Artificial Intelligence (AI) In Banking Market Size, Share, Opportunities, And Trends By Solution (Hardware, Software, Services), By Application (Customer Service, Robot Advice, General Purpose/Predictive Analysis, Cyber Security, Direct Learning), And By Geography - Forecasts From 2024 To 2029

Artificial intelligence (AI) in the banking market is estimated to grow at a CAGR of 55.53% to attain US$54.635 billion in 2029, increasing from US$17.383 billion in 2024.

In the banking sector, AI or artificial intelligence is set to play a critical role. In this sector, AI-based tools offer multiple benefits, which include personalizing customer service and experience. They can also automate various processes and systems, enriching the customer service experience. AI-based tools can also help detect fraud and other illegal activities, like money laundering.

The increasing adaptation of advanced technologies, such as AI-based accounting software, has increased the demand for hassle-free online and mobile banking services.

By integrating AI with banks through investment in coherent technology, banks can gain digital benefits and compete with FinTech companies.

Further, the AI algorithm performs money-laundering prevention activities in seconds. Otherwise, it will take hours to days. In addition, AI 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. Major developments in the market are due to enhancing customer benefits, increasing risk management, and following regulatory compliance. Sensitive information security issues can hinder market expansion, making the deployment of data security with AI integration in the banking sector crucial.

What are Artificial Intelligence (AI) in banking market drivers?

• Increasing 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 customers can access all the advanced services from the ease of their homes. It also streamlined the bank's workflow by assisting its consumers with banking-related difficulties.

For instance, in April 2024, Salesforce presented AI-powered capabilities built on the Einstein 1 Platform to help banks in dealing with transaction disputes more productively. These capabilities, including Transaction Dispute Management and Einstein Copilot Banking Actions, combine transaction information from banking stages with client information from Salesforce, computerizing manual errands, decreasing errors, and progressing client communications. The AI-powered handle streamlines the dispute process, whereas Einstein Copilot Banking Actions computerizes errands like updating client details and personalization communication via email.

Additionally, 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. Chabots are one of the best examples of AI in the banking industry.  Once the bots are positioned, they can work 24*7, unlike humans, who have fixed timings to work on.

• Rising regulatory compliance and risk management.

A major factor propelling global AI in the banking sector’s growth is the increasing introduction of strict regulations and compliance norms, which is generally difficult to track. Banks are one of the most regulated sectors, with governments worldwide setting up regulatory agencies to ensure that their customers do not use banks to commit financial crimes and that banks have an acceptable risk profile to avoid large-scale defaults. In this, AI can offer advanced algorithms and tools like deep learning and NLP, offering faster and easier compliance analysis. High-quality data is required to be able to apply the algorithm to real-time situations while protecting the information being transformed.

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.

Major restraints in Artificial Intelligence (AI) in the banking market:

• Data security challenges

Implementing cutting-edge technologies such as AI on a global scale will not be easy. From security issues to a lack of credible and quality data, banks are facing many challenges when adopting AI technology. One of the major challenges is the large amount of sensitive information that is collected in a large amount of data, which requires security measures to be implemented. Hence, 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.

Geographical outlook of Artificial Intelligence (AI) in the banking market:

• North America is forecasted to hold a major share of the Artificial Intelligence (AI) in the banking market.

North America is anticipated to grow due to the rising utilization of rapidly evolving digital technologies such as data analytics, AI, blockchain, IoT, cloud computing, and all Internet-based services in the region.

This region is also expected to dominate the global AI in the banking industry. According to the latest report from the United Nations Conference on Trade and Development, IoT devices with cellular connections are expected to grow from 320.6 million IoT connections in 2024 to account for 573.5 million IoT connections by 2029 in North America. This will increase the requirement to provide consumers with AI in Bank services, propelling the market expansion in the coming years.

Moreover, the regional market is expected to witness growth due to the increasing digitization of the banking sector. In addition, government policies and initiatives to promote the adoption of AI in various sectors, including banks, and the adoption of innovative technologies in developing countries such as the United States and Canada are expected during the forecast period.

Major players and products in Artificial Intelligence (AI) in the banking market:

• Intel Corporation is among the leading global technological companies based in the USA. The company offers various solutions for industries like automobiles, AI, security, retail, and financial services, among many others. Its products include processors, AI accelerators, software, and network-to-edge technology. For the banking sector, the company offers Intel Financial Service Technologies, which includes digital transformation, analytics & AI, and cloud computing.
• Wipro Limited is an Indian technological leader that offers products and solutions for multiple industries, including banking, capital market, consumer electronics, education, medical devices, and natural resources, among many others. The company's services include AI, cybersecurity, cloud, engineering, and consulting. In the AI in the banking market, the company offers Emotional AI, which offers a wide range of applications, like personalized customer services, financial wellness support, risk management, and fraud detection, among others.

