The global capital markets are set to face several challenges in 2024 that will persist into next year caused by a slowing international economy and multiple disruptive forces. One of these issues is the exponential growth in technological development, which will benefit market participants significantly while posing challenges as they work to optimize and implement new tech. Artificial intelligence in capital markets, or AI in capital markets, is one of the most prominent of these technologies.

The best way to protect your company from disruptive forces in any industry, particularly in international capital markets, is to ride the incoming wave ahead of your competitors. You stand to benefit far more from AI in capital markets if you embrace it earlier and use its associated tools to improve your financial performance and forecasting. To stay afloat, survive in turbulent seas, and reach your final destination as a company you need support; let’s start by covering the essentials of AI for capital markets

AI in Capital Markets

As in other sectors of the wider financial services industry, AI technologies have been present in the capital markets field for quite some time. What’s changed is the increased proliferation of these tools and their acceptance by leading companies. Increased globalization has led to increased sharing of new technology, making it easier for service providers to enter new territories and regions.

That’s because it has become significantly simpler for companies to adopt tools designed specifically for AI in capital markets and learn from the data they consequently gather. This allows companies to create products and services that more closely match the expectations of their clients. This allows said companies to improve both their offerings and their internal processes for increased efficiency, but AI doesn’t just travel in one direction.

Financial services consumers are now significantly more amenable to using AI tools and products, and expect most capital market services providers to integrate at least some AI technology into their offerings. The sudden appearance and widespread popularity of Chat GPT has made the general public all the more comfortable with using AI, and its generative AI capabilities are also being put to good use by capital markets firms.

The Role of AI in Capital Markets

AI technologies are playing an increasingly important role in the capital markets industry, improving the performance of companies internally and by providing new offerings to customers as outlined above. The potential applications of AI technology in this sector is nearly limitless as more data is gathered and processed to prove insights that can be used to develop new technology. The process is cyclical; for the immediate and short-term future, however, these are the primary roles of AI in capital markets.

Robotic Process Automation (RPA)

Labor-intensive and costly processes like trade settlement, customer onboarding and servicing, reconciliation, and regulatory compliance are typically outsourced to low-cost locations; However, offshoring is frequently not sustainable. RPA automates middle and back operations in capital market firms and can reduce labor costs and labor manuals, improve financial accuracy, break down departmental silos, and generate audit trails.

Real-Time Credit and Risk Sensitivity

AI systems can identify patterns in big datasets, and quickly and accurately identify changes in the market (including credit abnormalities) by incorporating machine learning. Not only does this ensure quicker reactions to possible threats, it also makes more advanced risk management techniques possible.

Algorithmic Trading

AI in capital markets can be used to produce rapid and precise market evaluations that offer vital insights into current market circumstances, consumer behavior, and emerging trends. Based on trade algorithms, this enables them to make judgments more quickly and effectively, but also more accurately, generating increased returns.

Regulatory Compliance

The financial markets industry is heavily regulated, and laws are frequently changed. By automating procedures for regulatory compliance, AI for financial markets technology can ensure that your business remains compliant automatically. Using AI reduces or eliminates the risk of human errors and generates automated reports on your entity’s ability to remain compliant, ensuring that any audits you undergo go smoothly.

The Impact of AI on Capital Markets

AI for financial markets is a growing trend that as we can see plays a number of rules in the capital markets sector, but that doesn’t mean that its adoption has been broadly welcomed by all market participants. Even in an industry characterized by rapidly shifting circumstances, there are plenty of operators that remain hesitant to adopt new technology.

Wells Fargo, Deutsche Bank, and JPMorgan Chase are just some of the leading banks in the United States to ban their employees from using Chat GPT. While this does not preclude them from using generative AI more generally, it does highlight how concerns exist amongst major capital markets participants that they should be concerned about generative AI and other associated tools.

The statistics speak for themselves: AI in the capital markets provides a significant number of benefits to those companies that will embrace new technology and achieve a significant competitive advantage. If your company is considering embracing an AI-driven transformation in the capital markets sector, then you should consider highlighting the following statistics: They should prove to even the most recalcitrant of colleagues that AI represents a good investment.

