UK Artificial Intelligence in Finance Market Size and Share 2032

Summary
Table Of Content

REPORT ATTRIBUTE	DETAILS
Historical Period	2019-2022
Base Year	2023
Forecast Period	2024-2032
UK Artificial Intelligence in Finance Market Size 2024	USD 1,245 million
UK Artificial Intelligence in Finance Market, CAGR	28.1%
UK Artificial Intelligence in Finance Market Size 2032	USD 11,569 million

Market Overview

The UK AI in Finance Market is projected to grow from USD 1,245 million in 2023 to an estimated USD 11,569 million by 2032, with a compound annual growth rate (CAGR) of 28.1% from 2024 to 2032.

Key market drivers include the rising demand for enhanced security and fraud prevention in financial transactions, along with the need for more personalized financial services. Additionally, the growing volume of financial data and the emergence of AI-driven tools for better decision-making are fueling market expansion. Trends such as the increasing integration of AI with blockchain technology and the continued development of robo-advisors are further transforming the finance sector. Financial institutions are increasingly leveraging AI to optimize customer experiences, improve compliance, and streamline operations.

Geographically, the UK AI in Finance market benefits from its well-established financial services sector, with key players such as HSBC, Barclays, and Lloyds Banking Group driving innovation in AI applications. The UK’s strong regulatory framework and investment in digital infrastructure further support the market’s growth. The increasing focus on AI-driven solutions in the region positions the UK as a leading player in the European and global AI in finance landscape.

Access crucial information at unmatched prices!

Request your sample report today & start making informed decisions powered by Credence Research!

Download Sample

Market Insights

The UK AI in Finance market is projected to grow significantly, from USD 1,245 million in 2023 to USD 11,569 million by 2032, driven by advancements in AI technology and financial services.
The increasing adoption of AI technologies for fraud detection, risk management, and personalized customer services is accelerating growth in the UK finance sector.
The growing demand for enhanced operational efficiency and predictive analytics to support decision-making is fueling the adoption of AI in financial institutions.
Data privacy concerns and stringent regulatory requirements are challenges hindering the widespread adoption of AI solutions in the financial industry.
High implementation costs and technological complexities associated with AI integration into existing systems are barriers for smaller financial institutions.
England leads the market, particularly in London, with the highest concentration of financial institutions investing in AI solutions for enhanced customer experience and operational efficiency.
Scotland, Wales, and Northern Ireland are also experiencing growing adoption of AI in finance, supported by increasing investments in fintech and digital infrastructure.

Market Drivers

Growing Demand for Enhanced Fraud Detection and Cybersecurity

One of the primary drivers of the UK AI in Finance market is the increasing demand for enhanced fraud detection and improved cybersecurity measures. As the financial sector continues to expand and digital transactions become more prevalent, so do the threats associated with cybersecurity. Financial institutions are adopting AI-powered tools to detect and prevent fraud in real time. Machine learning algorithms are increasingly used to monitor transactions and detect suspicious patterns that may indicate fraudulent activity. These AI systems can analyze vast amounts of data at an unprecedented speed, allowing for faster responses to threats and minimizing potential financial losses. For instance, HSBC has implemented AI to scan billions of transactions annually, identifying patterns indicative of money laundering in real time, thereby enhancing their fraud detection capabilities. Furthermore, the rise in sophisticated cyber-attacks necessitates the use of advanced AI systems for threat detection, risk assessment, and mitigation. AI’s ability to continuously learn from new data and adapt to emerging threats makes it an invaluable tool in combating cybercrime within the financial sector. As financial institutions face mounting pressure to protect sensitive data and maintain trust with customers, AI-driven fraud prevention systems are becoming indispensable.

Increasing Adoption of AI for Personalization and Customer Service

Another key driver in the UK AI in Finance market is the growing adoption of AI to deliver personalized financial services and enhance customer experiences. As customer expectations evolve, financial institutions are turning to AI-driven solutions to offer more tailored services and create engaging customer experiences. AI-powered chatbots and virtual assistants are already playing a pivotal role in improving customer service by providing 24/7 support and addressing customer queries in real time. These tools not only help reduce operational costs but also streamline customer interactions, enhancing satisfaction and engagement. For instance, Bank of America’s virtual assistant, Erica, uses AI to analyze customer data, providing personalized financial advice and insights, which has led to a significant improvement in customer engagement and satisfaction. Additionally, AI’s ability to analyze customer behavior and financial patterns enables the development of personalized financial products, such as customized investment advice, savings plans, and loan offerings. Robo-advisors powered by AI are increasingly used by both retail investors and institutional clients to receive personalized portfolio management services at a fraction of the cost of traditional advisory services. The ability to offer hyper-personalized experiences is thus becoming a competitive advantage, driving financial institutions to invest in AI technologies.

