Generative AI in Capital Market By Deployment Mode (Cloud-Based and On-Premises), By Application (Portfolio Optimization, Risk Assessment, and Others), By End-User (Portfolio Managers, Traders, Financial Analysts, Institutional Investors, and Others), By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Generative AI in Capital Market size is expected to be worth around USD 1,955 Mn by 2032 from USD 240 Mn in 2022, growing at a CAGR of 24% during the forecast period from 2023 to 2032.

As financial institutions see its potential, generative AI is attracting interest on a global scale in the capital market industry. Generative AI is used in many fields, including market research, algorithmic trading, risk management, and fraud detection, to create fake data, simulate market scenarios, and improve decision-making processes.  Financial institutions are exploring collaborations with tech firms, academic centers, and startups to create and implement generative AI solutions.

Organizations must navigate data privacy and ethical issues while implementing generative AI to maintain compliance with laws and safeguard sensitive information. As the landscape of generative AI in the capital market continues to evolve rapidly, staying informed through industry reports and news articles is crucial for understanding the latest advancements and developments.

Driving factors

Financial organizations and investors can make better judgments with the help of generative AI models. These models can process enormous volumes of data, spot trends, and produce insights to help investment choices, risk management, and portfolio optimization by utilizing machine learning techniques. This may result in more accurate forecasts and better investing results. Numerous processes, including algorithmic trading, portfolio rebalancing, and risk management, can be automated in the capital market. Automating these processes makes it possible for AI-powered systems to operate more swiftly and efficiently, reducing human error and perhaps improving market liquidity and stability.

Financial statements, market information, news stories, and sentiment from social media are just a few of the many types of data that the capital market produces. Being able to access such enormous and varied data sets gives generative AI models a rich supply from which to learn complicated patterns and generate predictions based on past trends. With a wide variety of financial products, trading methods, and market dynamics, the capital market is getting more and more complicated. By creating simulated scenarios, doing risk analyses, and optimizing investment strategies, generative AI algorithms have the capacity to analyze and comprehend this complexity in a way that traditional models might find challenging.

Restraining Factors

Due to the enormous amount of sensitive financial data involved, the stock market is a top target for cyberattacks. Because generative AI systems rely on private financial data and algorithms, implementing them increases cybersecurity threats. An organization may suffer significant financial and reputational losses as a result of unauthorized access, data breaches, and malicious attacks on generative AI models.

The deployment of generative AI may encounter opposition and skepticism from market participants because trust is so important in the capital markets. There may be worries about totally relying on AI systems for crucial investment decisions because traditional financial models frequently rely on human expertise and intuition. For generative AI models to be widely accepted, it will be crucial to establish trust in them and show that they are reliable and bring value.

Covid-19 Impact on Generative AI in Capital Market

The pandemic brought about unheard-of market instability and volatility levels, resulting in sharp changes in stock prices, currency exchange rates, and commodities prices. Generative AI models significantly rely on past data to create predictions and produce insights. However, the unusual market conditions and unheard-of events that occurred during the pandemic would have put a strain on the precision and efficacy of these models because they might not have had enough training data to handle such uncommon circumstances.

Investor behavior has changed as a result of the pandemic. Many investors have moved their attention to safe-haven investments and risk management techniques to lessen the effects of market volatility. To account for shifting market dynamics and investor sentiment, generative AI models are required to adjust to these changes in investor behavior and update their algorithms and training data.

The pandemic has spurred digital transformation and remote work adoption throughout businesses, including the banking industry. The adoption of generative AI in the capital market was made possible by this digital transformation. Financial organizations can use generative AI models for decision-making and risk management even with remote data access, cloud-based computing, and collaboration tools, even with people working from home.

The pandemic made clear the need for thorough risk management and assessment in the stock market. During this time, generative AI models were quite useful in assisting financial institutions with risk assessment and management. These models' capacity to examine huge datasets and simulate multiple scenarios made it easier to spot possible problems and put the proper risk-reduction measures in place.

