According to the report published by Allied Market Research, the global AI and advance machine learning in BFSI market garnered $7.66 billion in 2020, and is expected to generate $61.24 billion by 2030, witnessing a CAGR of 23.1% from 2021 to 2030. The report offers a detailed analysis of changing market trends, top segments, key investment pockets, value chain, regional landscape, and competitive scenario.
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Rise in investments by BFSI companies in AI and machine learning, preferences for personalized financial services, and increase in collaboration activities between financial institutes and AI & machine learning solution companies drive the growth of the global AI and advance machine learning in BFSI market. However, high cost of deployment for AI and advanced machine learning and lack of skilled professionals hinder the market growth. On the other hand, support in government initiatives and rise in investments to leverage the AI technology create new opportunities in the coming years.
AI and Advance Machine Learning in BFSI Market By Component (Solution and Services), Deployment Model (On-premise and Cloud), Enterprise Size (Large Enterprises and SMEs), and Application (Fraud & Risk Management, Customer Segmentation, Sales & Marketing, Digital Assistance and Others): Global Opportunity Analysis and Industry Forecast, 2021-2030
- The adoption of AI and advance machine learning in the BFSI sector increased during the Covid-19 pandemic due to shift toward “work from home” culture and need to carry out difficult tasks in banks and fintech organizations.
- Increase in digitization and transaction delays & need to raise the speed of payment processing led to increased adoption of AI and machine learning technologies.
- Furthermore, nearly 35% of banks reported that machine learning tools and data science technologies made a positive impact in supporting remote working among employees and providing overall security for projects.
- There has been increase in number of inquiries for various banking and fintech services during the pandemic. This accelerated the usage of machine learning-powered tools for management of sudden increase in customer enquiries.
The report offers detailed segmentation of the global AI and advance machine learning in BFSI market based on component, deployment model, enterprise size, application, and region.
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Based on component, the solution segment accounted for the highest share in 2020, contributing to nearly four-fifths of the total share, and is projected to maintain its lead position during the forecast period. However, the service segment is expected to manifest the fastest CAGR of 25.5% from 2021 to 2030.
Based on application, the fraud & risk management segment contributed to the highest market share in 2020, accounting for nearly one-third of the global AI and advance machine learning in BFSI market, and is estimated to continue its leadership status during the forecast period. However, the digital assistance segment is projected to witness the largest CAGR of 24.8% from 2021 to 2030.
Based on region, North America held the largest share in 2020, accounting for more than two-fifths of the total market share, and is expected to maintain its dominance in terms of revenue by 2030. However, Asia-Pacific is projected to manifest the highest CAGR of 24.6% during the forecast period.
List of companies profiled of the global AI and advance machine learning in BFSI market analyzed in the research include Amazon Web Services Inc., Cisco Systems, Inc., BigML, Inc, Hewlett Packard Enterprise Development LP, Fair Isaac Corporation, Microsoft Corporation, International Business Machines Corporation, SAP SE, RapidMiner, Inc., and SAS Institute Inc.
Rise in investments by BFSI companies in AI and machine learning and preferences for personalized financial services drive the growth of the global AI and advance machine learning in BFSI market. The adoption of AI and advance machine learning in the BFSI sector increased during the Covid-19 pandemic due to shift toward “work from home” culture and need to carry out difficult tasks in banks and fintech organizations.
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