The global Artificial Intelligence in BFSI market size is expected to reach USD 68.23 Billion at a steady CAGR of 36.5% in 2028, according to latest analysis by Emergen Research. Artificial Intelligence (AI) helps in predicting future trends based on analysis of past behavior of customers, and also helps banks to detect patterns in laundering, identify fraud, and make customer recommendations. These advantages are resulting in increasing deployment of AI in banking operations, which is driving revenue growth of the global Artificial Intelligence in BFSI market.
The report studies the factors influencing the growth of the industry in the global market and offers accurate predictions about the growth pattern. The report pays special attention to the key elements of the market, such as drivers, restraints, opportunities, threats, risks, limitations, and other aspects. The report covers a comprehensive analysis of the competitive landscape with a detailed analysis of the company profiles, product portfolio, and business expansion strategies.
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The report, additionally, offers a comprehensive SWOT analysis and Porter’s Five Forces analysis to offer a better understanding of the competitive landscape of the industry. It also covers strategies adopted by prominent players such as mergers and acquisitions, collaborations, joint ventures, product launches, and brand promotions, among others. The report aims to offer the readers a holistic understanding of the relevant features of the industry.
Key Players Profiled in the Report are:
Amazon Web Services (AWS), Inc., Google LLC, CognitiveScale, Inc., Descartes Labs, Inc., Avaamo, Inc., Intel Corporation, Baidu, Inc., SAP SE, Microsoft Corporation, and Oracle Corporation.
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Some Key Highlights
- In February 2021, FinMkt, which is a loan origination and point of sale financing SaaS provider, announced its partnership with Persistent, which is a global solutions company that delivers enterprise modernization, next-generation product engineering, and digital business acceleration. The partnership will allow small and mid-sized financial institutions to accelerate the digital lending strategies. FinMkt will ramp up its Artificial Intelligence and machine learning capabilities to provide financial institutions with significant insights to shape their credit policies.
- Fraud detection & prevention units in BFSI companies are using Artificial Intelligence to eliminate such activities. Big data is characterized by volume & value and by capturing and processing this in real-time, and Natural Language Processing (NLP) algorithms can detect inconsistencies and discrepancies and ensure fraud prevention.
- Machine learning provides banking companies the opportunity to enhance their business by improving performance and efficiency of BFSI activities. Machine learning attempts to study, identify and classify patterns and make decisions from data collected over time. Machine learning can be used to produce accurate and rapid results, which will drive demand among BFSIs.
Furthermore, the report provides a comprehensive overview of the Artificial Intelligence in BFSI market along with product portfolio and market performance. The report offers key insights into market share, supply chain analysis, demand and supply ratio, import/export details, and product and consumption patterns. To gain a better understanding, the report is further segmented into sections such as product types offered by the market, application spectrum, companies, and key geographical regions where the market has established its presence.
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Market Dynamics:
The report offers insightful information about the market dynamics of the Artificial Intelligence in BFSI market. It offers SWOT analysis, PESTEL analysis, and Porter’s Five Forces analysis to present a better understanding of the Artificial Intelligence in BFSI market, competitive landscape, factors affecting it, and to predict the growth of the industry. It also offers the impact of various market factors along with the effects of the regulatory framework on the growth of the Artificial Intelligence in BFSI market.
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Emergen Research has segmented the global Artificial Intelligence in BFSI market on the basis of offering, solution, technology, application, and region:
Offering Component Outlook (Revenue, USD Million; 2018–2028)
- Software
- Services
- Hardware
Solution Outlook (Revenue, USD Million; 2018–2028)
- Fraud Detection & Prevention
- Customer Relationship Management
- Chabot
- Anti-money Laundering
- Data Analytics & Prediction
- Others
Technology Outlook (Revenue, USD Million; 2018–2028)
- Machine Learning
- Context Aware Processing
- Natural Language Processing (NLP)
- Deep Learning
- Others
Application Outlook (Revenue, USD Million; 2018–2028)
- Risk Management
- Financial Advisory
- Compliance & Security
- Back Office / Operation Customer Service
Analysis of the segments and their growth projection is carried out by extensive historical and current analysis of the market scenario. Further, the report offers details about the factors and features of the Artificial Intelligence in BFSI market expected to boost the growth of the industry in the coming years.
Radical Highlights of the Artificial Intelligence in BFSI Market Report:
- Comprehensive overview of the Artificial Intelligence in BFSI market along with analysis of the changing dynamics of the market
- Growth Assessment of various market segments throughout the forecast period
- Regional and global analysis of the market players, including their market share and global position
- Growth strategies adopted by key market players to combat the impact of the COVID-19 pandemic on the market
- Impact of the technological developments and R&D advancements on the Artificial Intelligence in BFSI market
- Information about profit-making strategies and developmental strategies of major companies and manufacturers
- Insightful information for the new entrants willing to enter the market
- Details and insights about business expansion strategies, product launches, and other collaborations
- The report incorporates advanced analytical tools such as SWOT analysis, Porter’s Five Forces Analysis, feasibility analysis, and investment return analysis
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