Generative AI in Banking and Finance Market By Technology (Machine Learning, Deep Learning, Natural Language Processing, Computer Vision), By Application (Fraud Detection, Algorithmic Trading, Risk Management, Customer Service Automation, Credit Scoring and Underwriting, Wealth Management, Regulatory Compliance), By End-User (Banks, Financial Institutions, Investment Firms, Insurance Companies, Payment Providers, Wealth Management Firms), By Deployment Mode (Cloud-based, On-premise), and By Region; Global Insights & Forecast (2023 – 2030)

As per Intent Market Research, the Generative AI in Banking and Finance Market was valued at USD 1.8 billion in 2024-e and will surpass USD 13.1 billion by 2030; growing at a CAGR of 39.7% during 2025 - 2030.

The generative AI market in banking and finance is growing rapidly as financial institutions increasingly adopt advanced AI technologies to streamline operations, improve customer service, and manage risks. Generative AI refers to the use of machine learning and deep learning algorithms to generate new data, simulate scenarios, and predict outcomes, thus enhancing decision-making in various financial applications. The technology is being integrated into a wide array of applications, such as fraud detection, risk management, customer service automation, and algorithmic trading, leading to significant transformations in the way financial services operate.

Machine Learning Segment Is Largest Owing to Its Versatility in Financial Applications

Machine learning (ML) is the largest technology segment in the generative AI market for banking and finance, primarily due to its flexibility and the wide range of applications it supports. ML algorithms are designed to learn from historical data, making them ideal for applications such as fraud detection, credit scoring, and risk management. By analyzing vast amounts of transaction data, ML models can identify patterns and predict future outcomes with high accuracy, which is crucial for financial institutions looking to mitigate risks and improve customer experience.

The growing adoption of machine learning is further fueled by its ability to automate complex tasks like algorithmic trading and portfolio management. The predictive capabilities of ML models provide a competitive advantage to financial institutions, enabling them to optimize their operations and deliver personalized services to clients. As machine learning technologies continue to evolve, their role in reshaping the financial sector will only increase, making it a key driver of growth in the generative AI market.

Fraud Detection Application Is Fastest Growing Due to Increasing Cybersecurity Threats

Among the various applications of generative AI in banking and finance, fraud detection is the fastest growing. With the rise in cyber-attacks and financial fraud, financial institutions are increasingly turning to AI-powered solutions to protect sensitive customer data and assets. Generative AI models can analyze transaction patterns in real-time and detect anomalies that may indicate fraudulent activity. By leveraging advanced machine learning algorithms, these systems can continuously improve their accuracy in identifying and preventing fraud, thereby reducing financial losses and enhancing security.

The growing reliance on digital banking and online transactions has led to an increase in fraudulent activities, making the need for robust fraud detection systems even more critical. Banks and financial institutions are investing heavily in AI-driven fraud detection solutions, which is driving the rapid growth of this application. As AI models become more sophisticated, their ability to detect new and emerging types of fraud will continue to expand, further boosting the demand for these solutions.

Banks End-User Segment Is Largest Owing to High Adoption of AI Technologies

Banks represent the largest end-user segment in the generative AI market for banking and finance. Financial institutions, particularly banks, are at the forefront of adopting generative AI to enhance their operations, improve customer experience, and optimize decision-making processes. Banks use AI for various applications, including fraud detection, risk management, and customer service automation. The ability of generative AI to analyze large amounts of data in real-time enables banks to make more informed decisions, reduce operational costs, and improve customer satisfaction.

The increasing demand for personalized banking experiences and the growing complexity of financial markets are driving banks to adopt AI technologies at an accelerated rate. As banks continue to invest in AI, they are positioning themselves as leaders in the evolving digital landscape of the finance sector. The ability to automate back-end processes, streamline loan approvals, and optimize investment strategies gives banks a significant competitive edge in an increasingly crowded market.

Cloud-Based Deployment Mode Is Dominating Owing to Scalability and Flexibility

Cloud-based deployment is the dominant mode of deployment for generative AI solutions in banking and finance. The cloud offers significant advantages over on-premise solutions, including scalability, cost-efficiency, and flexibility. Financial institutions are increasingly adopting cloud-based AI platforms to meet the growing demand for data processing and analytics in real-time. The cloud also allows banks to access advanced AI tools without the need for significant upfront investment in infrastructure, making it an attractive option for both large institutions and smaller financial firms.

Cloud-based solutions also facilitate better collaboration between financial institutions, enabling them to share insights and data more effectively. This is particularly important for use cases such as fraud detection, where collaboration between various stakeholders can help improve the accuracy of AI models. As the cloud continues to evolve and offer more advanced capabilities, its adoption in the generative AI market will only increase, further driving growth in the sector.

North America Region Is Largest Owing to Strong Financial Sector and AI Adoption

North America is the largest region for generative AI in banking and finance, driven by the strong financial sector and high levels of AI adoption. The United States, in particular, is home to many of the world’s leading financial institutions, which are investing heavily in AI technologies to stay competitive. The region's financial services sector is a major driver of AI innovation, with banks and fintech companies utilizing AI for a range of applications, including fraud detection, risk management, and customer service automation.

The high level of digital transformation in North America's financial services industry, coupled with the increasing demand for personalized financial products and services, is propelling the adoption of generative AI. Additionally, the region's regulatory framework and strong technological infrastructure make it an ideal environment for AI-driven innovations in the banking and finance sector. As AI technologies continue to mature, North America is expected to remain the leading region for generative AI in banking and finance.

Competitive Landscape and Leading Companies

The competitive landscape in the generative AI market for banking and finance is characterized by the presence of leading technology companies, fintech startups, and financial institutions. Major players in the market include IBM, Microsoft, Amazon Web Services (AWS), Google, and Accenture, all of which are leveraging their AI capabilities to offer innovative solutions for fraud detection, risk management, and customer service automation. These companies are focusing on expanding their AI portfolios through strategic acquisitions, partnerships, and investments in R&D to stay ahead of the competition.

In addition to these global tech giants, fintech startups are also playing a crucial role in driving innovation in the generative AI space. These startups are often at the forefront of developing new AI solutions tailored to the unique needs of the banking and finance industry. As the market continues to evolve, financial institutions are likely to form more partnerships with AI technology providers to accelerate the adoption of AI-driven solutions across the sector. The competition is expected to intensify as the demand for AI solutions grows and new technologies emerge.

Recent Developments:

• Microsoft and Goldman Sachs have partnered to integrate AI-based trading solutions for advanced algorithmic trading, bringing new efficiencies to investment strategies.
• IBM has announced a new AI-driven risk management platform designed to help banks and financial firms detect financial risks in real-time with predictive capabilities.
• AWS has unveiled new AI tools aimed at enhancing fraud detection and compliance in the banking sector, allowing firms to implement real-time risk management practices.
• Cognizant has acquired a leading data analytics firm to bolster its generative AI capabilities in the finance sector, offering next-gen fraud detection and predictive analytics solutions.
• NVIDIA has partnered with several major financial institutions to deploy AI-powered wealth management solutions, utilizing generative models to personalize investment strategies.

List of Leading Companies:

• IBM
• Google
• Microsoft
• NVIDIA Corporation
• Amazon Web Services (AWS)
• Accenture
• Infosys
• Cognizant
• Salesforce
• SAS Institute
• KPMG
• Capgemini
• Oracle Corporation
• Palantir Technologies
• DataRobot

Report Scope: