IoT spending The Asia Pacific region is expected to become the fastest growing regional market for AI in banks due to the increasing digitization of the banking sector in the region. In addition, government policies and initiatives to promote the adoption of artificial intelligence (AI) in various sectors, including banks, and the adoption of innovative technologies in developing countries such as China and India are expected during the forecast period.

Market Segmentation:

  • By Solution




  • By Application

Customer Service

Robot Advice

General purpose/Predictive Analysis

Cyber Security

Direct Learning

  • By Geography

North America

  • USA
  • Canada
  • Mexico

South America

  • Brazil
  • Argentina
  • Others


  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Others

Middle East and Africa

  • Saudi Arabia
  • UAE
  • Israel
  • Others

Asia Pacific

  • China
  • Japan
  • South Korea
  • India
  • Thailand
  • Taiwan
  • Indonesia
  • Others
Product Code: KSI061613571



  • 1.1. Market Definition
  • 1.2. Market Segmentation


  • 2.1. Research Data
  • 2.2. Assumptions


  • 3.1. Research Highlights


  • 4.1. Market Drivers
  • 4.2. Market Restraints
  • 4.3. Porter's Five Forces Analysis
    • 4.3.1. Bargaining Power of Suppliers
    • 4.3.2. Bargaining Powers of Buyers
    • 4.3.3. Threat of Substitutes
    • 4.3.4. Threat of New Entrants
    • 4.3.5. Competitive Rivalry in Industry
  • 4.4. Industry Value Chain Analysis


  • 5.1. Introduction
  • 5.2. Hardware
  • 5.3. Software
  • 5.4. Services 


  • 6.1. Introduction
  • 6.2. Customer Service/Engagement
  • 6.3. Robo Advice
  • 6.4. General Purpose/Predictive Analysis
  • 6.5. Cybersecurity
  • 6.6. Direct Learning


  • 7.1. Introduction
  • 7.2. North America
    • 7.2.1. United States
    • 7.2.2. Canada
    • 7.2.3. Mexico
  • 7.3. South America
    • 7.3.1. Brazil
    • 7.3.2. Argentina
    • 7.3.3. Others
  • 7.4. Europe
    • 7.4.1. Germany
    • 7.4.2. France
    • 7.4.3. United Kingdom 
    • 7.4.4. Spain 
    • 7.4.5. Others
  • 7.5. Middle East and Africa
    • 7.5.1. Saudi Arabia
    • 7.5.2. UAE
    • 7.5.3. Israel
    • 7.5.4. Others
  • 7.6. Asia Pacific
    • 7.6.1. China
    • 7.6.2. India
    • 7.6.3. South Korea
    • 7.6.4. Taiwan
    • 7.6.5. Thailand
    • 7.6.6. Indonesia 
    • 7.6.7. Japan
    • 7.6.8. Others


  • 8.1. Major Players and Strategy Analysis
  • 8.2. Emerging Players and Market Lucrativeness
  • 8.3. Mergers, Acquisition, Agreements, and Collaborations
  • 8.4. Vendor Competitiveness Matrix


  • 9.1. Zest AI
  • 9.2. IBM
  • 9.3. Data Robot Inc.
  • 9.4. Accenture
  • 9.5. Personetics Technologies
  • 9.6. Kensho Technologies, LLC
  • 9.7. Wipro
  • 9.8. Intel
  • 9.9. SAP
  • 9.10. Temenos
  • 9.11. SAS
  • 9.12. Abe AI
  • 9.13. OSP Labs