Generative Artificial Intelligence (AI) in Banking and Finance Market Report, By Application (Risk Assessment and Management, Fraud Detection and Prevention, Customer Service, Automation, Personalized Financial Advice); Deployment Model (On-Premises, Cloud-Based); Technology (NLP, Machine Learning (ML), Predictive Analytics, Deep Learning); and Regions 2024-2032

Market Overview:

"The global generative Artificial Intelligence (AI) in banking and finance market size reached US$ 921.1 million in 2023. Looking forward, Reports and Insights expects the market to reach US$ 10,537.3 million in 2032, exhibiting a growth rate (CAGR) 31.1% of during 2024-2032."

Report Attributes	Details
Base Year	2023
Forecast Years	2024-2032
Historical Years	2021-2023
Market Growth Rate (2024-2032)	31.1%

Generative Artificial Intelligence (AI) or GenAI continues to emerge as a transformative force in the banking and finance sector and is expected to gain substantial traction over the next year. Generative AI is being used to leverage Machine Learning (ML) algorithms, along with capabilities of Large Language Models (LLM), to generate valuable insights, automates processes, enhances decision-making, and offers others advantages in this sector. Advanced applications include risk assessment, fraud detection, customer service automation, and personalized financial advice, among a number of others.

The benefits of Generative AI in banking and finance can be numerous, and the technology can improve operational efficiency by automating routine tasks, reduce potential errors, and enhance compliance through predictive analytics. Also, GenAI enables institutions to create personalized customer experiences and tailor financial products based on individual preferences to drive convenience and increase brand loyalty.

Real-time data analysis allows for quicker response to market changes, while predictive modeling aids in identifying potential risks and opportunities. The technology is also capable of enhancing cybersecurity measures and safeguarding sensitive financial information. Key trends in the Generative AI in banking and finance market include the rise of Explainable AI for regulatory compliance, increased focus on ethical AI practices, and integration of Natural Language Processing (NLP) for better customer interactions.

Recent developments and trends highlight banks and financial institutions exploring potential integration of Generative AI in credit scoring, algorithmic trading, and portfolio management in financial institutions. The market is also witnessing collaborations between traditional financial institutions and fintech companies to capitalize on the full potential of Generative AI, marking a significant evolution in the sector's technological landscape. Some of the leading banks to have adopted generative AI as of 2023 included Morgan Stanley, JPMorgan Chase, NatWest Group, Goldman Sachs, OCBC (Oversea-Chinese Banking Corporation), and Hokuhoku Financial Group, and Citigroup has also been examining potential of around 350 possible apps for generative AI later in the year. Some of the top Generative AI tools currently in use in the banking sector include DataRobot, H2O.ai, KAI, Symphony AyasdiAI, Numenta, Kount, ZestAi, Personetics, ComplyAdvantage, and IBM Watson.

Generative AI in Banking and Finance Market Trends and Drivers

Traction of Generative AI in the banking and finance sector is being driven by factors such as increased need for enhanced efficiency and automation in financial processes, benefits and advantages offered by automation of routine tasks, risk assessment, and fraud detection, and focus on streamlining operations and reducing overall operational costs. Banks are proceeding cautiously at the moment with some integrating GenAI in internal processes, but a number of banks that have been first-movers are reporting profits and favorable outcomes from AI integration. Some major banks are currently in process of training AI in pattern-spotting, process-automating such as rooting out credit-card fraud, guiding client teams, green-lighting lending, and aiding in managing some back-office functions. Banks are also in process of collaborating with FinTech companies, hiring AI experts, engineers, data scientists, and software developers, or piloting opportunities with GenAI, while cutting back in other areas to align and develop models to suit specifics, and this is the focal point across 2024.

However, the need for more efficient, safe, and advanced solutions continues to drive urgent need for advanced analytical tools, to manage increasing complexity of financial data and vast data volumes being generated in the banking and finance sector globally. Generative AI is adept in processing vast datasets, analyzing real-time customer interactions and transactions, and providing valuable insights that aid in making informed decisions. These among other capabilities, and the benefits and advantages offered are prompting positive considerations to adopt GenAI in the sector much sooner.

In addition, banking and finance companies have increased focus on providing more personalized customer experiences to retain and expand client bases. Integration of AI-powered chatbots in banking services, driven by GenAI, is being increasingly adopted and aiding major banks in expanding convenience and banking accessibility with 24X7 support to enhance customer experience.

Generative AI enables banks and financial institutions to tailor products and services based on individual preferences and provide personalized financial guidance and recommendations, among other services, and these have been serving to significantly improve customer satisfaction and loyalty.

