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세계의 은행용 빅데이터 분석 시장 : 종류별, 용도별, 지역별 - 성장, 동향, 예측(2018-2023년)

Big Data Analytics In Banking Market - Growth, Trends, and Forecast (2020 - 2025)

리서치사 Mordor Intelligence LLP
발행일 2020년 01월 상품 코드 546568
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세계의 은행용 빅데이터 분석 시장 : 종류별, 용도별, 지역별 - 성장, 동향, 예측(2018-2023년) Big Data Analytics In Banking Market - Growth, Trends, and Forecast (2020 - 2025)
발행일 : 2020년 01월 페이지 정보 : 영문

세계의 은행용 빅데이터 분석 시장은 2017년에 71억 9,000만 달러를 기록했습니다. 향후 12.97%의 연평균 성장률(CAGR)로 성장하여 2023년에는 148억 3,000만 달러에 달할 것으로 예측됩니다.

세계의 은행용 빅데이터 분석(Big Data Analytics) 시장에 대해 조사했으며, 시장 개요, 종류, 용도, 지역별 시장 동향, 시장 규모 추정과 예측, 시장 성장 촉진·저해요인 및 시장 기회, 경쟁 상황, 주요 기업 개요 등의 정보를 전해드립니다.

목차

제1장 서론

  • 조사 성과
  • 시장 정의
  • 조사 가정

제2장 조사 방법

제3장 주요 요약

제4장 시장 분석

  • 시장 개요
  • 밸류체인 분석
  • 업계의 매력 : Porter's Five Forces 분석
    • 신규 참여업체의 위협
    • 공급업체의 협상력
    • 소비자의 협상력
    • 대체 제품의 위협
    • 업계내에서의 경쟁
  • 산업 정책

제5장 시장 역학

  • 시장 성장 촉진요인
  • 시장 성장 저해요인

제6장 기술 개요

제7장 시장 세분화

  • 전개 모델별
    • 온프레미스
    • 클라우드
  • 용도별
    • 부정 검출과 관리
    • 오퍼레이션 인텔리전스
    • 고객 분석
    • 소셜 미디어 분석
    • 피드백 관리
    • 기타
  • 지역별
    • 북미
    • 유럽
    • 아시아태평양
    • 중남미
    • 중동 및 아프리카

제8장 시장 점유율 분석

제9장 기업 개요

  • SAP SE
  • Oracle Corporation
  • IBM Corporation
  • Alteryx, Inc
  • Aspire systems
  • ZestFinance
  • Adobe Systems Incorporated
  • Microstrategy, Inc.
  • Hexanika
  • PeerIQ

제10장 투자 분석

제11장 시장의 향후 전망

KSM 18.08.21

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Market Overview

The big data analytics market was valued at USD 29.87 billion in 2019 and is expected to reach USD 62.10 billion by 2025, at a CAGR of 12.97% over the forecast period 2020 - 2025.

  • Data is driving the modern financial industry in many ways. Financials institutions are making use of Big Data in various ways, ranging from boosting cybersecurity to cultivating customer loyalty, reducing customer churn and more, through personalized and innovative offerings that shape modern banking into an individualized experience.
  • As financial services companies embark on a journey to gain better understanding of customers and their household preferences, in order to provide effective and differentiated services, the amount of data is expected to grow, data collection will occur more frequently, and data variety is estimated to become more complex.
  • Various sources of data in the industry include traditional enterprise data from operational systems related to customer touch points (such as ATMs, credit cards mortgage units, volatility measures etc.), financials business forecast from various sources (regulatory data, trading data etc.) and other sources (advertising and social media).
  • Big data analytics services and platforms allow thousands of customers (Banking) to use similar resources, aiding BFSI companies to reduce their expenses and provide valuable insights from the continuously evolving data, thus driving their adoption in the industry.
  • For instance, the RBI, the regulatory banking authority in India, announced its foray into the world of big data analytics by opening a data sciences laboratory that would employ professionals with skills in computer science, data analytics, statistics, economics, econometrics, and finance.
  • Another prominent trend in the market that aids the growth of big data analytics in the banking sector includes increasing deployment of Internet of Things (IoT) devices in the banking sector, such as banking on wearables.
  • For instance, Bank of America provides applications for popular wearable's, such as Apple Watch and FitPay. Also, Bank of China has increased its investments (USD 70.2 billion) in the IOT and blockchain technologies, and these investments account for more than 1% of the bank's operating annual income, in 2017.

Scope of the Report

Big data gives insight into many complex areas of individual's life including their lifestyle, needs, and preferences of their customers so that it is easy for banks to personalize services to the needs of each individual.

