Artificial Intelligence (AI) in BFSI Market Analysis by Price trends, Top Manufacturers, Applications, Share, Forecast

By Rahul Varpe

AI in BFSI market will be driven by increasing digitization and government initiatives for promoting the use of AI in banking sector. With a large and distributed customer base, banks need to keep innovating in order to best serve their customers. To drive this innovation the financial industry has turned to Artificial Intelligence and machine learning. AI is considered as the future of banking since it brings in the power of advanced data analytics that helps the industry tackle fraudulent transactions and enhance compliance.
With AI algorithms, anti-money laundering activities could be accomplished in a shorter period of time, which otherwise takes a lot of time to develop manually. Artificial intelligence also allows banks to manage vast amount of data at immense speed in order to obtain valuable insights. Some of the features like biometric fraud detection mechanisms, digital payment advisers, and AI bots further lead to the facilitation of high quality services to a broad customer base. All this means reduced costs, increase in profits, and high revenue, fostering industry share further.
AI in BFSI market is bifurcated in terms of component, technology, application, end-use, and regional landscape.
Based on component, the AI in BFSI market is classified into solution, and service. The solution segment is further classified into fraud detection, data analytics & visualization, customer relationship management, customer behavior analytics, and chatbot. Customer behavior analytics segment will witness a CAGR of over 40% over the projected time period since organizations are looking for more advanced ways to understand customer behavior.
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The fraud detection segment witnessed a market share of over 15% in 2019 and is expected to register tremendous growth owing to the increasing incidence of financial frauds among the financial institutes.
The service segment is further categorized into managed service, and professional service. Professional service segment witnessed a market share of over 80% in 2019 due to increasing need for deployment and maintenance as retailers are rapidly embracing AI solutions.
The managed service segment is likely to witness an impressive CAGR of nearly 50% over the forecast time period due to the increasing adoption of intelligent algorithms among the managed service providers.
In terms of application, the overall AI in BFSI market is categorized into risk management, compliance & security; financial advisory; customer service; and back office/operation. The bank office/operation segment will witness a CAGR of over 40% through the analysis period owing to increasing trends of digitalization and automation of banking operation.
From a regional frame of reference, Latin America AI in BFSI market will witness a CAGR of around 43% over the forecast timeframe due to supportive government initiatives for promoting the use of AI in banking services.
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Middle East & Africa AI in BFSI market will witness a CAGR of over 45% over the projected timeframe due to the increasing digitalization in the region.
Table of Contents
Chapter 1. Methodology & Scope
    	
  • 1.1. Methodology	
      		
    • 1.1.1. Initial data exploration
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    • 1.1.2. Statistical model and forecast
      • 		
    • 1.1.3. Industry insights and validation
      • 		
    • 1.1.4. Scope
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    • 1.1.5. Definitions
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    • 1.1.6. Methodology & forecast parameters
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  • 1.2. Data Sources	
      		
    • 1.2.1. Secondary		
        			
      • 1.2.1.1. Paid sources
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      • 1.2.1.2. Public sources
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    • 1.2.2. Primary
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Chapter 2. Executive Summary
    	
  • 2.1. AI IN BFSI industry 360 degree synopsis, 2015 - 2026
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  • 2.2. Business trends
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  • 2.3. Regional trends
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  • 2.4. Component trends	
      		
    • 2.4.1. Solution trends
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    • 2.4.2. Service trends
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  • 2.5. Technology trends
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  • 2.6. Application trends
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  • 2.7. End use trends
Chapter 3. AI in BFSI Industry Insights
    	
  • 3.1. Introduction
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  • 3.2. Industry segmentation
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  • 3.3. Impact of COVID-19 outbreak	
      		
    • 3.3.1. By region		
        			
      • 3.3.1.1. North America
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      • 3.3.1.2. Europe
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      • 3.3.1.3. Asia Pacific
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      • 3.3.1.4. Latin America
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      • 3.3.1.5. Middle East & Africa
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    • 3.3.2. Industry value chain		
        			