Advancements in Machine Learning and Big Data Analytics

The rapid advancements in machine learning (ML) and big data analytics are playing a crucial role in accelerating the adoption of AI in the UK finance sector. The exponential growth of financial data, coupled with the growing need for actionable insights, has made machine learning algorithms indispensable for analyzing vast datasets. AI systems can sift through large amounts of structured and unstructured data, identifying trends, correlations, and anomalies that would otherwise go unnoticed. For instance, JPMorgan Chase employs AI algorithms to analyze customer spending patterns and lifestyle data, enabling the bank to offer highly personalized credit card and loan offers, which has resulted in higher customer retention and increased cross-selling opportunities. This capability is especially valuable for financial institutions that need to make data-driven decisions quickly in areas such as credit risk analysis, market prediction, and portfolio optimization. Additionally, the integration of AI with big data platforms enables financial institutions to enhance their decision-making processes, improve forecasting accuracy, and gain a deeper understanding of consumer behavior. As ML and data analytics continue to evolve, their ability to process complex datasets will remain a driving force behind AI adoption in the finance sector.

Regulatory Compliance and Operational Efficiency

The need to comply with stringent regulatory requirements while ensuring operational efficiency is a significant driver of AI adoption in the UK finance market. The financial services industry is heavily regulated, with institutions required to comply with laws related to anti-money laundering (AML), know-your-customer (KYC), data protection, and more. Meeting these regulatory demands can be time-consuming without advanced tools. AI-driven solutions are helping automate compliance processes by reducing manual tasks while ensuring adherence to regulations in real time. For instance, UBS uses AI tools to continuously monitor regulatory changes, automatically updating compliance procedures to ensure adherence to the latest standards, thereby reducing the risk of non-compliance and associated penalties. In addition to improving compliance, AI can help increase operational efficiency by streamlining back-office processes, reducing human error risks, and lowering operational costs. As regulatory frameworks evolve rapidly, AI technologies provide financial institutions with agility needed for quick adaptation while maintaining compliance standards effectively. This operational advantage is driving increasing integration of AI within financial services industries across sectors like banking or insurance while contributing significantly toward market growth trends globally.

Market Trends

Integration of AI with Blockchain Technology

One of the most notable trends in the UK AI in Finance market is the increasing integration of AI with blockchain technology. Blockchain, known for its security, transparency, and decentralized nature, is a natural partner for AI in transforming the financial services landscape. Financial institutions in the UK are exploring ways to combine these two technologies to streamline operations, enhance security, and reduce costs. AI’s role in blockchain applications revolves around enhancing data processing capabilities, improving transaction efficiency, and ensuring better compliance. For instance, AI algorithms can analyze transaction data on a blockchain to detect patterns of fraud or anomalies in real time. A UK-based bank successfully implemented an AI system to monitor its blockchain-based payment system, identifying and preventing a sophisticated scam that could have caused significant financial losses. Additionally, blockchain technology enhances the integrity and transparency of AI decision-making processes, ensuring that AI-based transactions and analyses are trustworthy and verifiable. This convergence is expected to provide more secure, scalable, and efficient financial systems, fostering innovation in digital payments, asset management, and beyond. As these technologies continue to evolve together, they hold immense potential to revolutionize the financial sector by addressing critical challenges like fraud detection and operational inefficiencies.

Rise of AI-powered Robo-Advisors

Another significant trend in the UK AI in Finance market is the rise of AI-powered robo-advisors. These platforms use AI algorithms to provide personalized financial advice and portfolio management services, offering cost-effective and tailored solutions for both retail investors and institutional clients. Robo-advisors analyze a client’s financial situation, investment goals, and risk tolerance to deliver personalized recommendations. Unlike traditional human advisors, they process large datasets and respond to market fluctuations instantly, enabling real-time portfolio adjustments. For instance, a UK fintech startup has developed a robo-advisor platform that uses AI to tailor investment portfolios based on individual client data. This includes analyzing spending habits and investment preferences derived from social media activity alongside traditional financial metrics. By providing advice at a lower cost compared to traditional advisors, robo-advisors democratize access to financial services while empowering individuals with better investment strategies. Moreover, these platforms continuously learn from clients’ behaviors and market changes, improving recommendation quality over time. As more investors seek convenient, low-cost solutions for managing their finances, AI-powered robo-advisors are becoming an increasingly prominent feature in the UK finance market, driving innovation in personalized financial planning while making wealth management accessible to a broader audience.

Focus on AI-driven Risk Management and Predictive Analytics

AI-driven risk management and predictive analytics are becoming increasingly important trends in the UK AI in Finance market. Financial institutions are leveraging AI technologies to manage various risks such as credit risk, market risk, operational risk, and liquidity risk. By analyzing historical data and generating predictive models, AI enables institutions to identify potential risks and make informed decisions proactively. For instance, a major UK bank has integrated AI into its credit scoring system where machine learning models assess borrowers’ creditworthiness by analyzing transaction histories and spending patterns alongside traditional credit reports. This approach allows for more nuanced risk assessments that go beyond conventional methods. Similarly, predictive analytics powered by AI helps forecast market trends, enabling investment firms and banks to make accurate predictions about stock performance or economic indicators. These capabilities enhance risk management processes by reducing exposure to volatile markets and creating robust financial strategies. With increasing global uncertainties impacting markets worldwide, proactive risk prediction through AI is becoming indispensable for maintaining stability in the financial sector. As institutions continue adopting these tools, they can better navigate complex risks while improving operational efficiency and fostering confidence among investors.

