Industry
Capital Markets
Unlocking Exponential Future Growth in Capital Market Firms
Solution
Consulting
TCS Consulting Services to Achieve Success in the Digital Era
Data and Analytics
Harness the Power of Data and Analytics for better Business Outcomes
Highlights
- Capital market firms need to constantly adapt to industry trends to stay ahead, and artificial intelligence (AI) technologies such as generative AI (GenAI) can help them achieve this.
- For instance, firms can benefit from recommendation engines built using large language models (LLMs) and can improve customer engagement with LLM-powered digital assistants.
- They must invest in assets, frameworks, and talent needed to explore the possibility of running proofs-of-concept (PoC) to assess the feasibility of large-scale adoption of GenAI.
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Introduction
Capital market firms operate in a complex and ever-changing technology landscape.
At the same time, they must contend with increased regulatory scrutiny, competition from fintech players and the crypto industry, soaring operational and service costs, increased customer expectations, black swan events and so on. Firms need to constantly adapt to stay ahead of the curve, and artificial intelligence (AI) technologies such as generative AI (GenAI) have become a key enabler in this strategic play.
In recent months, GenAI has garnered interest from various financial services firms after the launch of ChatGPT, which is powered by large language models (LLMs). GenAI can combine with AI and human intelligence to create content such as text and images and solve complex analytical problems.
GenAI in capital markets
GenAI finds relevance in capital markets in particular as domain-specific tools are readily available.
While tools such as ChatGPT are generic across domains, vertical-specific LLM-based tools are gaining popularity as they offer better accuracy. Domain-specific models perform significantly better than open models on specific tasks without compromising performance on general LLM benchmarks. BloombergGPT is an example of a domain-specific model relevant for capital markets. However, open models outperform domain-specific models in general tasks. In capital markets, domain-specific models are useful in classifying news, preparing and filing regulatory reports, retrieving company-specific information, and creating research reports on companies.
We examine a few areas where GenAI can be leveraged in the capital markets space.
Wealth management
Clients of wealth management firms expect personalized advice, presenting a dilemma to firms, which are under pressure to reduce customer service costs. Financial advisors can benefit from a recommendation engine built using LLMs. LLMs can be leveraged to draw insights from unstructured data such as research reports, regulatory filings, and images. GenAI can summarize data from many sources quickly and accurately and generate content for financial advisors and investors much faster than existing systems.
Trading
Electronic trading systems execute trades using algorithms and defined trading strategies. GenAI can enable financial services firms to summarize information required for trading in real time, thereby reducing execution time. Additionally, it can rapidly analyze vast amounts of historical data and trends to predict short- and near-term price movements based on news and economic indicators. Further, it can generate customized technical and fundamental indicators for making buy or sell decisions. From an algorithmic trading perspective, users can leverage GenAI to create bots that can automatically place orders based on personalized trading rules.
Customer engagement
Backed by extended reality (XR), an LLM-powered digital assistant can read facial features as well as analyze customer emotions in real time and adjust the tone of the conversation. Such assistants can help humanize digital customer interactions and deliver an immersive, contextual experience, in turn strengthening customer relationships.
Investment banking research
GenAI can help summarize news articles and financial reports of a specific company and generate economic summaries of industries relevant to investment targets. When trained on historical market data along with key economic parameters (such as inflation, interest rate, energy prices, news), it can help firms forecast market trends. Investment advisors and their clients can instantly access stock recommendations without paying hefty subscription charges for research reports.
Fraud and compliance management
Capital market firms can improve their ability to detect and report financial crimes to regulators and enhance overall compliance and risk management efforts. LLMs can be deployed to identify suspicious transactions in exchanges. Further, GenAI tools can be used to prepare accurate suspicious activity reports (SARs) in real time with lesser human effort. They can also be used for the review of contract documents during onboarding with lesser effort and at a higher accuracy, which improves compliance.
Risk management
GenAI has the potential to improve the effectiveness of risk management in capital markets through synthetic data or data that is artificially generated rather than produced by real-world events. Synthetic data has emerged as a popular tool for risk management experts to overcome the limitations of historical data and access a full range of stress scenarios. GenAI can greatly enhance risk controls by identifying fraudulent transactions in order books of stock exchanges using synthetic data, preventing insider trading and supporting anti-money laundering (AML) measures.
Concerns plaguing GenAI
GenAI has immense potential to disrupt capital markets in the analytics and advisory areas.
ChatGPT can enhance productivity by playing the role of a co-pilot to researchers, traders, and compliance specialists. End-customer experience too will improve significantly through smarter chatbots and transparent interactions.
However, GenAI has raised questions around transparency, data security, fundamental rights, and fake news leading to social tensions. Additionally, concerns such as fear of job loss, data privacy, and lack of enterprise offerings will also need to be addressed effectively. This will demand collaboration among stakeholders such as governments, regulators, and capital market firms to accelerate the adoption of GenAI in capital markets.
The way forward
In our view, GenAI adoption will occur gradually given skepticism and apprehensions in the absence of comprehensive regulations.
However, we believe that capital markets firms must invest in the requisite assets, frameworks, and talent needed to explore the possibility of running proofs-of-concept (PoC) to assess the feasibility of large-scale adoption. As they do this, they must also keep abreast of the developments in the GenAI space because once the technology becomes mainstream, firms will need to hit the ground running to gain a lead.