  • Market projections indicate that an additional value of $200 billion to $340 billion will be injected into the complete spectrum of retail and wholesale banking through increased productivity thanks to generative AI.
  • More than half of US-based financial advisors are implementing generative AI-powered customer relationship management (CRM) software to improve investor relationships, according to a Greenwich Wealth Management study.
  • Institutional firms working in capital markets can use AI to get more from their data and deliver better experiences. In fact, by 2030, banks and asset managers can save $1 trillion by incorporating AI technologies into their business models.
  • Manual documentation, phone calls, and emails are necessary for about 60% of capital markets services. Pre-boarding paperwork is being expedited by generative AI’s capacity to read audio, search, extract, and combine unstructured material into machine-readable representations.
  • According to a survey of financial service advisors in the US and Canada, 83% believe AI will have a direct, measurable, and consistent impact on the client-advisor relationship in the next 18 months, and 55% believe to a great extent that AI will have either a transformative or revolutionary effect.

    The Benefits of Using AI in Capital Markets

    There are plenty of examples of the benefits of using AI in capital markets, in fact, there are very few drawbacks to utilizing this exciting new iteration of AI-driven technology. Ernest and Young, one of the big four multinational accounting and consulting firms, published an insightful report into the applications of AI in capital markets and the benefits companies can reap from utilizing its associated technology.

    Similarly to the previous segment, these factors should highlight why any company in the capital markets segment should focus on adopting AI technology. Those that don’t risk surrendering their current market position.

    Performance Management

    • AI models are observed to have consistently better predictive performance compared to most traditional models because of their ability to analyze vast volumes and diverse types of data from various sources.
    • Greater predictive performance results in greater economic gains (confirmed by academia and industry) as these models can process both structured and unstructured datasets.

    Improved Risk Management

    • Advanced AI techniques improve risk management as financial service providers may reduce liquidity-related risks effectively by precise market liquidity forecasting enabled by the strong ability of these models to process a variety of datasets.
    • Reinforcement learning methods may provide added value in dynamic hedging and standard deviation when compared with traditional methods.

    Efficiency Gains

    • Automation of back-office tasks such as data management, reconciliation, and report generation.
    • Significantly improved regulatory compliance through measures such as AntiMoney Laundering (AML) and Know Your Customer (KYC) procedures.
    • Significantly reduced time spent on manual and repetitive tasks.

    Improved Customer Service

    • Improving customer service excellence through third-partyo AI tools thanks to chatbot functionalities and personalized advisory services. Customers frequently report increased satisfaction thanks to AI in capital markets services.

    New Working Methodologies

    • AI algorithms enable the analysis and use of volumes and types of data not feasible for other models, including both unstructured and structured data.
    • This results in improvements to strategies, risk management capability, and optimization of human effort within the utlizing company.

      Case Studies Involving AI in Capital Markets

      While, as we mentioned before, some quarters of the wider banking and financial services industries remain cautious about the application of AI, there are good examples of companies that have successfully utilized this technology. Artificial intelligence in financial markets, capital markets, the insurance industry, etc. has remarkable potential, and nowhere is this more apparent than in the capital markets segment. Its international reach, globalized workforce, and embrace of innovation make it perfect for AI.

      We have researched saver case studies that we believe highlight the best possible applications of AI in the capital markets segment. To emphasize that we remain unbiased on this issue and that we’re focusing solely on highlighting the excellent work done by some companies to innovate, we want to make it clear that we are not connected with these companies. We do not provide professional expertise to them; we are simply impressed (as fellow experts) by their work to use AI.

      Sales Team Analysis

      Professional services network and consulting company KPMG International, another member of the Big Four, reported that it advised a client in the capital markets segment to improve its sales processing. It advised the company to examine the actions of the top-performing salespeople to increase revenue by retraining the sales and account management staff. This relied on sentiment analysis, which uses natural language processing (NLP) to identify, extract, quantify, and study affective states and subjective information from text.

      KPMG was able to successfully engineer his process to facilitate the risk management of rule checks on individual trader mandates. These included crucial capital market factors such as risk limits, suitability, margin controls, and/or jurisdictional permissions. The outcome was that the client company was able to improve its productivity and cost-effectiveness by using AI in capital markets to hone in on their salespeople’s individual traits and apply them to focus on specific financial services segments.

      AI Governance

      Ernest & Young published an interesting case study that highlighted how AI in capital markets can be applied to AI governance. In this case study, the client company wanted to address the risks posed by AI systems that are not currently covered by governance standards in the capital markets segment. Current systems have issues with biases, lack explainability, and are often non-interopperable.

      The resulting solution was focused on creating an organizational-level solution that could maintain independent risk management, use an integrated risk mitigation approach, and prioritize risk based on the likelihood of occurrence and impact. The resulting model could assess the level of interpretability and explainability needed, design the system accordingly, and modify the loss function by putting financial constraints in place to gain more interpretability. It also included document methodology, including limitations, to streamline long processes, enhance data extraction, and analyze structured and unstructured data better.