Advancements in AI for Regulatory Compliance (RegTech)

Advancements in AI for regulatory compliance (RegTech) represent another significant trend in the UK’s AI in Finance market. The growing complexity of global regulations has made compliance increasingly challenging for financial institutions due to rising costs and resource demands. In response, many institutions are turning to AI technologies to automate compliance processes while enhancing accuracy and reducing human error. Machine learning algorithms can analyze vast volumes of data to identify potential violations of regulations such as anti-money laundering (AML) or know-your-customer (KYC) rules. For instance, a UK financial institution implemented an AI-driven RegTech solution that automatically scans regulatory documents to ensure compliance with AML and KYC requirements. This system significantly reduced manual oversight efforts while allowing real-time updates as regulations evolved. Additionally, these tools can detect suspicious transactions automatically and flag them for further review—streamlining processes that traditionally required extensive human intervention. By integrating AI into compliance frameworks, financial institutions not only avoid costly fines but also gain agility in responding to regulatory changes swiftly. As regulatory environments grow more stringent globally, RegTech solutions powered by AI will remain crucial for maintaining compliance while improving operational efficiency across the financial sector.

Market Challenges

Expansion of AI-Driven Personalized Financial Services

One of the significant market opportunities in the UK AI in Finance market is the growing demand for AI-driven personalized financial services. As customers increasingly seek tailored financial solutions, financial institutions are turning to AI technologies to deliver more individualized offerings. AI’s ability to analyze large volumes of customer data enables financial institutions to understand clients’ preferences, behaviors, and financial needs, thereby providing highly personalized services such as customized investment advice, savings plans, and lending solutions. For instance, AI technologies are being utilized by UK financial institutions to analyze customer transaction histories, spending patterns, and investment behaviors. This analysis allows for the creation of bespoke financial products like personalized savings accounts that adjust interest rates based on individual saving habits or investment portfolios tailored to risk tolerance and financial goals. With the rise of robo-advisors and AI-powered wealth management tools, the opportunity to serve both retail and institutional clients with cost-effective, data-driven solutions is substantial. Financial institutions that capitalize on AI to enhance customer experiences and provide personalized services are well-positioned to attract new customers and retain existing ones, fostering long-term customer loyalty and increasing market share in a highly competitive environment.

Advancements in AI for Regulatory Compliance and Risk Management

Another promising opportunity lies in the growing demand for AI solutions in regulatory compliance (RegTech) and risk management. Financial institutions are under constant pressure to adhere to stringent regulatory requirements while mitigating various risks, including credit risk, fraud, and market volatility. AI technologies can automate compliance processes, analyze vast datasets for anomalies, and predict potential risks, significantly improving operational efficiency and reducing costs. For instance, AI tools are now capable of scanning through millions of transactions in real-time to detect patterns indicative of fraud or money laundering, as demonstrated by UK Finance’s case studies where AI reduced customer complaint handling times by 30-50% and Know Your Customer (KYC) processing times by 90%. Additionally, AI systems are being employed to interpret and adapt to complex regulatory environments, ensuring compliance with laws like the Digital Operational Resilience Act (DORA) and the EU AI Act. As regulatory landscapes continue to evolve, AI’s ability to provide real-time compliance monitoring and adaptive risk management solutions is becoming increasingly vital. Financial firms that invest in AI-driven RegTech solutions can streamline compliance, reduce regulatory fines, and better manage risks, positioning themselves for sustained growth in a complex and fast-changing regulatory environment.

Market Opportunities

Expansion of AI-Driven Personalized Financial Services

One of the significant market opportunities in the UK AI in Finance market is the growing demand for AI-driven personalized financial services. As customers increasingly seek tailored financial solutions, financial institutions are turning to AI technologies to deliver more individualized offerings. AI’s ability to analyze large volumes of customer data enables financial institutions to understand clients’ preferences, behaviors, and financial needs, thereby providing highly personalized services such as customized investment advice, savings plans, and lending solutions. For instance, AI technologies are being utilized by UK financial institutions to analyze customer transaction histories, spending patterns, and investment behaviors. This analysis allows for the creation of bespoke financial products like personalized savings accounts that adjust interest rates based on individual saving habits or investment portfolios tailored to risk tolerance and financial goals. With the rise of robo-advisors and AI-powered wealth management tools, the opportunity to serve both retail and institutional clients with cost-effective, data-driven solutions is substantial. Financial institutions that capitalize on AI to enhance customer experiences and provide personalized services are well-positioned to attract new customers and retain existing ones, fostering long-term customer loyalty and increasing market share in a highly competitive environment.

