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세계의 은행용 인공지능(AI) 시장 : 예측(2022-2027년)

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

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발행일 2022년 05월 상품코드 1087093
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세계의 은행용 인공지능(AI) 시장 : 예측(2022-2027년) Artificial Intelligence (AI) in Banking Market - Forecasts from 2022 to 2027
발행일 : 2022년 05월 페이지 정보 : 영문 125 Pages

본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문목차를 참고해주시기 바랍니다.

세계의 은행용 인공지능(AI) 시장 규모는 2020년 41억 400만 달러에서 2027년에는 358억 8,400만 달러에 이르고, 예측 기간에 연평균 복합 성장률(CAGR) 36.31%의 성장이 예측됩니다. 소매 은행이나 상업은행용으로 AI 기반 회계 소프트웨어 등의 첨단 기술이 이용되면서 편리한 온라인/모바일 뱅킹 서비스 수요가 높아지고 있습니다. 이러한 사용자 친화적인 서비스를 제공하는 동향이 2021-2027년간 시장 성장을 가속할 것으로 예상됩니다.

세계의 은행용 인공지능(AI) 시장에 대해 조사분석했으며, 시장 역학, 부문별 시장 분석, 경쟁 환경, 기업 프로파일 등에 대한 정보를 제공합니다.

목차

제1장 서론

  • 시장의 정의
  • 시장 세분화

제2장 조사 방법

  • 조사 데이터
  • 전제조건

제3장 개요

  • 조사 하이라이트

제4장 시장 역학

  • 시장 성장 촉진요인
  • 시장 성장 억제요인
  • Porter의 산업 분석
    • 공급업체의 교섭력
    • 바이어의 교섭력
    • 대체품의 위협
    • 신규 진출업체의 위협
    • 업계 경쟁 구도
  • 업계 밸류체인 분석

제5장 은행용 인공지능(AI) 시장 : 솔루션별

  • 서론
  • 하드웨어
  • 소프트웨어
  • 서비스

제6장 은행용 인공지능(AI) 시장 : 용도별

  • 서론
  • 고객 서비스 및 고객 참여
  • 로봇 어드바이저
  • 범용/예측 분석
  • 사이버 보안
  • 다이렉트 러닝

제7장 은행용 인공지능(AI) 시장 : 지역별

  • 서론
  • 북미
    • 미국
    • 캐나다
    • 멕시코
  • 남미
    • 브라질
    • 아르헨티나
    • 기타
  • 유럽
    • 독일
    • 프랑스
    • 영국
    • 스페인
    • 기타
  • 중동 및 아프리카
    • 사우디아라비아
    • 아랍에미리트(UAE)
    • 이스라엘
    • 기타
  • 아시아태평양
    • 중국
    • 인도
    • 한국
    • 대만
    • 태국
    • 인도네시아
    • 일본
    • 기타

제8장 경쟁 환경과 분석

  • 주요 기업과 전략 분석
  • 신규 기업과 시장 수익성
  • 인수합병(M&A)/합의/협업
  • 벤더 경쟁 매트릭스

제9장 기업 개요

  • Zest AI
  • IBM
  • Data Robot Inc.
  • Accenture
  • Personetics Technologies
  • Kensho Technologies, LLC
  • Wipro
  • Intel
  • SAP
  • Temenos
  • SAS
  • Abe AI
  • OSP Labs
LSH 22.06.22

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

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

TABLE OF CONTENTS

1. INTRODUCTION

  • 1.1. Market Definition
  • 1.2. Market Segmentation

2. RESEARCH METHODOLOGY

  • 2.1. Research Data
  • 2.2. Assumptions

3. EXECUTIVE SUMMARY

  • 3.1. Research Highlights

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 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. 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/Engagement
  • 6.3. Robo Advice
  • 6.4. General Purpose/Predictive Analysis
  • 6.5. Cybersecurity
  • 6.6. Direct Learning

7. AI IN BANKING MARKET, BY GEOGRAPHY

  • 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. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 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. COMPANY PROFILES

  • 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
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