      • 3.3.2.1. Suppliers
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      • 3.3.2.2. AI in BFSI technology providers
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      • 3.3.2.3. Marketing & distribution channels
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    • 3.3.3. Competitive landscape		
        			
      • 3.3.3.1. Strategy
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      • 3.3.3.2. Distribution network
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      • 3.3.3.3. Business growth
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  • 3.4. AI in BFSI industry ecosystem analysis
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  • 3.5. Evolution of AI in BFSI technology
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  • 3.6. Investment landscape	
      		
    • 3.6.1. AI investment		
        			
      • 3.6.1.1. Americas
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      • 3.6.1.2. EMEA
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      • 3.6.1.3. Asia Pacific
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  • 3.7. Regulatory landscape	
      		
    • 3.7.1. Health Insurance Portability and Accountability Act (HIPAA)
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    • 3.7.2. Payment Card Industry Data Security Standard (PCI DSS)
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    • 3.7.3. North American Electric Reliability Corp. (NERC) Standards
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    • 3.7.4. Federal Information Security Management Act (FISMA)
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    • 3.7.5. The Gramma-Leach-Bliley Act (GLB) Act of 1999
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    • 3.7.6. Sarbanes-Oxley Act of 2002
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    • 3.7.7. General Data Protection Regulation (GDPR)
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    • 3.7.8. Alternative Investment Fund Managers Directive (AIFMD)
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    • 3.7.9. Anti-Money Laundering Directive 2015/849/EU (AMLD)
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    • 3.7.10. Dodd-Frank Wall Street Reform and Consumer Protection Act
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    • 3.7.11. European Market Infrastructure Regulation (EMIR)
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    • 3.7.12. Foreign Account Tax Compliance Act (FATCA)
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    • 3.7.13. Markets in Financial Instruments Directive (MIFID)
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  • 3.8. Use cases	
      		
    • 3.8.1. Algorithmic trading
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    • 3.8.2. Anti-money laundering (AML) pattern detection
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    • 3.8.3. Customer service
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    • 3.8.4. Fraud detection
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    • 3.8.5. Insurance & loan underwriting
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    • 3.8.6. Portfolio management
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  • 3.9. Industry impact forces	
      		
    • 3.9.1. Growth drivers		
        			
      • 3.9.1.1. Exponentially growing digital data
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      • 3.9.1.2. Rising investment in AI
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      • 3.9.1.3. Increasing partnership between financial institutes and fintech companies
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      • 3.9.1.4. Growing need to provide enhanced customer experience
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    • 3.9.2. Industry pitfalls & challenges		
        			
      • 3.9.2.1. Data safety & security
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      • 3.9.2.2. Black box effect
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  • 3.10. Growth potential analysis
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  • 3.11. Porter's analysis
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  • 3.12. PESTEL analysis
Chapter 4. Competitive Landscape, 2019
    	
  • 4.1. Introduction
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  • 4.2. Company market share, 2019
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  • 4.3. Competitive analysis of major market players, 2019	
      		
    • 4.3.1. Amazon Web Services, Inc.
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    • 4.3.2. Google LLC
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    • 4.3.3. IBM Corporation
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    • 4.3.4. Microsoft Corporation
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    • 4.3.5. Salesforce.com, Inc.
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  • 4.4. Competitive analysis of other prominent players, 2019	
      		
    • 4.4.1. Inbenta Technologies, Inc.
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    • 4.4.2. IPsoft, Inc.
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    • 4.4.3. Next IT Corporation
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    • 4.4.4. Palantir Technologies, Inc.
      •

About Author

Rahul Varpe

Rahul Varpe currently writes for Technology Magazine. A communication Engineering graduate by education, Rahul started his journey in as a freelancer writer along with regular jobs. Rahul has a prior experience in writing as well as marketing of services and products online. Apart from being an avid...

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