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Subject: MRR

Machine Learning in Banking Market to Garner $21.27 Billion, Globally, By 2031 at 32.2% CAGR: Allied Market Research


Improved productivity of banks and faster banking operations using machine learning have boosted the growth of the global machine learning in banking market.

PORTLAND, Ore., Sept. 26, 2022 /PRNewswire/ -- 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.

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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 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.

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Major Market Players

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:

Machine Learning in Banking Market Key Segments:

By Component:

By Enterprise Size:

By Application:

By Region:

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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.

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