Key developments in Artificial Intelligence (AI) in the banking market:

• In June 2024, NatWest introduced Cora+, a generative AI update to its computerized assistant, Cora, created in collaboration with IBM. The new adaptation will be presented amid London Tech Week and is one of the primary UK banks to deploy generative AI through a digital assistant. Cora supports clients 24/7 by replying to banking queries utilizing common language processing and ML capabilities.
• In May 2024, Temenos introduced its first Responsible Generative AI solutions for core banking, transforming proficiency, operations, and item management inside the banking industry. These secure arrangements coordinated with Temenos Core and Financial Crime Mitigation, improving bank information interaction and efficiency. Temenos is presently leading in Generative AI for banking, empowering quicker and more secure deployment of AI solutions in banks through its AI-infused solutions.

Artificial Intelligence (AI) In Banking Market Scope:

Report Metric	Details
Artificial Intelligence (AI) In Banking Market Size in 2024	US$17.383 billion
Artificial Intelligence (AI) In Banking Market Size in 2029	US$54.635 billion
Growth Rate	CAGR of 55.53%
Study Period	2019 to 2029
Historical Data	2019 to 2022
Base Year	2024
Forecast Period	2024 – 2029
Forecast Unit (Value)	USD Billion
Segmentation	Solution Application Geography
Geographical Segmentation	North America, South America, Europe, Middle East and Africa, Asia Pacific
List of Major Companies in Artificial Intelligence (AI) In Banking Market	Zest AI IBM Data Robot Inc. Personetics Technologies Kensho Technologies, LLC
Customization Scope	Free report customization with purchase

 

The Artificial Intelligence (AI) in the banking market is analyzed into the following segments:

• By Solution
  • Hardware
  • Software
  • Services
• 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
  • Europe
    • 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 

---

1. INTRODUCTION

1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base and Forecast Years Timeline

1.8. Key benefits for the stakeholders

2. RESEARCH METHODOLOGY  

2.1. Research Design

2.2. Research Process

3. EXECUTIVE SUMMARY

3.1. Key Findings

4. MARKET DYNAMICS

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 Power of Buyers

4.3.3. The Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

4.5. Analyst View

5. AI IN BANKING MARKET BY SOLUTION

5.1. Introduction

5.2. Hardware

5.3. Software

5.4. Services

6. AI IN BANKING MARKET BY APPLICATION

6.1. Introduction

6.2. Customer Service

6.3. Robot Advice

6.4. General Purpose/Predictive Analysis

6.5. Cyber Security

6.6. Direct Learning

7. AI IN BANKING MARKET BY GEOGRAPHY

7.1. Introduction

7.2. North America

7.2.1. By Solution

7.2.2. By Application

7.2.3. By Country

7.2.3.1. United States

7.2.3.2. Canada

7.2.3.3. Others

7.3. South America

7.3.1. By Solution

7.3.2. By Application

7.3.3. By Country

7.3.3.1. Brazil

7.3.3.2. Argentina

7.3.3.3. Others

7.4. Europe

7.4.1. By Solution

7.4.2. By Application

7.4.3. By Country

7.4.3.1. Germany

7.4.3.2. France

7.4.3.3. United Kingdom

7.4.3.4. Italy

7.4.3.5. Spain

7.4.3.6. Others

7.5. Middle East and Africa

7.5.1. By Solution

7.5.2. By Application

7.5.3. By Country

7.5.3.1. Saudi Arabia

7.5.3.2. UAE

7.5.3.3. Israel

7.5.3.4. Others

7.6. Asia Pacific

7.6.1. By Solution

7.6.2. By Application

7.6.3. By Country

7.6.3.1. China

7.6.3.2. Japan

7.6.3.3. India

7.6.3.4. Australia

7.6.3.5. South Korea

7.6.3.6. Taiwan

7.6.3.7. Thailand

7.6.3.8. Indonesia

7.6.3.9. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

8.1. Major Players and Strategy Analysis

8.2. Market Share Analysis

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Competitive Dashboard

9. COMPANY PROFILES

9.1. Zest AI

9.2. IBM

9.3. Data Robot Inc.

9.4. Personetics Technologies

9.5. Kensho Technologies, LLC

9.6. Wipro

9.7. Intel

9.8. SAP

9.9. Temenos

9.10. SAS

9.11. Abe AI (Yodlee, Inc.)

9.12. OSP Labs

Zest AI 

IBM 

Data Robot Inc. 

Personetics Technologies 

Kensho Technologies, LLC 

Wipro 

Intel 

SAP 

Temenos 

SAS 

Abe AI (Yodlee, Inc.) 

OSP Labs