By Deployment Mode Analysis

The On-Premises Segment Accounted for the Largest Revenue Share in Generative AI in Capital Market in 2022.

Based on deployment mode, the market is segmented into Cloud-Based and On-Premises. Among these deployment modes, the On-Premises is expected to be the most lucrative in the global Generative AI in Capital Market, with the largest revenue share of 59%. On-premises deployment gives institutions more control over data security and privacy because the data is kept in their own facilities. Financial institutions that handle sensitive financial data and must adhere to regulatory regulations may find this essential.

Because the institution has direct control over the generative AI system's hardware, software, and configurations, on-premises implementation enables greater customization and flexibility. This can be crucial when modifying the system to meet certain business requirements or integrating it with existing infrastructure. The financial institution must put aside funds for infrastructure setup, upkeep, and upgrades to implement on-premises. Investing in hardware, software licenses, qualified employees, and continuing IT support are all included in this. It might need a lot of resources and have a high initial investment.

The Cloud-Based Segment is Fastest Growing Segment in Generative AI in Capital Market.

The Cloud-Based segment is projected as the fastest-growing segment in Generative AI in Capital Market from 2023 to 2032. Thanks to the scalability and flexibility of cloud-based deployment, financial organizations can scale up or down depending on their computational demands. The cloud architecture can handle Large data volumes, offering elastic resources to meet demand spikes.

Financial organizations often pay for their resources via a pay-as-you-go pricing model for cloud-based deployment. When compared to on-premises deployment, this can be more affordable because it eliminates the need for initial hardware investments and lowers ongoing maintenance and upgrade costs.

By Application Analysis

The Portfolio Optimization Holds the Significant Share Application Segment in Generative AI in Capital Market.

Based on application, the market is divided into Portfolio Optimization, Risk Assessment, Financial, Market Data Generation, Market Forecasting, and Other Applications. Among these, the Portfolio Optimization segment is dominant in the format segment in Generative AI in Capital Market, with a market share of 42%. Advanced machine learning techniques are used by generative AI for portfolio optimization to analyze enormous volumes of historical and real-time market data, spot trends, and produce the best possible investment plans.

In a complicated and volatile market environment, building and rebalancing portfolios present a number of difficult issues. Asset allocation is a crucial component of portfolio optimization, where generative AI models can evaluate the risk and return characteristics of multiple asset classes and establish the ideal distribution of funds among various assets. These models can calculate the ideal asset weightings that maximize returns while controlling risk exposure by looking at historical market patterns, correlations, and risk variables.

Risk Assessment is Identified as Fastest Growing Application Segment in Projected Period.

Risk Assessment is also an important format segment in Generative AI in Capital Market, and it is expected to grow faster in the application segment in Generative AI in Capital Market. In the financial sector, generative AI presents both an opportunity and threat that should be carefully considered. Potential market manipulation is a crucial area to examine in terms of risk assessment.

Generative AI algorithms may be used to generate false news, rumors, or market projections to artificially impact stock prices and create market distortions and unfair advantages for some businesses. Data privacy and security is another crucial area. The application of generative AI raises worries about the possibility of unauthorized access, data breaches, or information misuse because capital market data is extremely sensitive and proprietary.

By End-User Analysis

The Financial Institutions Hold the Significant Share of the End-User Segment in Generative AI in Capital Market.

Based on end-user, the market is divided into Financial Institutions, Financial Analysts, Institutional Investors, and Other End-Users. Financial Institutions dominate the Generative AI in Capital Market with a 51% market share. Generative AI can be used by banks, brokerage houses, and other financial organizations for a variety of reasons. It can help with risk assessments, fraud detection, automating trade operations, and improving consumer interactions. Generative AI facilitates operations, improves security, and provides clients with individualized services.

