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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, COVID-19 Impact, and Forecasts (2021 - 2026)

리서치사 Mordor Intelligence Pvt Ltd
발행일 2021년 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, COVID-19 Impact, and Forecasts (2021 - 2026)
발행일 : 2021년 01월 페이지 정보 : 영문

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

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

The Big Data Analytics in Banking market is expected to register a CAGR of 22.97%, during the period of 2021-2026. The major drivers for the adoption of Big Data analytics in the banking sector are the significant growth in the amount of data generated and governmental regulations. As technology is advancing, the number of devices that consumers use to initiate transactions are also proliferating (such as smartphones), making the number of transactions increase. This rapid growth in data requires better acquisition, organization, integration, and analysis.

  • According to Open Banking Implementation Entity (OBIE), API calls increased from one million a month in May 2018, to more than 66.7 million in June 2019. PSD2 mandates that banks create APIs (vehicles for bundling and sharing discrete data sets between organizations) for digital banking transactions.
  • China, banks are driving open banking voluntarily with minimal regulatory mandates. As a result, they are shaping consumers' online experience. Australia's federal government made it mandatory for major banks to provide product information APIs by July 2019, and it will require the banks to make all consumer and transaction data open and available by July 2020.
  • JPMorgan Chase and Co. is the largest bank in the United States and the sixth-largest in the world. Due to a large customer base of over 3 billion, a large volume of credit card information and other transactional data of its customers is created. By adopting Hadoop, they are now able to generate insights on customers' trends, and the same reports are offered to its clients.
  • The market is also witnessing various investments from known investors and government. For instance, The Big Data Europe Project (2015-2017) received funding from the European Union's Horizon 2020 research and innovation program. Its main aim was to develop Big Data application prototypes in industries that can provide large data-sets and have an urgent need to progress toward data-driven solution approaches.
  • With the outbreak of COVID-19, humongous losses in the financial markets of up to USD 744 billion were recorded in March 2020. Investor sentiments at present are at an all-time low, and it is also becoming a difficult task for banks all over the world to continue to maintain good assets and earnings. Due to the shutdowns across various regions and income slowdown, many repayments of loans, especially in Europe, may cease leaving the banks dry, which could slow down the existing banks to incorporate Big Data Analytics in their system.

Key Market Trends

Fraud Detection and Management Account for Significant Market Share

  • Financial organizations around the world lose more than 5% of annual reve­nue to fraud. While direct losses due to fraud amount to a large million dollars, the actual cost is much higher in terms of loss of productivity and loss of customer confidence (and possible attrition). Various losses due to fraud go undetected. With USD 5.7 billion in global money laundering fines issued in 2019, growing threat sophistication, and rising compliance costs, financial institutions need advanced analytics to deter financial crime.
  • Big Data Analytics for fraud prevention is driven by metadata; all records are exhaustively linked based on combinations of attributes within the data. Using statistical techniques, collective entities are identified and collapsed to produce single views of entities within networks. Discrete, bounded networks within the data coulde also be generated, which helps in the representation of statistically relevant groups of activities and relationships.
  • Big Data Analytics is being used extensively to gain relationships among fraudulent activities, including several suspicious activities in a single account or patterns of similar actions across different accounts. Deep analytics looks for similarities that helps in the indication of fraud among transactions or sets of transactions. The relationships are increasingly complex, so they often evade simple monitoring techniques. CyberScout estimates that 85 percent of identity theft goes unnoticed by traditional monitoring tools.
  • According to UK's Financial Ombudsman Service, complaints about banking fraud and scams were the highest in 2018. Approximately 12,000 complaints regarding financial fraud were logged with Ombudsman in 2018-2019, which is an increase of 40% compared to the previous year. According to UK Finance, a trade body, customers lost almost EUR 145 million to push-payment fraud in the first half of 2018. Push-payment was the most common kind of fraud plaguing the European market.
  • In April 2019, a digital bank, N26, headquartered in Germany, reported that EUR 80,000 was stolen from a customer's account. As a result, the German banking regulator, BaFin, ordered the bank to implement fraud management techniques to improve safety measures. The increasing penetration of online services is eventually leading to more fraud management solutions.

