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Asia-Pacific Generative AI in BFSI Market Size, Share & Trends Analysis Report By End-User, By Application), By Country and Growth Forecast, 2024 - 2031

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    Report

  • 111 Pages
  • November 2024
  • Region: Asia Pacific
  • Marqual IT Solutions Pvt. Ltd (KBV Research)
  • ID: 6029242
The Asia Pacific Generative AI in BFSI Market is expected to witness market growth of 33.4% CAGR during the forecast period (2024-2031).

The China market dominated the Asia Pacific Generative AI in BFSI Market by Country in 2023, and is expected to continue to be a dominant market till 2031; thereby, achieving a market value of $659.9 Million by 2031. The Japan market is experiencing a CAGR of 32.7% during 2024-2031. Additionally, the India market would register a CAGR of 34.4% during 2024-2031.

Generative AI also significantly enhances BFSI in the realm of operational efficiency. Time-consuming and labor-intensive processes, such as document verification, compliance reporting, and data entry, often burden financial institutions. Generative AI automates these repetitive tasks, reducing human error and accelerating process times. For instance, natural language processing (NLP), a subset of generative AI, can quickly analyze and interpret complex legal documents or financial statements, extracting relevant information and flagging potential compliance issues. This automation reduces the time and cost of these tasks and enhances accuracy and compliance adherence.

For example, AI can expedite claims processing in the insurance sector by assessing the details of a claim against policy terms and identifying any inconsistencies or potential fraud. This process, which could take days or weeks manually, can now be completed in hours, leading to faster payouts and improved customer satisfaction. Operational efficiency is crucial for financial firms seeking to stay competitive, as it allows them to allocate more resources towards innovation and customer service rather than routine administrative tasks.

Australia’s BFSI sector increasingly utilizes generative AI for compliance, fraud detection, and customer experience. Major banks like Commonwealth Bank and Westpac use AI to analyze vast datasets, helping them maintain regulatory compliance and detect fraud patterns in real-time. Australia’s regulatory environment, which mandates strong data protection, has driven financial institutions to adopt AI solutions that improve compliance and protect customer data.

Additionally, with a high demand for online banking and digital financial services, Australian banks are investing in AI-driven customer support solutions to enhance user experience and maintain competitiveness in the digital age. Thus, the collective demand and investments in generative AI underscore the region’s vital role in advancing AI-driven solutions catering to local needs and global standards.

List of Key Companies Profiled

  • Accenture PLC
  • SAS Institute Inc.
  • Google LLC
  • Salesforce, Inc.
  • Microsoft Corporation
  • Adobe, Inc.
  • OpenAI, LLC
  • IBM Corporation
  • NVIDIA Corporation
  • Intel Corporation

Market Report Segmentation

By End-User

  • Banks
  • Insurance Companies
  • Financial Service Providers

By Application

  • Fraud Detection
  • Risk Assessment
  • Customer Experience
  • Algorithmic Trading
  • Other Application

By Country

  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Malaysia
  • Rest of Asia Pacific