Advancements in AI for Regulatory Compliance and Risk Management

Another promising opportunity lies in the growing demand for AI solutions in regulatory compliance (RegTech) and risk management. Financial institutions are under constant pressure to adhere to stringent regulatory requirements while mitigating various risks, including credit risk, fraud, and market volatility. AI technologies can automate compliance processes, analyze vast datasets for anomalies, and predict potential risks, significantly improving operational efficiency and reducing costs. For instance, AI tools are now capable of scanning through millions of transactions in real-time to detect patterns indicative of fraud or money laundering, as demonstrated by UK Finance’s case studies where AI reduced customer complaint handling times by 30-50% and Know Your Customer (KYC) processing times by 90%. Additionally, AI systems are being employed to interpret and adapt to complex regulatory environments, ensuring compliance with laws like the Digital Operational Resilience Act (DORA) and the EU AI Act. As regulatory landscapes continue to evolve, AI’s ability to provide real-time compliance monitoring and adaptive risk management solutions is becoming increasingly vital. Financial firms that invest in AI-driven RegTech solutions can streamline compliance, reduce regulatory fines, and better manage risks, positioning themselves for sustained growth in a complex and fast-changing regulatory environment.

Market Segmentation Analysis

By Component

The market is divided into solutions and services. The solution segment includes AI-powered software tools for automation, analytics, fraud detection, and customer service, among others. The services segment encompasses consulting, integration, and managed services that support the implementation and optimization of AI technologies. The growing demand for automation and data-driven decision-making is boosting the adoption of AI solutions, while financial institutions increasingly seek expert guidance and support for effective integration and deployment of AI.

By Deployment Mode

The deployment mode segment is divided into on-premise and cloud-based solutions. On-premise deployment refers to AI systems hosted within the financial institution’s own data centers, offering greater control over data security and infrastructure. Cloud-based deployment, on the other hand, is witnessing rapid growth due to its scalability, cost-effectiveness, and ease of access to powerful computing resources. As more financial institutions move to cloud environments, the cloud-based deployment model is expected to dominate the market, enabling firms to scale AI-driven services more efficiently and with less overhead.

Segments

Based on component

Solution
Services

Based on deployment mode

On-premise
Cloud

Based on technology

Generative AI
Other AI Technologies

Based on Application

Virtual Assistant (Chatbots)
Business Analytics and Reporting
Fraud Detection
Quantitative and Asset Management
Others

Based on region

England
Scotland
Wales
Northern Ireland

Regional Analysis

England (75%)

England, particularly London, dominates the UK AI in Finance market, accounting for the largest market share of approximately 75%. London, as a global financial hub, is home to numerous multinational banks, fintech companies, and other financial institutions that are at the forefront of adopting AI solutions. The region’s strong digital infrastructure, significant investments in financial technology, and supportive regulatory environment have contributed to the rapid integration of AI technologies across various financial applications, such as fraud detection, asset management, and customer service. With a focus on innovation, England leads in AI research and development, with many startups and tech companies developing cutting-edge AI-driven solutions for the financial sector. The concentration of financial services in London has made it the focal point of AI adoption in the UK, setting the pace for the rest of the nation.

Scotland (12%)

Scotland holds a smaller but rapidly growing share of the market, contributing around 12%. Key financial cities like Edinburgh and Glasgow are emerging as strong centers for fintech and AI innovation. Scotland has witnessed a rise in AI adoption among banks and insurance companies, primarily focused on improving customer experiences and operational efficiency. Additionally, the Scottish government has actively supported AI development through various initiatives aimed at fostering digital skills, research, and the startup ecosystem. This has provided a conducive environment for AI-based solutions to thrive, particularly in sectors like risk management, compliance, and predictive analytics. As a result, Scotland is positioning itself as an increasingly important region for AI adoption in the UK financial services sector.

Shape Your Report to Specific Countries or Regions & Enjoy 30% Off!

Customize Now

Key players

Instructure, Inc.
Google LLC
Workiva
FIS
Zoho
Microsoft Corporation
IBM Corporation
SAP SE
NetApp
Highradius
Intel Corporation
AWS (Amazon Web Services)
Oracle Corporation

Competitive Analysis

The UK AI in Finance market is highly competitive, with several major players leading the charge in AI-driven solutions. Companies like Google LLC, Microsoft Corporation, and IBM Corporation have established themselves as strong contenders, leveraging their advanced AI capabilities to provide a range of services in data analytics, machine learning, and cloud-based solutions. FIS, SAP SE, and Oracle Corporation focus on offering robust financial services platforms that integrate AI to improve operational efficiency, fraud detection, and risk management. Meanwhile, AWS and Intel Corporation provide essential cloud computing infrastructure and AI hardware solutions, enabling financial institutions to scale their AI capabilities. Companies such as Instructure, Inc., Zoho, and Workiva also compete by delivering AI-enhanced platforms for financial reporting, compliance, and business analytics. These players are continuously innovating to meet the evolving needs of financial institutions, with a strong emphasis on regulatory compliance, security, and customer-centric solutions.