Europe to Expected to Witness Significant Growth

  • Considering the regional analysis of government regulations, the government's approach in every region varies in intensity. The European banks are taking more robust regulatory strategies than their Asian counterparts. For instance, in Europe, regulations have been significant catalysts for the rise of open banking. These include Europe's implementation of its Second Payment Services Directive (PSD2) and the UK Competition and Markets Authority's (CMA) Open Banking regulation.
  • Danske Bank is the largest bank in Denmark, with a customer base of more than 5 million. It utilizes its in-house advanced analytics to identify fraud while reducing false positives. Thus, after the implementation of a modern enterprise analytics solution, the bank realized a 60% reduction in false positives, which increased true positives by 50%.
  • German consumers are increasingly using their mobile devices for internet banking. About 40% of them have a banking app on their mobile phones, and one-fifth of them also use their apps for mobile payment services (Eurostat estimates). The trend of "open banking" in European retail banks is causing them to adopt Big Data analytics solutions, which combat issues that traditional financial institutions have faced for decades. Several banks in the region are already using Big Data analytics to deliver compelling use cases.
  • Lloyds Banking Group was the first European bank to implement Pindrop's Phoneprinting technology for detecting fraud. An 'audio fingerprint' of every call was created by analyzing over 1,300 unique call features, such as location, background noise, number history, and call type. In January 2020, The European Banking Authority (EBA) stated some elements of trust, such as data protection, quality, and security. These should be incorporated to support the rollout of advanced analytics.
  • However, according to Commerzbank, Big Data analytics adoption is lagging in some parts of Europe. More substantial infrastructure investments, wider adoption of public cloud, and 5G deployment are the factors required to stay competitive and relevant in global markets. This can be considered as an opportunity and a risk.

Competitive Landscape

Big Data Analytics In Banking Market is quite fragmented due to the presence large number of international players that offer a variety of big data analytics solutions for banks for various applications, which include fraud detection and management, customer analytics, social media analytics, etc. Some of the key players in the market are SAP SE, IBM Corporation, and Oracle Corporation.

  • February 2020 - Oracle Financial Crime and Compliance Management (FCCM) suite of products now include an integrated analytics workbench, 300-plus customer risk indicators, and embedded graph analytics visualizations. These capabilities build on Oracle's strategy to help financial institutions fight money laundering and achieve compliance.
  • February 2020 - The Central Bank of Libya in Tripoli, which includes four of Libya's public sector banks, is upgrading its current FLEXCUBE solution, which is offered by Oracle Corporation. FLEXCUBE engages in helping banks meet customers' evolving expectations for more digital, responsive, and connected experiences. In addition to addressing core-banking needs, the integrated solution will help the banking staff with critical insights and help improve operations.

Reasons to Purchase this report:

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

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions & 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 Force Analysis
    • 4.2.1 Threat of New Entrants
    • 4.2.2 Bargaining Power of Buyers/Consumers
    • 4.2.3 Bargaining Power of Suppliers
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Industry Value Chain Analysis

5 MARKET DYNAMICS

  • 5.1 Introduction to Market Dynamics​
  • 5.2 Market Drivers
    • 5.2.1 Enforcement of Government Initiatives
    • 5.2.2 Increasing Volume of Data Generated by Banks
  • 5.3 Market Challenges
    • 5.3.1 Lack of Data Privacy and Security

6 RELEVANT CASE STUDIES AND USE CASES

7 MARKET SEGMENTATION

  • 7.1 Deployment Type
    • 7.1.1 On-premise
    • 7.1.2 Cloud
  • 7.2 Application
    • 7.2.1 Fraud Detection and Management
    • 7.2.2 Customer Analytics
    • 7.2.3 Social Media Analytics
    • 7.2.4 Other Applications
  • 7.3 Geography
    • 7.3.1 North America
      • 7.3.1.1 United States
      • 7.3.1.2 Canada
    • 7.3.2 Europe
      • 7.3.2.1 United Kingdom
      • 7.3.2.2 France
      • 7.3.2.3 Germany
      • 7.3.2.4 Spain
      • 7.3.2.5 Rest of Europe
    • 7.3.3 Asia Pacific
      • 7.3.3.1 China
      • 7.3.3.2 India
      • 7.3.3.3 Japan
      • 7.3.3.4 Rest of Asia Pacific
    • 7.3.4 Latin America
      • 7.3.4.1 Brazil
      • 7.3.4.2 Argentina
      • 7.3.4.3 Mexico
      • 7.3.4.4 Rest of Latin America
    • 7.3.5 Middle East and Africa
      • 7.3.5.1 United Arab Emirates
      • 7.3.5.2 Saudi Arabia
      • 7.3.5.3 South Africa
      • 7.3.5.4 Rest of Middle East and Africa

8 COMPETITIVE LANDSCAPE

  • 8.1 Company Profiles
    • 8.1.1 Oracle Corporation
    • 8.1.2 SAP SE
    • 8.1.3 IBM Corporation
    • 8.1.4 Alteryx Inc.
    • 8.1.5 Aspire Systems Inc.
    • 8.1.6 Adobe Systems Incorporated
    • 8.1.7 Microstrategy Inc.
    • 8.1.8 Mayato GmbH
    • 8.1.9 Mastercard Inc.
    • 8.1.10 ThetaRay Ltd

9 INVESTMENT ANALYSIS

10 FUTURE OF THE MARKET

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