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+29% CAGR Growth to Be Archived by Global Artificial Intelligence in BFSI Market Market According to New Research, Key Players - Microsoft, Google, Intel and Others

Advance guide on Artificial Intelligence in BFSI market with +29% CAGR, purchasing criteria and difficulties, business sector structure, and the business pattern, top key vendors - Microsoft, Google, Intel, IBM, AWS, Salesforce, Oracle, SAP, Palantir, IPsoft ans others


Houston, TX -- (SBWIRE) -- 09/12/2018 -- Artificial Intelligence in BFSI market is driven by a rapid surge in the digital data. The digital data is anticipated to grow at an annual growth rate of about 40% over the next 10 years. The increasing adoption of IoT devices and advanced technologies, such as Big Data, are the major factors responsible for the surge in the data. Furthermore, the growing adoption of smart devices and internet services across the globe also fuels the growth of the data.

The risk management, compliance, and security applications of the AI in BFSI market are anticipated to grow during forecast period. The market is driven by the improved demand for advanced machine learning algorithms in Anti-Money Laundering (AML) and fraud detection applications. Unlike the traditional security systems, the AI-powered system can actively learn and calibrate as per the new potential security threats. This solution can detect unique security issues & vulnerabilities and flag the security teams.

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Key Players:
Microsoft, Google, Intel, IBM, AWS, Salesforce, Oracle, SAP, Palantir, IPsoft, Inbenta, Lexalytics, Interaction, and Next IT

The report takes a close and analytical look at the various companies that strive for a higher share of the global Artificial Intelligence in BFSI market. For this purpose, a section dedicated to company profiles has been included in the report. The profiling of the leading players is done in order to judge the current competitive scenario. The competitive landscape is assessed by taking into consideration many important factors such as business growth, recent developments, product pipeline, and others.

The research report further makes use of graphical representations such as tables, info graphics, and charts to forecast figures and historical data of the global Artificial Intelligence in BFSI market. The report is presented in a clear and concise manner so that readers can understand the market structure and dynamics effectively.

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Key segmentations and sub segmentations are elaborated with right information for Artificial Intelligence in BFSI market. The popularity and anticipated opportunities for all the segmentations are well explained in order to give a clear idea about the market trends. On the basis of estimations of the key segmentations, Artificial Intelligence in BFSI market has been elucidated. Technological developments which have taken place during the ongoing market situation and the predicted scenario during the forecast period are presented systematically.

The future potentials of this market are analyzed and the prevalent trends are been looked upon with utmost detailing. The growth in all the major geographic regions are been studied in detail in this particular report. With exact data on the top most regions for this market and the reason they hold that position is studied and elaborated.

Moving forward, a competitive landscape of Artificial Intelligence in BFSI market is presented for understanding the market importance. This section gives the prime strategies, weaknesses and strengths and the major contributed of the growth have been highlighted. Gross, revenues, product overview, contact information and many such points are explained for Artificial Intelligence in BFSI market in depth.

Table of Contents:

Global Artificial Intelligence in BFSI Market Research Report 2018-2023

Chapter 1 Artificial Intelligence in BFSI Market Overview
Chapter 2 Global Economic Impact
Chapter 3 Competition by Manufacturers
Chapter 4 Production, Revenue (Value) by Region (2018-2023)
Chapter 5 Supply (Production), Consumption, Export, Import by Regions (2018-2023)
Chapter 6 Production, Revenue (Value), Price Trend by Type
Chapter 7 Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Market Forecast (2018-2023)
Chapter 13 Appendix

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