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2021
Ai In Banking Market

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

A11871
Pages: 333
Oct 2021 | 4279 Views
Author(s) : Pramod Borasi, Shadaab Khan , Vineet Kumar
Tables: 177
Charts: 84
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COVID-19

Pandemic disrupted the entire world and affected many industries.

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The global AI in banking market size was valued at $3.88 billion in 2020, and is projected to reach $64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030. 

COVID-19 pandemic is expected to positively impact the growth rate of the AI in banking market, owing to increased shift towards work from home culture across banks & fintech agencies and rapid adoption of artificial intelligence and machine learning tools in banking organizations for performing critical jobs across the globe.

Artificial Intelligence (AI) brings the advantage of digitization to banks and helps them meet the competition posed by FinTech players. For instance, according to joint research conducted by the National Business Research Institute and Narrative Science in 2020, about 32% of banks are already using AI technologies such as predictive analytics, voice recognition, and various others, to have a competitive advantage in the market. 

AI-in-Banking-Market-2021-2030

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Furthermore, AI also enables banks to manage huge volumes of data at record speed to derive valuable insights and develops a better understanding of customers and their behavior. This enables banks to customize financial products and services by adding personalized features and intuitive interactions to deliver meaningful customer engagement and build strong relationships with its customers.

Improvement in data collection technology among the banks and financial institutions positively impacts the AI in banking market growth. In addition, increase in investment by banks in AI and rise in customer preferences for personalized financial services boost the growth of the market across the globe. However, factors such as higher deployment cost of AI and advance machine learning and lack of skilled labor are limiting market growth. On the contrary, surge in adoption of modern applications in banks is expected to offer remunerative opportunities for the expansion of the AI in banking market during the forecast period.

By component, the solution segment is expected to garner a significant AI in banking market share during the forecast period owing to managing the massive volume of data being generated for meaningful insights and better-informed decisions. Moreover, companies majorly focus on creating novel opportunities for growth and revenue generation, thereby increasing the preference for AI and advance machine learning algorithms across industries. However, the service segment is expected to witness growth at the highest rate during the forecast period, owing to surge in demand for cloud-based AI services among the end users.

Region wise, the AI in banking market share was dominated by North America in 2020 owing to increase in demand for modernizing banks and legacy business systems across the region. In addition, surge in demand for conducting hassle-and risk-free digital transformation among financial institutes is anticipated to boost the growth of the market. However, Asia- Pacific is expected to witness significant growth during the forecast period, owing to surge in need to monitor growing number of financial violations and offences.

The report focuses on growth prospects, restraints, and trends of the AI in banking market analysis. The study provides Porter’s five forces analysis to understand impact of various factors, such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers, on the AI in banking market share.

Segment review

The global AI in banking market share is segmented on the basis of component, enterprise size, application, technology and region. Depending on component, the market is segregated into solution and service.

Depending on enterprise size, it is fragmented into large enterprises and SMEs.  Based on application, the market is divided into risk management compliance & security, customer service, back office/operations, financial advisory and others. On the basis of technology, it is categorized into machine learning & deep learning, natural language processing (NLP), computer vision and others. Region wise, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.

AI in Banking Market
By Component

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Solution is segment is projected as one of the most lucrative segments.

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The key players profiled in the AI in banking market are Amazon Web Services Inc., BigML, Inc, Cisco Systems, Inc., Fair Isaac Corporation, Hewlett Packard Enterprise Development LP, International Business Machines Corporation, Microsoft Corporation, RapidMiner, Inc., SAP SE and SAS Institute Inc. These players have adopted various strategies to increase their market penetration and strengthen their position in the AI in banking industry.

COVID-19 impact analysis

The COVID-19 outbreak has a significant impact on growth of the AI in banking market, mainly owing to rise in digitization among both the financial institutes and end users, the demand for advanced AI technology increased to reduce the load on the banking servers and reduce transaction delay with rising digital transaction during the pandemic. For instance, according to the survey of Prudential Regulation Authority (PRA) by Bank of England in August 2020, around 40% of respondents reported an increase in the importance of AI and data science for critical financial operation. Furthermore, around 35% of banks reported that AI and data science had a positive impact on technologies that support remote working among employees and on their overall security provided for AI projects. In addition, the pandemic has accelerated the use of AI-powered tools to manage a sudden increase in customer enquiries. Thus, number of such developments across the globe are anticipated to provide lucrative opportunities for the expansion of the market.

AI in Banking Market
By Enterprise Size

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Large Enterprises is projected as one of the most lucrative segments.