Key Market Trends

Social Media Analytics Account for Significant Market Share

  • Social Media Analytics in banking is a broad term used for referring to the approach of mining data from social media sources and bank website, using Big Data technologies for processing and presenting the statistics-based insights to businesses, in order to aid their decision-making processes.
  • Social Media Analytics enables banking and financial institutions to classify customer interactions. These identified interactions could then be used to determine key insights into their sentiments and opinions, which is vital for addressing their core concerns.
  • Analyzing key patterns in the social sphere could help banking and financial institutions gain a competitive advantage over other firms, as in recent years it has become important to have an effective social media strategy.
  • Social media analytics and insights require robust data and text processing tools in addition to highly-customizable visualization means, for enabling the banking industry to find key linkages between mined information, further driving the demand.
  • For instance, Nedbank Ltd, a prominent bank in South Africa, uses social media to organize Nedbank's marketing campaign, analyze customer preferences, and complaints.
  • However, the BFSI industry has faced concerns on whether social media analytics/insights could be used as the reliable sample size to reflect the sentiment of the entire customer base, as it is prone to bias, as customers active on social media may demonstrate completely different behavior from normal customers.

North America Account for Significant Market Share

  • In the digital age, the financial crime against banks and other financial services institutions are accelerating rapidly. Through 2020, globally, card fraud is expected to increase to USD 183.29 billion.
  • The BFSI sector in the United States has more than 1 exabyte of stored data. This data generates from various sources, such as - credit/debit card histories, customer bank visits, banking volumes, call logs, account transactions, and web interactions.
  • According to the FBI Internet Crime report, more than 214,217,302 complaints related to data loss has been issued. To overcome these losses, banks and capital market firms are using big data analytics to cope with data breaches and fraud.
  • Accessibility to adequate infrastructure, the presence of numerous global financial institutions, increased adoption of IoT devices and internet users are expected to shape big data analytics in the banking market, in the North American region.
  • Additionally, the proliferation of digital services and technological advancements, coupled with early adoption of the latest technologies in banking sectors, is supplementing the growth of the region.
  • Further, in the forecast period, most of the financial institutions in the region are expected to show interest to buy assets in core/non-core markets, set up partnerships in new strategic markets, the need for big data analytics is expected to increase substantially, thus driving the market.

Competitive Landscape

Big Data Analytics In Banking Market is Highly Fragmented due to a large number of companies in the market. Some key players in the market are SAP SE, IBM Corporation, Microsoft Corporation. Some key recent developments in the market are:

  • IBM added Cloud Private data to its product portfolio, which is designed to help organizations to utilize data science and machine learning techniques, to generate valuable insights from data.
  • IBM acquired Armanta, an analytics software company. This acquisition will help the company enhance its management decision-making and better address regulatory compliance, allowing firms to re-allocate saved capital to innovation initiatives.
  • IBM, Packet Clearinghouse (PCH), and The Global Cyber Alliance (GCA) launched a free service, designed to give consumers and businesses added privacy and security protection, as they access the internet. The new Quad 9 Domain Name System (DNS) service helps protect users from accessing millions of malicious internet sites known to steal personal information, infect users with ransomware and malware, or conduct fraudulent activity.

Reasons to Purchase this report:

  • The market estimate (ME) sheet in Excel format
  • Report customization as per the client's requirements
  • 3 months of analyst support

Table of Contents

1 INTRODUCTION

  • 1.1 Study Deliverables
  • 1.2 Study Assumptions
  • 1.3 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

  • 4.1 Market Overview
  • 4.2 Introduction to Market Drivers and Restraints
  • 4.3 Market Drivers
    • 4.3.1 Rising Need To Expand Wealth Management Portfolio To Ensure Consistent Fee-Based Revenue And Mitigate Risk
    • 4.3.2 Convergence Of Internet Of Things (IoT) And Big Data In Banking
  • 4.4 Market Restraints
    • 4.4.1 Lack Of General Awareness And Expertise In Emerging Regions
    • 4.4.2 Non-Uniformity Of Data
  • 4.5 Value Chain / Supply Chain Analysis
  • 4.6 Industry Attractiveness - Porter's Five Force Analysis
    • 4.6.1 Threat of New Entrants
    • 4.6.2 Bargaining Power of Buyers/Consumers
    • 4.6.3 Bargaining Power of Suppliers
    • 4.6.4 Threat of Substitute Products
    • 4.6.5 Intensity of Competitive Rivalry

5 MARKET SEGMENTATION

  • 5.1 By Type of Deployment
    • 5.1.1 On-Premise
    • 5.1.2 Cloud
  • 5.2 By Application
    • 5.2.1 Fraud Detection and Management
    • 5.2.2 Operation Intelligence
    • 5.2.3 Customer Analytics
    • 5.2.4 Social Media Analytics
    • 5.2.5 Feedback Management
    • 5.2.6 Other Applications
  • 5.3 Geography
    • 5.3.1 North America
    • 5.3.2 Europe
    • 5.3.3 Asia Pacific
    • 5.3.4 South America
    • 5.3.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Company Profiles
    • 6.1.1 SAP SE
    • 6.1.2 Oracle Corporation
    • 6.1.3 IBM Corporation
    • 6.1.4 Alteryx, Inc
    • 6.1.5 Aspire Systems, Inc.
    • 6.1.6 ZestFinance, Inc.
    • 6.1.7 dobe Systems Incorporated
    • 6.1.8 Microstrategy, Inc.
    • 6.1.9 Hexanika, Inc.
    • 6.1.10 PeerIQ, Inc.

7 INVESTMENT ANALYSIS

8 MARKET OPPORTUNITIES AND FUTURE TRENDS

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