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핀테크용 인공지능(AI) 시장 : 성장, 동향, COVID-19의 영향, 예측(2022-2027년)

AI in Fintech Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

발행일: | 리서치사: Mordor Intelligence Pvt Ltd | 페이지 정보: 영문 | 배송안내 : 2-3일 (영업일 기준)

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

핀테크용 인공지능(AI) 시장은 예측 기간(2022-2027년)에 연평균 복합 성장률(CAGR) 25.3%를 나타낼 것으로 예상됩니다.

목차

제1장 서론

  • 조사의 전제조건과 시장의 정의
  • 조사 대상 범위

제2장 조사 방법

제3장 개요

제4장 시장 인사이트

  • 시장 개요
  • 업계의 매력 - Porter's Five Forces 분석
    • 공급 기업의 교섭력
    • 바이어의 교섭력
    • 신규 진출업체의 위협
    • 대체품의 위협
    • 경쟁 기업간 경쟁 관계
  • 금융 기술에서 AI의 새로운 활용
  • 기술 현황
  • COVID-19가 시장에 미치는 영향

제5장 시장 역학

  • 시장 성장 가속요인
    • 금융기관의 프로세스 자동화 수요 증가
    • 데이터 소스의 이용 가능성 향상
  • 시장 성장 억제요인
    • 숙련 노동자의 필요성

제6장 시장 세분화

  • 유형별
    • 솔루션
    • 서비스
  • 전개 형태별
    • 클라우드
    • 온프레미스
  • 용도별
    • 챗봇
    • Credit Scoring
    • Quantitative & Asset Management
    • 부정 탐지
    • 기타 용도
  • 지역별 정보
    • 북미
    • 유럽
    • 아시아태평양
    • 세계 기타 지역

제7장 경쟁 구도

  • 기업 개요
    • IBM Corporation
    • Intel Corporation
    • ComplyAdvantage.com
    • Narrative Science
    • Amazon Web Services Inc.
    • IPsoft Inc.
    • Next IT Corporation
    • Microsoft Corporation
    • Onfido
    • Ripple Labs Inc.
    • Active.Ai
    • TIBCO Software(Alpine Data Labs)
    • Trifacta Software Inc.
    • Data Minr Inc.
    • Zeitgold
    • Sift Science Inc.
    • Pefin Holdings LLC
    • Betterment Holdings
    • WealthFront Inc.

제8장 투자 분석

제9장 시장의 미래

LSH 22.10.27

The AI in Fintech market is expected to register a CAGR of 25.3% during the forecast period (2022 - 2027). The COVID-19 pandemic outbreak has been accelerating the change in the way how people interact with financial services. Payment- and wealth-focused fintech companies have focused on bolstering their existing infrastructure by investing in new resources or expanding capacity to withstand the stress to their systems from higher transaction volumes. Though it seemed challenging for fintech companies, such actions have provided a significant need for AI solutions as these companies depend on transaction volumes for revenue. Such factors are expected to spearhead the demand for AI solutions in the fintech market.

Key Highlights

  • Financial firms have been the early adopters of the mainframe computer and relational database. They eagerly waited for the next level of computational power. Artificial Intelligence (AI) improves results by applying methods derived from the aspects of human intelligence at a broader scale. The computational arms race for past years has revolutionized fintech companies. Technologies, such as machine learning, AI, neural networks, Big Data Analytics, evolutionary algorithms, and much more, have allowed computers to crunch huge, varied, diverse, and deep datasets than ever before.
  • Moreover, AI and machine learning have benefited banks and fintech as they can process vast amounts of information about customers. This data and information are then compared to obtain results about timely services/products that customers want, which has aided, essentially, in developing customer relations.
  • Additionally, machine learning is being adopted at unprecedented rates, specifically to create propensity models. Banks and insurance companies are introducing machine learning-based solutions for web and mobile applications. This has further enhanced the real-time target marketing by predicting the product propensity of the customers based on behavioral data in real-time.
  • Several market incumbents are establishing a niche by explicitly offering solutions, like AI Chatbots for banking. For instance, in June 2021, Talisma and Active.Ai has partnered to enable improved customer experience in BFSI using conversation AI enabled Chatbot.
  • Moreover, several credit card companies implement predictive analytics into their existing fraud detection workflows to reduce false positives. The studied market further gains traction with several players offering AI-based Anti-money Laundering (AML) and Fraud detection solutions for credit card companies and other financial institutions.
  • For instance, in June 2022, Lucinity, a developer of AI-driven anti-money laundering (AML) software has partnered with fraud management company SEON to include real time fraud prevention capabilities in AML compliance software. SEON's fraud prevention solution will be available through Lucinity's platform, providing customers with compliance risk services from transaction monitoring to real-time fraud detection and prevention.
  • Further, AI-ready infrastructure should be capable of efficient data management, have enough processing power, be agile, flexible, and scalable, and have the capacity to accommodate different volumes of data. Therefore, it would be more challenging for fintech small businesses to assemble the necessary hardware and software elements to support AI. Moreover, as the democratization of AI and deep learning applications expands, not only for tech giants but is now viable for small and medium-sized businesses. The demand for AI professionals to do the work has ballooned as well, and the scarcity of trained resources is the major challenge for AI in fintech.

