은행용 챗봇 시장 : 제품 유형, 용도, 유통 채널, 지역별(2024-2031년)

Chatbot for Banking Market Valuation - 2024-2031

The banking market is undergoing rapid transformation driven by digitalization, regulatory changes and evolving customer expectations. Fintech, mobile banking and blockchain technology are emerging as key developments that improve security and efficiency. Traditional banks face competition from digital-only institutions that provide greater user experiences. Regulatory compliance and cybersecurity remain key issues. This is likely to enable the market size to surpass USD 3.37 Billion in 2024 to reach a valuation of around USD 31.5 Billion by 2031.

Personalization and customer-centric services are increasingly important for client retention. The market is also seeing a surge in mergers and acquisitions as banks seek to scale and innovate. Overall, the banking industry is shifting toward nimbler, technology-driven models to satisfy the needs of today's consumers. The rising demand for Chatbot for Banking is enabling the market to grow at a CAGR of 37.62% from 2024 to 2031.

Chatbot for Banking Market: Definition/ Overview

A Chatbot for Banking is an AI-powered virtual assistant that provides consumers with banking services such as account inquiries, transaction processing and financial advising using conversational interfaces, thereby improving user experience and operational efficiency in the banking sector.

Banking chatbots improve overall customer experience and operational efficiency by streamlining customer service, offering 24/7 assistance, handling transactions, providing account information, facilitating bill payments, assisting with loan applications, enhancing fraud detection and providing personalized financial advice.

Chatbots in banking can improve customer service, expedite operations, provide tailored financial advice, detect fraud, facilitate transactions, support 24/7 availability, reduce operational costs and provide a seamless interactive experience for customers.

Will Adoption of AI and Natural Language Processing (NLP) to Boost the Chatbot for Banking Market Growth?

The use of AI and Natural Language Processing (NLP) is expected to considerably increase the chatbot for banking industry growth. AI improves chatbot performance by allowing for more accurate responses and efficient processing of complicated consumer requests. NLP enables chatbots to better understand and interpret human language, resulting in improved consumer interactions and happiness.

The combination of AI and NLP allows for more personalized banking experiences, faster resolution times and 24/7 availability, all of which are critical for modern banking consumers. These technologies also support additional functionality such as fraud detection, financial advice and transaction assistance, which further encourages their use. Additionally, AI and NLP assist banks in lowering operating expenses by automating mundane processes and freeing up human personnel for more strategic functions. Thus, adoption of these technologies is a crucial factor for the growth of chatbots in the banking sector.

Will Limited Understanding and Capabilities Hamper the Chatbot for Banking Market?

Limited comprehension and capabilities may impede the chatbot for the banking business. While chatbots have many benefits, their usefulness is limited by their ability to correctly read and respond to client requests. If chatbots fail to understand difficult or nuanced queries, customers may become frustrated and lose trust in the technology.

Additionally, present limits in AI and NLP may result in insufficient handling of many languages, slang and idiomatic expressions, limiting their utility. Inconsistent or erroneous responses may cause customers to seek human assistance, negating the advantages of automation.

Furthermore, security concerns and an inability to adequately manage sensitive information can discourage adoption. To reach the full potential of chatbots in banking, ongoing developments in AI, NLP and security standards are required to overcome these limitations and enhance their capabilities.

Category-Wise Acumens

Will Increasing Advanced Capabilities Over Rule-Based Chatbots Drive the Type Segment?

The growing advanced capabilities of AI-powered chatbots over rule-based chatbots will drive the type segment in the chatbot for banking market. AI-powered chatbots outperform traditional chatbots by using machine learning and natural language processing (NLP) to answer complicated inquiries, provide personalized responses and improve over time via engagement.

These advanced features improve the client experience, operational efficiency and service quality, making AI-powered chatbots more desirable than rule-based systems. Rule-based chatbots, which are limited to predefined scripts and responses, struggle to handle complex and dynamic consumer interactions. As banks attempt to improve customer service and streamline operations, AI-powered chatbots' improved capabilities are projected to boost adoption and market supremacy.

Will Increasing Prevalence of Smartphones Drive the Application Segment?

The growing popularity of smartphones will propel the application segment in the chatbot for banking industry. As more people rely on smartphones for daily tasks, including banking, the need for mobile-based services increases. Chatbots embedded into mobile banking apps enable rapid, 24/7 client service for a variety of functions including questions, transactions and tailored financial advice.

