Allied Market Research recently published a report, titled, “Machine Learning in Banking Market By Component (Solution and Service), Enterprise Size (Large Enterprises, Small and Medium-Sized Enterprises [SMEs]), Application (Credit Scoring, Risk Management Compliance and Security, Payments and Transactions, Customer Service, and Others): Global Opportunity Analysis and Industry Forecast, 2021-2031″. As per the report, the global machine learning in banking industry accounted for $1.33 billion in 2021, and is expected to reach $21.27 billion by 2031, growing at a CAGR of 32.2% from 2021 to 2030.
Download Sample Report @ https://www.alliedmarketresearch.com/request-sample/17643
Major determinants of the market growth
Improved productivity of banks and faster banking operations using machine learning have boosted the growth of the global machine learning in banking market. However, higher cost of implementation of machine learning technology and risk of unemployment with adoption of machine learning technology hinder the market growth. On the contrary, technological developments in machine learning would open new opportunities in the future.
Report Coverage and Details:
| Report Coverage | Details |
| Forecast Period | 2022–2031 |
| Base Year | 2021 |
| Market Size in 2021 | $1.33 billion |
| Market Size in 2031 | $21.27 billion |
| CAGR | 32.2 % |
| No. of Pages in Report | 270 |
| Segments covered | Component, Enterprise Size, Application, and Region |
| Drivers | Improved productivity of banks owing to adoption of ML |
| Effective risk assessment through machine learning in financial industry and efficient customer service | |
| Opportunities | Technological advancements in ML technology |
| Restrains | High costs of implementation of ML technology |
| Risk of unemployment owing to adoption of ML |
COVID-19 Scenario:
- The COVID-19 pandemic had a positive impact on the market as most of the banks adopted technologies such as machine learning. These technologies helped the financial institutes by making credit repair and credit monitoring more accurate and faster.
- Machine learning have helped banking sector leaders to deliver better results for customers and reduce credit frauds from process automation to biometric identification.
- During the pandemic, several banks experienced surge in demand as working practices and customer banking habits changed drastically.
The solution segment dominated the market growth
By component, the solution segment held the largest share in 2021, accounting for nearly three-fourths of the global machine learning in banking market. Moreover, the segment is expected to continue its dominance in terms revenue throughout the forecast period. However, the service segment is expected to manifest the highest CAGR of 36.5% during the forecast period, due to Surge in demand for cloud-based machine learning services. Moreover, rise in demand for software-as-a-service (SaaS) due to its numerous benefits such as scalability and one-time customer acquisition cost is expected to provide lucrative opportunities for the growth of the market.
The small and medium-sized enterprises (SMEs) segment to manifest the highest CAGR through 2031
By enterprise size, the small and medium-sized enterprises (SMEs) segment is projected to register the highest CAGR of 35.6% during the forecast period, due to their less risk-taking capabilities. In addition, the segment is expected to hold the largest share by 2031. However, the large enterprises segment held the largest share in 2021, contributing to more than one-fourth of the global machine learning in banking market.
The credit scoring segment dominated the market
By application, the credit scoring segment held the largest share in 2021, accounting for more than one-fourth of the global machine learning in banking market. Furthermore, the segment is expected to maintain its dominance the largest share during the forecast period. However, the payments and transactions segment is anticipated to register the highest CAGR of 35.4% during the forecast period. Payment providers are well familiar with machine learning, as it pertains to credit card transaction monitoring, where learning algorithms play important roles in near real-time authorization of transactions. These are the major growth factors for the payments and transactions in the machine learning in banking market.
North America held the largest share
By region, the global machine learning in banking market across North America held the largest share in 2021, accounting for nearly two-fifths of the market. However, market across Asia-Pacific is expected to showcase the highest CAGR of 35.5% during the forecast period, owing to surge in need to monitor growing number of financial violations and offences. Moreover, the region is projected to hold the largest share throughout the forecast period.
Interested to Procure the Data? Inquire Here @ https://www.alliedmarketresearch.com/purchase-enquiry/17643
Major Market Players
- Affirm, Inc.
