Machine learning (ML) in banking is transforming the process of transactions, as ML is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. In addition, ML is being used to help organizations to improve customer experience and to enhance their market share by enabling frictionless, 24/7 customer interactions. Furthermore, it provides various solutions to the banking sector to replace routine manual work with automation and to increase productivity. Moreover, ML applications offer the greatest cost savings opportunity to banks as it helps in reducing credit default frauds by monitoring suspicious transactions with compliance concerns.
Productivity of banks has improved owing to adoption of machine learning as it reduces the overall costs of banks and financial institutions. Moreover, faster banking operations using machine learning provides quicker responses and results to the organizations. In addition, better risk assessment through machine learning in the banking industry and better customer service boost the growth of the market across the globe. However, factors such as higher cost of implementation of machine learning technology and risk of unemployment owing to adoption of machine learning are limiting the growth of the market. On the contrary, technological advancements in machine learning technology are expected to provide major lucrative opportunities for the growth of the market in the upcoming years.
The global machine learning in banking market is segmented on the basis of component, enterprise size, application, and region. Depending on component, the market is segregated into solution and service. On the basis of enterprise size, it is fragmented into large enterprises and small & medium-sized enterprises (SMEs). As per application, the market is divided into credit scoring, risk management compliance & security, payments & transactions, customer service, and others. Region wise, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
The key players profiled in the machine learning in banking market analysis are Affirm, Inc., Amazon Web Services, Inc., BigML, Inc., Cisco Systems, Inc., FICO, Google LLC, Mindtree Ltd., Microsoft Corporation, SAP SE, and SPD-Group. These players have adopted various strategies such as product development to increase their market penetration and strengthen their position in the industry.
Productivity of banks has improved owing to adoption of machine learning as it reduces the overall costs of banks and financial institutions. Moreover, faster banking operations using machine learning provides quicker responses and results to the organizations. In addition, better risk assessment through machine learning in the banking industry and better customer service boost the growth of the market across the globe. However, factors such as higher cost of implementation of machine learning technology and risk of unemployment owing to adoption of machine learning are limiting the growth of the market. On the contrary, technological advancements in machine learning technology are expected to provide major lucrative opportunities for the growth of the market in the upcoming years.
The global machine learning in banking market is segmented on the basis of component, enterprise size, application, and region. Depending on component, the market is segregated into solution and service. On the basis of enterprise size, it is fragmented into large enterprises and small & medium-sized enterprises (SMEs). As per application, the market is divided into credit scoring, risk management compliance & security, payments & transactions, customer service, and others. Region wise, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.
The key players profiled in the machine learning in banking market analysis are Affirm, Inc., Amazon Web Services, Inc., BigML, Inc., Cisco Systems, Inc., FICO, Google LLC, Mindtree Ltd., Microsoft Corporation, SAP SE, and SPD-Group. These players have adopted various strategies such as product development to increase their market penetration and strengthen their position in the industry.
KEY BENEFITS FOR STAKEHOLDERS
- This report provides a quantitative analysis of the market segments, current trends, estimations, and dynamics of the machine learning in banking market analysis from 2021 to 2031 to identify the prevailing machine learning in banking market opportunities.
- The market research is offered along with information related to key drivers, restraints, and opportunities.
- Porter's five forces analysis highlights the potency of buyers and suppliers to enable stakeholders make profit-oriented business decisions and strengthen their supplier-buyer network.
- In-depth analysis of the machine learning in banking market segmentation assists to determine the prevailing market opportunities.
- Major countries in each region are mapped according to their revenue contribution to the global market.
- 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.
Key Market 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
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Netherlands
- Rest of Europe
- Asia-Pacific
- China
- Japan
- India
- Australia
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
Key Market Players
- Affirm, Inc.
- Amazon Web Services, Inc.
- Big ML, Inc.
- Cisco Systems, Inc.
- FICO
- Google LLC
- Mindtree
- Microsoft
- SAP SE
- SPD-Group
Table of Contents
CHAPTER 1: INTRODUCTION
CHAPTER 2: EXECUTIVE SUMMARY
CHAPTER 3: MARKET OVERVIEW
CHAPTER 4: MACHINE LEARNING IN BANKING MARKET, BY COMPONENT
CHAPTER 5: MACHINE LEARNING IN BANKING MARKET, BY ENTERPRISE SIZE
CHAPTER 6: MACHINE LEARNING IN BANKING MARKET, BY APPLICATION
CHAPTER 7: MACHINE LEARNING IN BANKING MARKET, BY REGION
CHAPTER 8: COMPANY LANDSCAPE
CHAPTER 9: COMPANY PROFILES
List of Tables
List of Figures
Executive Summary
According to the report, titled, “Machine Learning in Banking Market," the machine learning in banking market size was valued at $1.33 billion in 2021, and is estimated to reach $21.27 billion by 2031, growing at a CAGR of 32.2% from 2022 to 2031.In recent years, machine learning has been adopted by various banks for strategic decision making, customer insights, and understanding consumer purchasing behavior, and improving the digital transaction experience. For instance, in 2019, Government of India announced the rapid digitalization of the banking sector as part of the Digital India initiative that is expected to stimulate financial inclusion. RBI further promoted its policy of Secure and Informed Digital Banking. Moreover, Allied Digital Services Ltd., a publicly-traded global IT solutions, services, and master systems integration company, officially announced the launch of its new FinTech product FinoAllied, which is an ML-powered conversational banking platform, that comes with built-in banking services and transactions fully ready to be offered to the customers through various digital channels of the banks. Allied Digital sources claim that FinoAllied could be helpful for small and mid-size banks that are struggling in their digital transformation.
On the basis of application, the credit scoring segment dominated the market in 2021. This is attributed to the fact that machine learning in financial industry can expand a lender’s customer base to cover the so-called credit invisible people with thin or no credit histories and those whose credit scores are not accurate reflections of their risk. Therefore, these are the major growth factors for the machine learning in banking market for credit scoring.
Region wise, North America attained the highest growth in 2021. This is owing to growing pressure in managing risk along with increasing governance and regulatory requirements to improve personalized banking and to provide better customer service. In addition, rapid digitization in financial firms all across the region and adoption of machine learning among banks to monitor data for unusual transactions to detect and prevent fraudulent activities and to keep end users accounts secure drive the machine learning in banking market growth.
The COVID-19 pandemic had resulted in a positive impact on the machine learning in banking sector since most of the banks and other financial institutions readily adopted technology during the pandemic. Machine learning was one of the most widely adopted technology by banks worldwide during the pandemic. Therefore, the COVID-19 pandemic had a positive impact on the machine learning in banking market trends.
Key Findings of the Study
- By component, the solution segment led the machine learning in banking market in terms of revenue in 2021.
- By enterprise size, the large enterprises segment accounted for the highest machine learning in banking market share in 2021.
- By region, North America generated the highest revenue in 2021.
- The key players profiled in the machine learning in banking market analysis are Affirm, Inc., Amazon Web Services, Inc., BigML, Inc., Cisco Systems, Inc., FICO, Google LLC, Mindtree Ltd., Microsoft Corporation, SAP SE, and SPD-Group. These players have adopted various strategies such as product development to increase their market penetration and strengthen their position in machine learning in banking industry.
Companies Mentioned
- Affirm, Inc.
- Amazon Web Services, Inc.
- Big Ml, Inc.
- Cisco Systems, Inc.
- Fico
- Google LLC
- Mindtree
- Microsoft
- Sap Se
- Spd-Group
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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