Key Market Segments

Based on Deployment Mode

• Cloud-Based
• On-Premises

Based on Application

• Portfolio Optimization
• Risk Assessment
• Financial Market Data Generation
• Market Forecasting
• Other Applications

Based on End-User

• Financial Institutions
• Financial Analysts
• Institutional Investors
• Other End-Users

Growth Opportunity

Generative AI provides significant opportunities for expansion in the capital market. The improvement of investment methods and decision-making procedures is one important topic. To help investors make wise decisions, generative AI systems can analyze enormous volumes of financial data, spot trends, and provide predictive models. Additionally, this technology can make it possible to develop complex trading algorithms that enhance portfolio management and adjust to shifting market conditions.

Risk assessment, compliance monitoring, and fraud detection are just a few jobs that generative AI can automate and streamline within the capital market, improving efficiency and reducing costs. By utilizing generative AI, investors can acquire a competitive edge, enhance investment results, and open up new doors for innovation in the capital market's dynamic and complicated environment.

Latest Trends

The use of generative adversarial networks (GANs) is one of the newest developments in generative AI for the financial industry. The generator and discriminator neural networks in GANs collaborate to produce realistic data samples. GANs are used in the capital market to produce fictitious financial data, including stock prices, market scenarios, and economic indicators. This artificial data can be utilized for a variety of tasks, such as stress tests, market simulations, and backtesting trading methods. GANs assist in overcoming restrictions linked to data availability and historical market data by producing a variety of real data.

This development creates fresh opportunities for boosting risk management, developing decision-making procedures, and investigating alternate capital market situations. Additionally, GANs can be integrated with other AI methods, such as reinforcement learning, to produce trading algorithms that are more complex and adaptive. A promising development that has the potential to completely alter how financial data is produced, examined, and used for investing reasons is the use of GANs in the capital market.

Regional Analysis

North America Accounted for the Largest Revenue Share in Generative AI in Capital Market in 2022.

North America is estimated to be the most lucrative market in the global Generative AI in Capital Market, with the largest market share of 42%; generative AI has emerged as a game-changing technology with a considerable impact in North America. The region has a thriving financial sector, cutting-edge technology infrastructure, and innovative culture, all of which have contributed to the development and use of generative AI in capital markets.

The booming startup environment in North America and easy access to venture financing further drive generative AI's expansion in the capital market. Numerous technology businesses with a focus on creating cutting-edge generative AI solutions specially designed for the financial sector may be found in the area. This dynamic ecosystem encourages cooperation, competitiveness, and ongoing innovation, producing various ground-breaking technologies and inventive uses.

Key Regions and Countries

North America

• US
• Canada
• Mexico

Western Europe

• Germany
• France
• The UK
• Spain
• Italy
• Portugal
• Ireland
• Austria
• Switzerland
• Benelux
• Nordic
• Rest of Western Europe

Eastern Europe

• Russia
• Poland
• The Czech Republic
• Greece
• Rest of Eastern Europe

APAC

• China
• Japan
• South Korea
• India
• Australia & New Zealand
• Indonesia
• Malaysia
• Philippines
• Singapore
• Thailand
• Vietnam
• Rest of APAC

Latin America

• Brazil
• Colombia
• Chile
• Argentina
• Costa Rica
• Rest of Latin America

Middle East & Africa

• Algeria
• Egypt
• Israel
• Kuwait
• Nigeria
• Saudi Arabia
• South Africa
• Turkey
• United Arab Emirates
• Rest of MEA

Market Share & Key Players Analysis

The generative AI market for the capital market is characterized by various players, including industry giants, technology leaders, and agile startups. The competition among these key players underscores the immense potential and demand for generative AI solutions in transforming the capital market and shaping the future of finance.

Top Key Players in Generative AI in Capital Market

• IBM
• NVIDIA Corporation
• Goldman Sachs
• JPMorgan Chase
• BlackRock
• Citadel
• Bloomberg
• DataRobot
• Other Key Players

Recent Development

• In March 2023, NVIDIA A100 Tensor Core GPUs running on Supermicro servers captured leading results for inference in the latest STAC-ML Markets benchmark, a key technology performance gauge for the financial services industry.

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