- Amazon Web Services, Inc.
- BigML, Inc.
- Cisco Systems, Inc.
- FICO
- Google LLC
- Mindtree Ltd.
- Microsoft Corporation
- SAP SE
- SPD-Group
The report analyzes these key players of the global machine learning in banking market. These players have adopted various strategies such as expansion, new product launches, partnerships, and others to increase their market penetration and strengthen their position in the industry. The report is helpful in determining the business performance, operating segments, product portfolio, and developments by every market player.
Key Benefits For Stakeholders:
- This report provides a quantitative analysis of market segments, current trends, estimations, and dynamics of the machine learning in banking market share from 2021 to 2031 to identify the prevailing market opportunities.
- In-depth analysis of the machine learning in banking market forecast segmentation assists to determine the prevailing machine learning in banking market opportunity.
- Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
- The report includes the analysis of the regional as well as global machine learning in banking market trends, key players, market segments, application areas, and market growth strategies.
Machine Learning in Banking Market Key Segments:
By Component:
- Solution
- Service
By Enterprise Size:
- Large Enterprises
- Small and Medium-Sized Enterprises (SMEs)
By Application:
- Credit Scoring
- Risk Management Compliance and Security
- Payments and Transactions
- Customer Service
- Others
By Region:
- North America (U.S., Canada, and Mexico)
- Europe (Germany, Italy, France, Spain, U.K., Russia, and Rest of Europe)
- Asia-Pacific (China, India, Japan, South Korea, and Rest of Asia-Pacific)
- LAMEA (Latin America, Middle East and Africa)
“We have also published few syndicated market studies in the similar area that might be of your interest. Below are the report titles for your reference, considering Impact of COVID-19 over this market which will help you to assess aftereffects of pandemic on short-term and long-term growth trends of this market”.
Trending Reports in Semiconductor and Electronics Industry (Book Now with 10% Discount + COVID-19 Scenario):
AI in Banking Market By Component (Solution and Service), Enterprise Size (Large Enterprise and SMEs), Applications (Risk Management Compliance & Security, Customer Service, Back Office/Operations, Financial Advisory, and Others) and Technology (Machine Learning & Deep Learning, Natural Language Processing (NLP), Computer Vision, and Others): Global Opportunity Analysis and Industry Forecast, 2021-2030
Data Analytics in Banking Market By Component (Solution and Services), Deployment Model (On-Premise and Cloud), Organization Size (Large Enterprises, and Small & Medium Sized Enterprises), Type (Predictive Analytics, Prescriptive Analytics, Descriptive Analytics, and Others), Application (Fraud Detection & Prevention, Customer Management, Sales & Marketing, Workforce Management, and Others): Global Opportunity Analysis and Industry Forecast, 2021-2031
Hybrid Cloud in BFSI Market By Component (Solution and Services), Type (Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)), Enterprise Size (Large Enterprises, and Small & Medium Size Enterprises), and End User (Banking, NBFCs, and Others): Global Opportunity Analysis and Industry Forecast, 2021-2030
Predictive Analytics in Banking Market By Component (Solution and Service), Deployment Model (On-Premise and Cloud), Organization Size (Large Enterprise and SME), Application (Fraud Detection and Prevention, Customer Management, Sales and Marketing, Workforce Management and Others): Global Opportunity Analysis and Industry Forecast, 2019-2026
API Banking Market By Component (Solution [Application Programming Interface Platform, Application Programming Interface Analytics, and Application Programming Interface Security], and Service [Training & Education, Integration & Implementation, Support & Maintenance, and Consulting]), Organization Size (Small & Medium-Sized Enterprise, and Large Enterprise), and Deployment (Cloud-Based and On-Premise): Global Opportunity Analysis and Industry Forecast, 2022-2029
About Us:
Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of “Market Research Reports“ and “Business Intelligence Solutions.” AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.
We are in professional corporate relations with various companies and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Allied Market Research CEO Pawan Kumar is instrumental in inspiring and encouraging everyone associated with the company to maintain high quality of data and help clients in every way possible to achieve success. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.