Mobile applications are the most convenient and accessible platform for banking chatbots. Furthermore, mobile chatbots improve the user experience with features such as push notifications and real-time updates. The extensive usage of smartphones, as well as the demand for efficient, on-the-go banking solutions, will fuel chatbot application acceptance and growth in the mobile banking category.

Country/Region-wise Acumens

Will High Customer Demand for Efficient Banking Solutions Drive the Market in North America?

High customer need for efficient banking solutions would propel the chatbot for banking market in North America. Consumers increasingly want speedy, personalized and 24-hour access to banking services, which chatbots can efficiently provide. The drive for better customer service and more frictionless banking experiences is driving banks to implement AI-powered chatbots.

Furthermore, the region's advanced technological infrastructure, high internet penetration and widespread usage of smartphones all help to drive chatbot adoption. Regulatory support for digital banking advances, as well as significant expenditures in artificial intelligence and natural language processing, help to drive market growth. As customers seek simplicity and efficiency, chatbot use in North American banks is projected to accelerate.

Will Rising Investments in AI And Natural Language Processing Technologies Drive the Market in Asia Pacific Region?

Rising investments in AI and natural language processing (NLP) technology will fuel the Asia-Pacific banking chatbot market. Governments and business sectors in China, India and Japan are boosting their investments in AI and NLP to increase technological skills and digital banking services. These developments allow chatbots to provide more accurate, efficient and tailored consumer interactions, satisfying the growing demand for simple and easily accessible financial solutions.

The region's fast expanding digital economy, increasing smartphone penetration and tech-savvy populace all contribute to this rise. As banks strive to streamline processes, decrease expenses and improve customer happiness, the usage of AI-powered chatbots is likely to grow, greatly driving the market in Asia-Pacific.

Competitive Landscape

The chatbot for banking market is a dynamic and competitive space, characterized by a diverse range of players vying for market share. These players are on the run for solidifying their presence through the adoption of strategic plans such as collaborations, mergers, acquisitions and political support. The organizations are focusing on innovating their product line to serve the vast population in diverse regions.

Some of the prominent players operating in the chatbot for banking market include:

Amazon (Lex)

Google (Dialogflow)

Microsoft (Azure Bot Service)

IBM (Watson Assistant)

LivePerson

Nuance Communications

eGain Corporation

Kasisto

Inbenta

Chatbot for Banking Market, By Category

• Type:
• Rule-based Chatbots
• AI-powered Chatbots
• Application:
• Website
• Contact Centers
• Social Media
• Mobile Application
• Deployment Mode:
• On-Premise
• Cloud
• Region:
• North America
• Europe
• Asia-Pacific
• South America
• Middle East & Africa

TABLE OF CONTENTS

1. Introduction

• Market Definition
• Market Segmentation
• Research Methodology

2. Executive Summary

• Key Findings
• Market Overview
• Market Highlights

3. Market Overview

• Market Size and Growth Potential
• Market Trends
• Market Drivers
• Market Restraints
• Market Opportunities
• Porter's Five Forces Analysis

4. Chatbot For Banking Market, By Type of Chatbot

• Rule-based Chatbots
• AI-powered Chatbots

5. Chatbot For Banking Market, By Deployment Mode

• On-Premises Chatbots
• Cloud-based Chatbots

6. Chatbot For Banking Market, By Functionality

• Customer Service Chatbots
• Sales and Marketing Chatbots
• Transactional Chatbots

7. Regional Analysis

• North America
• United States
• Canada
• Mexico
• Europe
• United Kingdom
• Germany
• France
• Italy
• Asia-Pacific
• China
• Japan
• India
• Australia
• Latin America
• Brazil
• Argentina
• Chile
• Middle East and Africa
• South Africa
• Saudi Arabia
• UAE

8. Market Dynamics

• Market Drivers
• Market Restraints
• Market Opportunities
• Impact of COVID-19 on the Market

9. Competitive Landscape

• Key Players
• Market Share Analysis

10. Company Profiles

• Amazon (Lex)
• Google (Dialogflow)
• Microsoft (Azure Bot Service)
• IBM (Watson Assistant)
• LivePerson
• Nuance Communications
• eGain Corporation
• Kasisto
• Inbenta

11. Market Outlook and Opportunities

• Emerging Technologies
• Future Market Trends
• Investment Opportunities

12. Appendix

• List of Abbreviations
• Sources and References