Recent Developments

In February 2025, Microsoft announced its vision for AI agents reshaping corporate finance functions. These agents, developed through tools like Copilot Studio, are designed to automate labor-intensive processes such as financial reconciliation and compliance while dynamically adapting to user needs. For example, Pets at Home, a UK-based company, created an AI agent for its profit protection team, potentially saving millions annually. Additionally, Microsoft committed £2.5 billion to expand its AI datacenter infrastructure in the UK over the next three years, aiming to double its footprint and train over one million people for the AI economy. This investment underscores Microsoft’s commitment to bolstering AI adoption across sectors, including finance.

In November 2024, at the Think London event IBM showcased how its hybrid cloud and AI solutions are enabling scalability and compliance for financial institutions. Virgin Money implemented IBM’s “Redi” intelligent agent to optimize customer service, reducing call volumes and improving operational efficiency while enhancing customer satisfaction metrics like Net Promoter Scores (NPS). IBM also emphasized partnerships with AWS and Microsoft to create robust AI ecosystems tailored for financial services.

On February 5, 2025, Zoho unveiled its “agentic AI” capabilities across its platform, enabling enterprises to create and deploy autonomous digital agents. These tools aim to enhance customer engagement and operational efficiency in financial services by leveraging Zoho’s shared data model and engineering expertise. The initiative reflects Zoho’s focus on low-code/no-code solutions and productivity gains for developers using AI technologies. This development is expected to streamline processes like customer relationship management (CRM) within the finance sector.

As of February 2025, FIS has focused on integrating AI into treasury and risk management systems for financial firms in the UK. These systems enable real-time insights into cash flow forecasts and risk modeling while automating complex processes like collateralized loan obligations. FIS’s research highlights how AI is improving operational efficiency and reducing errors in securities processing through predictive analytics.

In December 2024, SAP introduced generative AI features in tools like Advanced Financial Closing to automate error detection during financial reconciliations, saving significant time per error resolved. These innovations aim to reduce compliance costs while improving accuracy in regulatory reporting.

Market Concentration and Characteristics

The UK AI in Finance market exhibits moderate to high concentration, with a mix of global technology giants, fintech startups, and established financial institutions driving innovation and adoption. Major players like Google LLC, Microsoft Corporation, IBM Corporation, and AWS dominate the market by providing advanced AI solutions across various applications such as fraud detection, risk management, and personalized financial services. These companies benefit from substantial resources, cutting-edge AI technologies, and large-scale infrastructure, allowing them to capture a significant market share. However, the market also sees contributions from specialized fintech companies and niche AI providers, offering tailored solutions to meet the unique needs of financial institutions. The market characteristics are defined by rapid technological advancements, a high degree of regulatory scrutiny, and a focus on improving operational efficiency, security, and customer experience. With ongoing innovation and a growing demand for AI-driven solutions, the competitive landscape remains dynamic and evolving.

Report Coverage

The research report offers an in-depth analysis based on Component, Deployment Mode, Technology, Application and Region. It details leading market players, providing an overview of their business, product offerings, investments, revenue streams, and key applications. Additionally, the report includes insights into the competitive environment, SWOT analysis, current market trends, as well as the primary drivers and constraints. Furthermore, it discusses various factors that have driven market expansion in recent years. The report also explores market dynamics, regulatory scenarios, and technological advancements that are shaping the industry. It assesses the impact of external factors and global economic changes on market growth. Lastly, it provides strategic recommendations for new entrants and established companies to navigate the complexities of the market.

Future Outlook

Financial institutions in the UK will continue to adopt AI technologies to streamline operations, enhance customer experiences, and improve decision-making. As AI becomes more accessible, smaller institutions will also leverage its benefits.

AI-driven RegTech solutions will play a pivotal role in helping financial institutions meet evolving regulatory requirements more efficiently. AI’s ability to monitor and adapt to regulatory changes in real-time will further boost its use in compliance processes.

With the rise in cyber threats, AI-powered fraud detection systems will see increased adoption. Machine learning algorithms will be crucial in identifying patterns and preventing fraudulent activities in real-time.

Generative AI will drive innovation in personalized financial services, enabling hyper-customized investment advice, wealth management solutions, and more. Financial institutions will use generative AI to create tailored products for individual customers.

Financial institutions and fintech companies will continue to invest in AI research and development. This focus on innovation will lead to the creation of more sophisticated AI tools designed to address specific financial sector challenges.

As cloud infrastructure becomes more advanced, more financial institutions in the UK will move their AI applications to cloud-based platforms. This shift will improve scalability, flexibility, and reduce operational costs.

Robo-advisors, powered by AI, will continue to gain popularity, offering cost-effective and personalized investment management solutions. They will become increasingly sophisticated in providing tailored financial guidance to retail investors.

Financial institutions will partner with AI startups to develop innovative solutions, fostering a collaborative ecosystem. This will lead to faster development of AI applications specifically tailored for the financial services sector.

The integration of AI with blockchain technology will increase, improving the security and transparency of financial transactions. AI will help detect and prevent fraudulent activities in blockchain networks, ensuring secure transactions.

As AI adoption grows, the UK financial sector will face challenges in regulating AI applications, especially in areas such as data privacy and ethical decision-making. Balancing innovation with ethical AI use will become a critical focus.

