The global machine learning in banking market revenue was around US$ 1.99 billion in 2021 and is estimated to reach US$ 21.99 billion by 2031, growing at a compound annual growth rate (CAGR) of 32.2% during the forecast period from 2022 to 2031.
Machine learning (ML) offers, the banking industry has found success with it due to the significant increase in security. Chatbots, which replicate discussions in response to frequently asked inquiries, are the banking of cyber security. Additionally, as required, they can add more access or reset lost passwords.
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Factors Influencing Market Growth
Good risk assessment using machine learning in the banking sector and good customer service drive the market’s expansion.
The rising technological improvements in ML technology are likely to offer lucrative opportunities for market growth.
Greater implementation costs for ML technology may hamper the market growth.
Study of the COVID-19 Pandemic
The COVID-19 pandemic had a positive effect on machine learning in the banking market due to most banks adopting ML technology, which has helped the banking industry since the COVID-19 economic crisis began by making both credit repair and credit monitoring faster and more accurate. In addition, ML is driving advancements that produce better outcomes for customers, from process automation to leveraging biometric identification to decrease credit theft. Additionally, it aids in the more effective and profitable operation of machine learning in the banking industry leaders. Additionally, as working procedures and client banking habits evolved throughout the COVID-19 era, the need for many banks increased. When compared to traditional financial services, the emergence of ML-based financial services has made banking faster, more effective, and less expensive.
Regional Insights
North America dominated the market. This is due to an increased focus on risk management, increased governance, and regulatory requirements for bettering personalized banking and delivering better customer service. The adoption of machine learning in the financial sector to monitor data for unusual transactions in order to detect and prevent fraudulent activities and maintain the security of end users’ accounts is another factor that is accelerating the digitization of financial institutions across the region.
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Leading Competitors
The leading competitors in the global machine learning in banking market are:
Affirm
Amazon Web Services
Big ML
Cisco Systems
FICO
Google LLC
Mindtree
Microsoft
SAP SE
SPD-Group
Others
Segmentation Analysis
The global machine learning in banking market segmentation focuses on Component, Enterprise Size, Application and Region.
Segmentation based on Component
Solution
Service
Segmentation based on Enterprise Size
Large Enterprises
Small and Medium-sized Enterprises (SMEs)
Segmentation based on Application
Credit Scoring
Risk Management Compliance and Security
Payments and Transactions
Customer Service
Others
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Segmentation based on Region
North America
The U.S.
Canada
Mexico
Europe
Western Europe
The UK
Germany
France
Italy
Spain
Rest of Western Europe
Eastern Europe
Poland
Russia
Rest of Europe
Asia Pacific
China
India
Japan
Australia & New Zealand
ASEAN
Rest of Asia Pacific
Middle East & Africa (MEA)
UAE
Saudi Arabia
South Africa
Rest of MEA
South America
Brazil
Argentina
Rest of South America
Reasons to Buy This Report
(A) The research provides valuable insights for top administration, policymakers, professionals, product advancements, sales managers, and stakeholders in the market. It helps them make informed decisions and strategize effectively.
(B) The report offers comprehensive analysis of Machine Learning in Banking market revenues on a global, regional, and country level, projecting trends until 2031. This data allows companies to assess their market share, identify growth opportunities, and explore new markets.
(C) The research includes segmentation of the Machine Learning in Banking market based on types, applications, technologies, and end-uses. This segmentation enables leaders to plan their products and allocate resources based on the expected growth rates of each segment.
(D) Analysis of the Machine Learning in Banking market benefits investors by providing insights into market scope, position, key drivers, challenges, restraints, expansion opportunities, and potential threats. This information helps them make informed investment decisions.
(E) The report offers a detailed analysis of competitors, their key strategies, and market positioning. This knowledge allows businesses to better understand the competition and plan their own strategies accordingly.
(F) The study helps evaluate Machine Learning in Banking business predictions by region, key countries, and top companies, providing valuable information for investment planning and decision-making.
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