Key Market Trends

Fraud Detection​ is Expected to Witness Significant Growth

  • Fraudulent activities in the industry have evolved over the decades. Earlier, frauds were limited to cheque frauds and wire frauds. However, with the growth of the cybersphere and the accompanying expansion of the cybercriminal realm, fraud has taken on more virtualized forms.
  • Moreover, fraud prevention and detection represent the most significant area of concern for financial institutions. This segment is likely to become one of the prominent drivers of IT expenditure. Thus, AI capable of avoiding these frauds is expected to experience increased adoption in Fintechs.
  • Further, NetGuardians, a Switzerland-based Fintech company, developed an augmented intelligence solution. It has been made especially for the banks to proactively prevent fraud, empower their clients with ML technology and contextual information, and provide an excellent user experience.
  • According to the US-based Coalition Against Insurance Fraud, fraud accounts for 5-10% of claims costs for American and Canadian insurers. Some insurers expect a total as high as 20% of the claim costs. Across all lines of insurance in North America, the estimated cost is between USD 80 billion and USD 90 billion. AI has emerged as an essential tool in the fight against fraud. AI can help organizations in multiple ways to control fraud in all or any of its variants.
  • Further, McAfee predicted that cybercrime, of which financial fraud is a component, now costs the world around USD 600 billion, equating to 0.8% of the global GDP.AI and ML, combined with the rich data sets available in the financial services sector, which are providing organizations with the means to protect their businesses and defeat criminals.
  • In October 2021, Equicom savings bank partnered with Financial Software and Systems (FSS) to address online fraud. Under this partnership, FSS will provide its Secure3D, an intelligent payment authentication solution, to create a safer and seamless way for consumers to transact in the digital economy.

North America Accounts For the Largest Market Share

  • North America holds a significant share of the global market. Over the forecast period, the region is expected to dominate the global market, owing to its developed infrastructure that can house advanced solutions in the fintech sector. The increasing inflow of investments in startups for AI implementation would further augment the market's growth.
  • In March 2021, HighRadius, a US-based AI start-up that has developed AI-powered fintech software, received a USD 300 million series C funding round. The company will use the new capital to fuel product innovation and expand its global go-to-market reach.
  • Furthermore, slackening of federal regulations, enacting national data breach protections, and drafting model laws at the state level to reduce overlapping red tape could help promote fintech companies in the United States, according to the US Treasury Department.
  • Additionally, lawmakers have increased attention to promoting innovation among non-bank providers of new payments and payment-related technologies and garnered more consumer concern about online data security. These factors are expected to contribute to the growth of the market studied in the region.
  • However, certain factors like aggressive enforcement by security regulators and a fragmented regulatory environment could restrict the growth of certain fintech businesses in the region during the forecast period.

Competitive Landscape

AI in the Fintech market is moving towards fragmented due to many global players. Further various acquisitions and collaborations of large companies are expected to occur shortly, focusing on innovation. Some major players in the market are IBM Corporation, Intel Corporation, Narrative Science, and Microsoft Corporation. Some of the recent developments in the market are:

  • June 2022 - Virgin Money and SurePay have partnered to protect consumers against fraud and misdirected online payments. SurePay's Confirmation of Payee (CoP) service is a real-time name-checking solution that gives UK payers greater assurance that their payments are going to the intended recipient.
  • April 2022 - Cross River and Sardine have partnered to build critical risk and payments infrastructure for Fintech, Web3, and Crypto companies. Sardine will leverage Cross River's payments platform as part of its integrated fraud prevention software for fiat and crypto transactions.
  • February 2021 - Microsoft has launched a cloud for the finance industry housed on an integrated platform that includes Microsoft 365, Azure, Dynamics 365, and Microsoft Power Platform. The public preview of the new service will be available on 31 March 2021, featuring specific capabilities for retail banking and broader industry services.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Suppliers
    • 4.2.2 Bargaining Power of Buyers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitutes
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Emerging Uses of AI in Financial Technology
  • 4.4 Technology Snapshot
  • 4.5 Impact of COVID-19 on the market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Demand For Process Automation Among Financial Organizations
    • 5.1.2 Increasing Availability of Data Sources
  • 5.2 Market Restraints
    • 5.2.1 Need for Skilled Workforce

6 MARKET SEGMENTATION

  • 6.1 By Type
    • 6.1.1 Solutions
    • 6.1.2 Services
  • 6.2 By Deployment
    • 6.2.1 Cloud
    • 6.2.2 On-premise
  • 6.3 By Application
    • 6.3.1 Chatbots
    • 6.3.2 Credit Scoring
    • 6.3.3 Quantitative & Asset Management
    • 6.3.4 Fraud Detection
    • 6.3.5 Other Applications
  • 6.4 By Geography
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia Pacific
    • 6.4.4 Rest of the World

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 IBM Corporation
    • 7.1.2 Intel Corporation
    • 7.1.3 ComplyAdvantage.com
    • 7.1.4 Narrative Science
    • 7.1.5 Amazon Web Services Inc.
    • 7.1.6 IPsoft Inc.
    • 7.1.7 Next IT Corporation
    • 7.1.8 Microsoft Corporation
    • 7.1.9 Onfido
    • 7.1.10 Ripple Labs Inc.
    • 7.1.11 Active.Ai
    • 7.1.12 TIBCO Software (Alpine Data Labs)
    • 7.1.13 Trifacta Software Inc.
    • 7.1.14 Data Minr Inc.
    • 7.1.15 Zeitgold
    • 7.1.16 Sift Science Inc.
    • 7.1.17 Pefin Holdings LLC
    • 7.1.18 Betterment Holdings
    • 7.1.19 WealthFront Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET

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