CHAPTER NO. 1 : INTRODUCTION 19

1.1.1. Report Description 19

Purpose of the Report 19

USP & Key Offerings 19

1.1.2. Key Benefits for Stakeholders 19

1.1.3. Target Audience 20

1.1.4. Report Scope 20

CHAPTER NO. 2 : EXECUTIVE SUMMARY 21

2.1. UK Artificial Intelligence in Finance Market Snapshot 21

2.1.1. UK Artificial Intelligence in Finance Market, 2018 – 2032 (USD Million) 22

CHAPTER NO. 3 : GEOPOLITICAL CRISIS IMPACT ANALYSIS 23

3.1. Russia-Ukraine and Israel-Palestine War Impacts 23

CHAPTER NO. 4 : ARTIFICIAL INTELLIGENCE IN FINANCE MARKET – INDUSTRY ANALYSIS 24

4.1. Introduction 24

4.2. Market Drivers 25

4.2.1. Driving Factor 1 Analysis 25

4.2.2. Driving Factor 2 Analysis 26

4.3. Market Restraints 27

4.3.1. Restraining Factor Analysis 27

4.4. Market Opportunities 28

4.4.1. Market Opportunity Analysis 28

4.5. Porter’s Five Force analysis 29

4.6. Value Chain Analysis 30

4.7. Buying Criteria 31

CHAPTER NO. 5 : ANALYSIS COMPETITIVE LANDSCAPE 32

5.1. Company Market Share Analysis – 2023 32

5.1.1. UK Artificial Intelligence in Finance Market: Company Market Share, by Revenue, 2023 32

5.1.2. UK Artificial Intelligence in Finance Market: Top 6 Company Market Share, by Revenue, 2023 32

5.1.3. UK Artificial Intelligence in Finance Market: Top 3 Company Market Share, by Revenue, 2023 33

5.2. UK Artificial Intelligence in Finance Market Company Revenue Market Share, 2023 34

5.3. Company Assessment Metrics, 2023 35

5.3.1. Stars 35

5.3.2. Emerging Leaders 35

5.3.3. Pervasive Players 35

5.3.4. Participants 35

5.4. Start-ups /Business Analytics and Reporting Assessment Metrics, 2023 35

5.4.1. Progressive Companies 35

5.4.2. Responsive Companies 35

5.4.3. Dynamic Companies 35

5.4.4. Starting Blocks 35

5.5. Strategic Developments 36

5.5.1. Acquisitions & Mergers 36

New Product Launch 36

Regional Expansion 36

5.6. Key Players Product Matrix 37

CHAPTER NO. 6 : PESTEL & ADJACENT MARKET ANALYSIS 38

6.1. PESTEL 38

6.1.1. Political Factors 38

6.1.2. Economic Factors 38

6.1.3. Social Factors 38

6.1.4. Technological Factors 38

6.1.5. Environmental Factors 38

6.1.6. Legal Factors 38

6.2. Adjacent Market Analysis 38

CHAPTER NO. 7 : UK ARTIFICIAL INTELLIGENCE IN FINANCE MARKET – BY COMPONENT SEGMENT ANALYSIS 39

7.1. UK Artificial Intelligence in Finance Market Overview, by Component Segment 39

7.1.1. UK Artificial Intelligence in Finance Market Revenue Share, By Component, 2023 & 2032 40

7.1.2. UK Artificial Intelligence in Finance Market Attractiveness Analysis, By Component 41

7.1.3. Incremental Revenue Growth Opportunity, by Component, 2024 – 2032 41

7.1.4. UK Artificial Intelligence in Finance Market Revenue, By Component, 2018, 2023, 2027 & 2032 42

7.2. Solution 43

7.3. Services 44

CHAPTER NO. 8 : UK ARTIFICIAL INTELLIGENCE IN FINANCE MARKET – BY DEPLOYMENT MODE SEGMENT ANALYSIS 45

8.1. Artificial Intelligence in Finance Market Overview, by Deployment Mode Segment 45

8.1.1. Artificial Intelligence in Finance Market Revenue Share, By Deployment Mode, 2023 & 2032 46

8.1.2. Artificial Intelligence in Finance Market Attractiveness Analysis, By Deployment Mode 47

8.1.3. Incremental Revenue Growth Opportunity, by Deployment Mode, 2024 – 2032 47

8.1.4. Artificial Intelligence in Finance Market Revenue, By Deployment Mode, 2018, 2023, 2027 & 2032 48

8.2. On-premise 49

8.3. Cloud 50

CHAPTER NO. 9 : UK ARTIFICIAL INTELLIGENCE IN FINANCE MARKET – BY TECHNOLOGY SEGMENT ANALYSIS 51

9.1. Artificial Intelligence in Finance Market Overview, by Technology Segment 51

9.1.1. Artificial Intelligence in Finance Market Revenue Share, By Technology, 2023 & 2032 52

9.1.2. Artificial Intelligence in Finance Market Attractiveness Analysis, By Technology 53

9.1.3. Incremental Revenue Growth Opportunity, by Technology, 2024 – 2032 53

9.1.4. Artificial Intelligence in Finance Market Revenue, By Technology, 2018, 2023, 2027 & 2032 54

9.2. Generative AI 55

9.3. Other AI Technologies 56

