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, and Forecast (2020 - 2025)

出版商 Mordor Intelligence LLP 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
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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, and Forecast (2020 - 2025)
出版日期: 2020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

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目錄
Product Code: 53906

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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