Table of Contents

Chapter 1. Market Scope & Methodology
1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Asia Pacific Generative AI in BFSI Market, by End-User
1.4.2 Asia Pacific Generative AI in BFSI Market, by Application
1.4.3 Asia Pacific Generative AI in BFSI Market, by Country
1.5 Methodology for the Research
Chapter 2. Market at a Glance
2.1 Key Highlights
Chapter 3. Market Overview
3.1 Introduction
3.1.1 Overview
3.1.1.1 Market Composition and Scenario
3.2 Key Factors Impacting the Market
3.2.1 Market Drivers
3.2.2 Market Restraints
3.2.3 Market Opportunities
3.2.4 Market Challenges
Chapter 4. Competition Analysis - Global
4.1 Market Share Analysis, 2023
4.2 Strategies Deployed in Generative AI in BFSI Market
4.3 Porter Five Forces Analysis
Chapter 5. Asia Pacific Generative AI in BFSI Market by End-User
5.1 Asia Pacific Banks Market by Country
5.2 Asia Pacific Insurance Companies Market by Country
5.3 Asia Pacific Financial Service Providers Market by Country
Chapter 6. Asia Pacific Generative AI in BFSI Market by Application
6.1 Asia Pacific Fraud Detection Market by Country
6.2 Asia Pacific Risk Assessment Market by Country
6.3 Asia Pacific Customer Experience Market by Country
6.4 Asia Pacific Algorithmic Trading Market by Country
6.5 Asia Pacific Other Application Market by Country
Chapter 7. Asia Pacific Generative AI in BFSI Market by Country
7.1 China Generative AI in BFSI Market
7.1.1 China Generative AI in BFSI Market by End-User
7.1.2 China Generative AI in BFSI Market by Application
7.2 Japan Generative AI in BFSI Market
7.2.1 Japan Generative AI in BFSI Market by End-User
7.2.2 Japan Generative AI in BFSI Market by Application
7.3 India Generative AI in BFSI Market
7.3.1 India Generative AI in BFSI Market by End-User
7.3.2 India Generative AI in BFSI Market by Application
7.4 South Korea Generative AI in BFSI Market
7.4.1 South Korea Generative AI in BFSI Market by End-User
7.4.2 South Korea Generative AI in BFSI Market by Application
7.5 Australia Generative AI in BFSI Market
7.5.1 Australia Generative AI in BFSI Market by End-User
7.5.2 Australia Generative AI in BFSI Market by Application
7.6 Malaysia Generative AI in BFSI Market
7.6.1 Malaysia Generative AI in BFSI Market by End-User
7.6.2 Malaysia Generative AI in BFSI Market by Application
7.7 Rest of Asia Pacific Generative AI in BFSI Market
7.7.1 Rest of Asia Pacific Generative AI in BFSI Market by End-User
7.7.2 Rest of Asia Pacific Generative AI in BFSI Market by Application
Chapter 8. Company Profiles
8.1 Accenture PLC
8.1.1 Company Overview
8.1.2 Financial Analysis
8.1.3 Segmental Analysis
8.1.4 Research & Development Expenses
8.1.5 Recent Strategies and Developments
8.1.5.1 Partnerships, Collaborations, and Agreements
8.1.6 SWOT Analysis
8.2 SAS Institute, Inc.
8.2.1 Company Overview
8.2.2 SWOT Analysis
8.3 Google LLC (Alphabet Inc.)
8.3.1 Company Overview
8.3.2 Financial Analysis
8.3.3 Segmental and Regional Analysis
8.3.4 Research & Development Expense
8.3.5 Recent Strategies and Developments
8.3.5.1 Partnerships, Collaborations, and Agreements
8.3.6 SWOT Analysis
8.4 Salesforce, Inc.
8.4.1 Company Overview
8.4.2 Financial Analysis
8.4.3 Regional Analysis
8.4.4 Research & Development Expenses
8.4.5 SWOT Analysis
8.5 Microsoft Corporation
8.5.1 Company Overview
8.5.2 Financial Analysis
8.5.3 Segmental and Regional Analysis
8.5.4 Research & Development Expenses
8.5.5 Recent Strategies and Developments
8.5.5.1 Product Launches and Product Expansions
8.5.6 SWOT Analysis
8.6 Adobe, Inc.
8.6.1 Company Overview
8.6.2 Financial Analysis
8.6.3 Segmental and Regional Analysis
8.6.4 Research & Development Expense
8.6.5 SWOT Analysis
8.7 OpenAI, L.L.C.
8.7.1 Company Overview
8.7.2 SWOT Analysis
8.8 IBM Corporation
8.8.1 Company Overview
8.8.2 Financial Analysis
8.8.3 Regional & Segmental Analysis
8.8.4 Research & Development Expenses
8.8.5 SWOT Analysis
8.9 NVIDIA Corporation
8.9.1 Company Overview
8.9.2 Financial Analysis
8.9.3 Segmental and Regional Analysis
8.9.4 Research & Development Expenses
8.9.5 SWOT Analysis
8.10. Intel Corporation
8.10.1 Company Overview
8.10.2 Financial Analysis
8.10.3 Segmental and Regional Analysis
8.10.4 Research & Development Expenses
8.10.5 SWOT Analysis

Companies Mentioned

  • Accenture PLC
  • SAS Institute Inc.
  • Google LLC
  • Salesforce, Inc.
  • Microsoft Corporation
  • Adobe, Inc.
  • OpenAI, LLC
  • IBM Corporation
  • NVIDIA Corporation
  • Intel Corporation

Methodology

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