CHAPTER NO. 10 : UK ARTIFICIAL INTELLIGENCE IN FINANCE MARKET – BY APPLICATION SEGMENT ANALYSIS 57

10.1. Artificial Intelligence in Finance Market Overview, by Application Segment 57

10.1.1. Artificial Intelligence in Finance Market Revenue Share, By Application, 2023 & 2032 58

10.1.2. Artificial Intelligence in Finance Market Attractiveness Analysis, By Application 59

10.1.3. Incremental Revenue Growth Opportunity, by Application, 2024 – 2032 59

10.1.4. Artificial Intelligence in Finance Market Revenue, By Application, 2018, 2023, 2027 & 2032 60

10.2. Virtual Assistant (Chatbots) 61

10.3. Business Analytics and Reporting 62

10.4. Fraud Detection 63

10.5. Quantitative and Asset Management 64

10.6. Others 65

CHAPTER NO. 11 : UK ARTIFICIAL INTELLIGENCE IN FINANCE MARKET 66

11.1. UK 66

11.1.1. Key Highlights 66

11.2. Component 67

11.3. UK Artificial Intelligence in Finance Market Revenue, By Component, 2018 – 2023 (USD Million) 67

11.4. UK Artificial Intelligence in Finance Market Revenue, By Component, 2024 – 2032 (USD Million) 67

11.5. Deployment Mode 68

11.6. UK Artificial Intelligence in Finance Market Revenue, By Deployment Mode, 2018 – 2023 (USD Million) 68

11.6.1. UK Artificial Intelligence in Finance Market Revenue, By Deployment Mode, 2024 – 2032 (USD Million) 68

11.7. Technology 69

11.8. UK Artificial Intelligence in Finance Market Revenue, By Technology, 2018 – 2023 (USD Million) 69

11.8.1. UK Artificial Intelligence in Finance Market Revenue, By Technology, 2024 – 2032 (USD Million) 69

11.9. Application 70

11.9.1. UK Artificial Intelligence in Finance Market Revenue, By Application, 2018 – 2023 (USD Million) 70

11.9.2. UK Artificial Intelligence in Finance Market Revenue, By Application, 2024 – 2032 (USD Million) 70

CHAPTER NO. 12 : COMPANY PROFILES 71

12.1. Instructure, Inc. 71

12.1.1. Company Overview 71

12.1.2. Product Portfolio 71

12.1.3. Swot Analysis 71

12.1.4. Business Strategy 72

12.1.5. Financial Overview 72

12.2. Google LLC 73

12.3. Workiva 73

12.4. FIS 73

12.5. Zoho 73

12.6. Google LLC 73

12.7. Microsoft Corporation 73

12.8. IBM Corporation 73

12.9. SAP SE 73

12.10. NetApp 73

12.11. highradius 73

12.12. Intel Corporation 73

12.13. AWS 73

12.14. Oracle Corporation 73

12.15. Others 73

List of Figures

FIG NO. 1. UK Artificial Intelligence in Finance Market Revenue, 2018 – 2032 (USD Million) 22

FIG NO. 2. Porter’s Five Forces Analysis for UK Artificial Intelligence in Finance Market 29

FIG NO. 3. Value Chain Analysis for UK Artificial Intelligence in Finance Market 30

FIG NO. 4. Company Share Analysis, 2023 32

FIG NO. 5. Company Share Analysis, 2023 32

FIG NO. 6. Company Share Analysis, 2023 33

FIG NO. 7. Artificial Intelligence in Finance Market – Company Revenue Market Share, 2023 34

FIG NO. 8. UK Artificial Intelligence in Finance Market Revenue Share, By Component, 2023 & 2032 40

FIG NO. 9. Market Attractiveness Analysis, By Component 41

FIG NO. 10. Incremental Revenue Growth Opportunity by Component, 2024 – 2032 41

FIG NO. 11. Artificial Intelligence in Finance Market Revenue, By Component, 2018, 2023, 2027 & 2032 42

FIG NO. 12. UK Artificial Intelligence in Finance Market for Solution, Revenue (USD Million) 2018 – 2032 43

FIG NO. 13. UK Artificial Intelligence in Finance Market for Services, Revenue (USD Million) 2018 – 2032 44

FIG NO. 14. Artificial Intelligence in Finance Market Revenue Share, By Deployment Mode, 2023 & 2032 46

FIG NO. 15. Market Attractiveness Analysis, By Deployment Mode 47

FIG NO. 16. Incremental Revenue Growth Opportunity by Deployment Mode, 2024 – 2032 47

FIG NO. 17. Artificial Intelligence in Finance Market Revenue, By Deployment Mode, 2018, 2023, 2027 & 2032 48

FIG NO. 18. UK Artificial Intelligence in Finance Market for On-premise, Revenue (USD Million) 2018 – 2032 49

FIG NO. 19. UK Artificial Intelligence in Finance Market for Cloud, Revenue (USD Million) 2018 – 2032 50