Moreover, rising focus on regulatory compliance and risk management is supporting integration and adoption of GenAI in banking and finance. GenAI offers reduced risk of compliance violations by monitoring and analyzing legal guidelines, and automating and ensuring continuous compliance. Generative AI supports scenario simulation and analysis of risk factors, enabling a proactive approach to risk management. Through the generation of synthetic data that represents various risk scenarios, financial institutions can identify correlations, dependencies, and emerging risks, and this process enhances the overall effectiveness of risk management strategies. The technology is also capable of strengthening Domain Name System (DNS) security by analyzing and recognizing patterns and boosting resilience of FinTech systems against cyber threats.

Collaborative efforts between traditional financial institutions and fintech companies and partners to harness the transformative potential of Generative AI is also contributing significantly to potentially wider adoption, encouraging innovation, and shaping the future landscape in banking and finance.

Generative AI in Banking and Finance Market Restraining Factors

Inherent risks accompany the deployment of AI technology in the financial sector, encompassing embedded bias, security and data privacy issues, integration complexity and cost factors, opacity in outcomes, robustness in performance, dependence on outdated data and potential hazards, and the possibility of introducing novel sources and channels for the transmission of systemic risks.

Among these, concerns around data security and privacy is a major factor restraining implementation, as financial institutions handle sensitive information, making them cautious about integrating technologies that could cause losses, legal issues, or other negative repercussions in the short or long term. The cybersecurity aspect of GenAI, including its vulnerability to data manipulation attacks, is also a major cause for concern, especially considering potential of GenAI to produce false and malicious content. The dissemination of such content has the potential to induce public panic, leading to adverse consequences such as bank runs in the context of financial services.

Another key factor is complexity and cost associated with integrating, training, and calibrating Generative AI solutions into existing infrastructure, particularly for smaller institutions. Also, the lack of regulatory clarity and standards for AI in finance poses challenges, leading to hesitancy in widespread adoption. Regulators have also warned that rapid adoption of AI could create new risks for financial systems if the technology is not properly supervised.

Then there exists a potential for GenAI to introduce solvency and liquidity risks, especially if machine-driven trades adopt higher-credit or market risks with the aim of maximizing profits. Furthermore, financial institutions utilizing generative AI for operational purposes need to remain vigilant about the risk of relying on outdated information to prevent potentially severe consequences for both customers and the overall economy, and this is another key factor restraining adoption.

Generative AI in Banking and Finance Market Opportunities

Leading players in the market need to address security and data related concerns, and alleviate fears and associated risks of adopting GenAI. Companies can capitalize on the evolving landscape and cater to growing demand for tailored, AI-driven financial products, and develop and offer innovative solutions that cater to the individual needs and preferences of customers. Also, the surge in Regulatory Technology (RegTech) provides avenues for GenAI applications to enhance compliance and risk management processes, ensuring adherence to complex regulatory frameworks. Rapidly increasing traction of Decentralized Finance (DeFi) presents opportunity for companies to integrate GenAI into blockchain-based financial services, optimizing decentralized lending, and enhancing smart contract functionality.

In addition, increasing adoption of cloud-based services in the finance sector creates opportunities for Generative AI providers to offer scalable and flexible solutions. Traditional financial institutions and fintech disruptors can enter into strategic collaborations to innovate and facilitate seamless integration of GenAI technologies into established financial ecosystems.

Generative AI in Banking and Finance Market Segmentation

By Application

• Risk Assessment and Management
• Fraud Detection and Prevention
• Customer Service Automation
• Personalized Financial Advice

The personalized financial advice segment is expected to maintain revenue share dominance among the other application segments over the forecast period. This can be attributed to increasing demand for customized financial services and banking and financial institutions focusing on customer-centric approaches to enhance customer engagement and satisfaction through tailored advisory services.

By Deployment Model

• On-Premises
• Cloud-Based

The cloud-based segment is expected to account for largest revenue share due to high adoption of cloud solutions among financial institutions for scalability, flexibility, and cost-effectiveness benefits. Cloud-based deployment facilitates seamless integration, enables real-time data processing, and supports collaborative efforts, making it the preferred choice for banks and financial entities seeking efficient and agile Generative AI implementations.

By End-User

• Banks
• Insurance Companies
• Investment Firms
• Fintech Companies

The banks segment is projected to dominate other end-user segments in terms of revenue share over the forecast period. This is attributed to the integral role of banks in financial ecosystems, increasing focus on leveraging AI for efficiency gains, enhancing risk management, personalizing customer services, and improving operational efficiency along with steady adoption of other transformative technologies in the sector.

