NEWS: 公告在東京證券交易所JASDAQ標準市場新上市

市場調查報告書

銀行的巨量資料、分析的全球市場 - 各類型、用途、地區 - 成長,趨勢,及預測(2018年∼2023年)

Big Data Analytics In Banking Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

出版商 Mordor Intelligence Pvt Ltd 商品編碼 546568
出版日期 內容資訊 英文 120 Pages
商品交期: 2-3個工作天內
價格
銀行的巨量資料、分析的全球市場 - 各類型、用途、地區 - 成長,趨勢,及預測(2018年∼2023年) Big Data Analytics In Banking Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)
出版日期: 2021年01月01日內容資訊: 英文 120 Pages
簡介

全球銀行的巨量資料、分析市場2017年是71億9,000萬美金。今後預計以12.97%的年複合成長率擴大,2023年達到148億3,000萬美元。

本報告提供銀行的巨量資料、分析的全球市場調查,提供市場概要,各類型、用途、地區的市場趨勢,市場規模的變化與預測,市場促進、阻礙因素以及市場機會分析,競爭情形,主要企業的簡介等全面性資訊。

目錄

第1章 簡介

  • 調查成果
  • 市場定義
  • 調查的前提條件

第2章 調查方法

第3章 摘要整理

第4章 市場分析

  • 市場概況
  • 價值鏈分析
  • 產業的魅力 - 波特的五力分析
    • 新加入廠商的威脅
    • 供應商談判力
    • 消費者談判力
    • 替代產品的威脅
    • 產業內的競爭
  • 產業政策

第5章 市場動態

  • 市場成長要素
  • 市場阻礙因素

第6章 技術概要

第7章 市場區隔

  • 各部署模式
    • 內部部署
    • 雲端
  • 各用途
    • 詐欺檢測與管理
    • 營運情報
    • 客戶分析
    • 社群媒體分析
    • 回饋管理
    • 其他
  • 各地區
    • 北美
    • 歐洲
    • 亞太地區
    • 中南美
    • 中東、非洲

第8章 市場佔有率分析

第9章 企業簡介

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

第10章 投資分析

第11章 市場未來展望

本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

目錄
Product Code: 53906

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