FIG NO. 20. Artificial Intelligence in Finance Market Revenue Share, By Technology, 2023 & 2032 52

FIG NO. 21. Market Attractiveness Analysis, By Technology 53

FIG NO. 22. Incremental Revenue Growth Opportunity by Technology, 2024 – 2032 53

FIG NO. 23. Artificial Intelligence in Finance Market Revenue, By Technology, 2018, 2023, 2027 & 2032 54

FIG NO. 24. UK Artificial Intelligence in Finance Market for Generative AI, Revenue (USD Million) 2018 – 2032 55

FIG NO. 25. UK Artificial Intelligence in Finance Market for Other AI Technologies, Revenue (USD Million) 2018 – 2032 56

FIG NO. 26. Artificial Intelligence in Finance Market Revenue Share, By Application, 2023 & 2032 58

FIG NO. 27. Market Attractiveness Analysis, By Application 59

FIG NO. 28. Incremental Revenue Growth Opportunity by Application, 2024 – 2032 59

FIG NO. 29. Artificial Intelligence in Finance Market Revenue, By Application, 2018, 2023, 2027 & 2032 60

FIG NO. 30. UK Artificial Intelligence in Finance Market for Virtual Assistant (Chatbots), Revenue (USD Million) 2018 – 2032 61

FIG NO. 31. UK Artificial Intelligence in Finance Market for Business Analytics and Reporting, Revenue (USD Million) 2018 – 2032 62

FIG NO. 32. UK Artificial Intelligence in Finance Market for Fraud Detection, Revenue (USD Million) 2018 – 2032 63

FIG NO. 33. UK Artificial Intelligence in Finance Market for Quantitative and Asset Management, Revenue (USD Million) 2018 – 2032 64

FIG NO. 34. UK Artificial Intelligence in Finance Market for Others, Revenue (USD Million) 2018 – 2032 65

FIG NO. 35. UK Artificial Intelligence in Finance Market Revenue, 2018 – 2032 (USD Million) 66

List of Tables

TABLE NO. 1. : UK Artificial Intelligence in Finance Market: Snapshot 21

TABLE NO. 2. : Drivers for the Artificial Intelligence in Finance Market: Impact Analysis 25

TABLE NO. 3. : Restraints for the Artificial Intelligence in Finance Market: Impact Analysis 27

TABLE NO. 4. : UK Artificial Intelligence in Finance Market Revenue, By Component, 2018 – 2023 (USD Million) 67

TABLE NO. 5. : UK Artificial Intelligence in Finance Market Revenue, By Component, 2024 – 2032 (USD Million) 67

TABLE NO. 6. : UK Artificial Intelligence in Finance Market Revenue, By Deployment Mode, 2018 – 2023 (USD Million) 68

TABLE NO. 7. : UK Artificial Intelligence in Finance Market Revenue, By Deployment Mode, 2024 – 2032 (USD Million) 68

TABLE NO. 8. : UK Artificial Intelligence in Finance Market Revenue, By Technology, 2018 – 2023 (USD Million) 69

TABLE NO. 9. : UK Artificial Intelligence in Finance Market Revenue, By Technology, 2024 – 2032 (USD Million) 69

TABLE NO. 10. : UK Artificial Intelligence in Finance Market Revenue, By Application, 2018 – 2023 (USD Million) 70

TABLE NO. 11. : UK Artificial Intelligence in Finance Market Revenue, By Application, 2024 – 2032 (USD Million) 70

Frequently Asked Questions

What is the market size of the UK AI in Finance market in 2023 and 2032?

The UK AI in Finance market is valued at USD 1,245 million in 2023 and is projected to reach USD 11,569 million by 2032, with a CAGR of 28.1% from 2024 to 2032.

What are the key drivers for the growth of AI in the UK finance market?

Key drivers include the rising demand for enhanced fraud prevention, the need for personalized financial services, and the growing volume of financial data, all fueling AI adoption in the sector.

How is AI improving fraud detection in the financial services industry?

AI enhances fraud detection by analyzing large datasets in real-time to identify unusual patterns or activities, significantly reducing the risk of fraudulent transactions.

What are some current trends in the UK AI in Finance market?

Current trends include the integration of AI with blockchain for security, the rise of AI-powered robo-advisors, and advancements in machine learning for predictive analytics and risk management.

Which financial institutions are leading the AI adoption in the UK?

Major financial institutions such as HSBC, Barclays, and Lloyds Banking Group are leading the adoption of AI, driving innovation in fraud prevention, algorithmic trading, and customer experience optimization.

Purchase Options

Single User

$2699

Multi User

$3699

Custom Report

$5699

Let us help you

MIKE, North America

+1 304 308 1216

KEITH PHILLIPS, Europe

+44 7809 866 263

EMAIL US

Request Sample

Report delivery within 24 to 48 hours

– Other Info –

–What people say?-

I am very impressed with the information in this report. The author clearly did their research when they came up with this product and it has already given me a lot of ideas.

Jana Schmidt
CEDAR CX Technologies

– Connect with us –