By Technology

• Natural Language Processing (NLP)
• Machine Learning (ML)
• Predictive Analytics
• Deep Learning

The Machine Learning (ML) segment is expected to maintain dominance in terms of revenue share owing to the pivotal role the technology plays in processing vast financial datasets, enabling predictive analytics, and facilitating informed decision-making. Financial institutions are becoming increasingly reliant on data-driven insights, and the adaptability and proven success of ML in risk assessment and fraud detection is expected to continue to drive integration over the forecast period.

By Region

North America

• United States
• Canada

Europe

• Germany
• United Kingdom
• France
• Italy
• Spain
• Russia
• Poland
• Benelux
• Nordic
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• South Korea
• ASEAN
• Australia & New Zealand
• Rest of Asia Pacific

Latin America

• Brazil
• Mexico
• Argentina

Middle East & Africa

• Saudi Arabia
• South Africa
• United Arab Emirates
• Israel
• Rest of MEA

The global Generative AI in banking and finance market is divided into five key regions: North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. North America currently maintains dominance as the largest generative AI for FinTech market and accounting for a substantially large share in the global revenue. This is attributed to presence of major financial institutions and advanced technology adoption, number of startups and key players offering cutting-edge generative AI services, and major focus being placed in the United States in addressing challenges related to data collection, data security, fairness and transparency, accuracy and reliability, and accountability adding to the steadily growing traction of GenAI in banking and finance sector.

Asia Pacific is expected to remain a lucrative market with steadily expanding avenues and opportunities for players in the market owing to a growing fintech landscape and the digitization trend in countries such as India and China. GenAI is being adopted in the financial services industry in Europe, and expenditure on deployment is expected to register a sizable increase through 2024. While industry leaders foresee AI as a catalyst for enhancing productivity, need for more strategic and long-term initiatives related to training and upskilling due to limited comprehension of generative AI among existing workforce, uncertainties regarding future regulations, and ethical considerations associated with its implementation are being emphasized upon.

Leading Generative AI in Banking and Finance Solutions Providers & Competitive Landscape:

The landscape in the global generative AI in banking and finance market is highly competitive, and leading companies are employing strategic measures to broaden their consumer bases. Key strategies include investing in research and development to enhance AI capabilities, developing collaborations with fintech partners to develop and leverage innovative solutions, and prioritizing cybersecurity measures to address data security concerns. Also, companies are focusing on providing comprehensive training and support services to facilitate seamless integration of Generative AI solutions, and ensuring clients can maximize the benefits of these advanced technologies while maintaining a competitive edge in the dynamic banking and financial services sector.

These companies include:

• Amazon Web Services Inc.
• Microsoft Corporation
• Cisco Systems Inc.
• SAP SE
• BigML Inc.
• Fair Isaac Corporation
• IBM Corporation
• Google LLC
• NVIDIA
• Accenture
• Oracle

Recent Development:

• November 2023: NatWest and IBM announced updates to the bank's virtual assistant, Cora, by leveraging IBM's enterprise grade AI and data platform, watsonx. These enhancements leverage generative AI to expand customer access to a broader spectrum of information through conversational interactions. NatWest stands as one of the pioneering banks in the UK to implement generative AI within a virtual assistant framework, ensuring a secure, user-friendly, and inclusive experience for customers engaging with its digital services.
• September 2023: Fujitsu, in collaboration with Hokuriku Bank, Ltd. and Hokkaido Bank, Ltd., both of which are subsidiaries of Hokuhoku Financial Group, Inc., announced details regarding joint trials to investigate the application of generative AI in banking operations. The trial entails use of generative AI technology, including an AI module for conversational AI provided by Fujitsu through Fujitsu Kozuchi, which is the company’s AI platform, and the aim is to integrate generative AI into the banking operations of Hokuriku Bank and Hokkaido Bank, addressing internal inquiries, generating and verifying various business documents, and developing programs.

Generative AI in Banking and Finance Market Research Scope

Report Metric	Report Details
Market size available for the years	2021-2023
Base Year	2023
Forecast Period	2024-2032
Compound Annual Growth Rate (CAGR)	31.1%
Segment covered	Application, Deployment Model, End-User, Technology
Regions Covered	North America:  The U.S. & Canada Latin America: Brazil, Mexico, Argentina, & Rest of Latin America Asia Pacific: China, India, Japan, Australia & New Zealand, ASEAN, & Rest of Asia Pacific Europe: Germany, The U.K., France, Spain, Italy, Russia, Poland, BENELUX, NORDIC, & Rest of Europe The Middle East & Africa:  Saudi Arabia, United Arab Emirates, South Africa, Egypt, Israel, and Rest of MEA
Fastest Growing Country in Europe	UK
Largest Market	North America
Key Players	Amazon Web Services Inc., Cisco Systems Inc., Microsoft Corporation, SAP SE, BigML Inc., Fair Isaac Corporation, IBM Corporation, Google LLC, NVIDIA, Accenture, Oracle