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Top impacting factors

Increase in investment by banks in AI 

Banks are increasing investment in AI solutions to transform the management process of FinTech and to provide better services to end users. In addition, with increase in complexity and competition in the banking sector, the demand for industry-specific solutions increased to meet its goals. Thus, to meet the requirement of customers, various banking institutes and FinTech are investing in AI solution, which, in turn, drives the AI in banking market growth. Furthermore, AI can assist financial institutes at various stages of risk management process ranging from identifying risk exposure, measuring, estimating, and assessing its effects. In addition, banks are adopting and developing AI techniques to analyze large volume of data and to deliver valuable insights to customers. Moreover, increase in investments in AI by FinTech & banks to enhance the automation process and to offer more streamlined and personalized customer experience propels the AI in banking market growth. In addition, major financial institutes such as Bank of America, JPMorgan, and Morgan Stanley, are investing heavily in AI technology to develop automated investment advisors and train systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring, thus augmenting the growth of the AI in banking market revenue.

Rise in preference for personalized financial services

End users are increasingly preferring personalized financial services, owing to surge in adoption of chatbots among banks and increase in competition among the banks for garnering maximum market share. Various banks are providing budget management apps powered by AI technology, which help customers to achieve their financial targets and improve their money management process, thus driving the growth of the market. Furthermore, robo-advisors are one of the other rapidly emerging trends in personalized financial services, as they specifically target investors with limited resources such as individuals and small- to medium-sized businesses for managing their funds. In addition, AI-based robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, and retirement plans for their users. Moreover, with rise of usage-based loans AI technologies are helping to calculate the interest suitable for each individual, which, in turn, propels the growth of the AI in banking market.

AI in Banking Market
By Region

2030
North America 
Europe
Asia-pacific
Lamea

Asia-Pacific would exhibit the highest CAGR of 34.3% during 2021-2030.

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Key Benefits For Stakeholders     

  • The study provides an in-depth analysis of the AI in banking market forecast along with the current trends and future estimations to explain the imminent investment pockets.
  • Information about key drivers, restraints, & opportunities and their impact analysis on the global AI in banking market is provided in the report.
  • Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
  • The quantitative analysis of the market from 2021 to 2030 is provided to determine the AI in banking market potential.

Key Market Segments

Component

  • Solution
    • Chatbot
    • Customer Behavior Analytics
    • Customer Relationship Management (CRM)
    • Data Analytics and Visualization
    • Fraud Detection
    • Others
  • Service

By Enterprise Size

  • Large Enterprise
  • SMEs

By Application

  • Risk Management Compliance & Security
  • Customer Service
  • Back Office/Operations
  • Financial Advisory 
  • Others 

By Technology

  • Machine Learning & Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision 
  • 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

  • Amazon Web Services Inc.
  • BigML, Inc
  • Cisco Systems, Inc.
  • Fair Isaac Corporation
  • Hewlett Packard Enterprise Development LP
  • International Business Machines Corporation
  • Microsoft Corporation
  • RapidMiner, Inc.
  • SAP SE
  • SAS Institute Inc.
 

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The adoption of AI in banking solutions has increased over the years to help organizations monitor production processes and to provide enhanced customer services. In addition, AI in banking technology is being used across a number of applications to help drive productivity, improve efficiency, and save people time and organizational funds. Furthermore, AI in banking tools is used to clean data sets, give predictions, improve decision-making, and to respond to customer service needs, which are expected to fuel the market growth. In addition, surge in adoption of cloud as well as mobile applications is expected to drive the growth of the market.

Key providers of AI in banking market such as SAP SE, International Business Machines Corporation, and Microsoft Corporation account for a significant share in the market. For instance, Saxo Bank, a leading Danish investment bank, managed to drastically reduce the time it takes to onboard new customers and get them trading on its platform, owing to investments in data science and advanced AI technologies by automating repeating and time-consuming tasks and provide better time management of workers.

Furthermore, financial institutes are collaborating with AI companies to enhance their existing AI system for better and secure systems. For instance, in August 2021, RBL Bank partnered with Amazon Web Services (AWS) to strengthen its AI-powered banking solutions and drive digital transformation at the bank, adding significant value to the bank’s innovative offerings, saving costs, and tightening risk controls. In addition, it is leveraging Amazon Textract, a machine learning service that automatically extracts text, handwriting, and data from scanned documents, across the bank’s risk and operations divisions to analyze documents such as financial statements, stock statements, and stock audit reports to predict default risk.

Moreover, many open banking platforms are leveraging solutions from financial technology providers to improve their management technology. For instance, in September 2020, open banking platform, Tink leveraged open banking technology from Enel to develop digital financial solutions for its clients in Italy and Europe. With the agreement, Tink will be able to support its clients in the daily management of their finances, with an innovative and engaging solution that uses machine learning to provide tailored and personalized advice. Thus, such developments across the globe drive the growth of the market.

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