Industry
Capital Markets
Unlocking Exponential Future Growth in Capital Market Firms
HIGHLIGHTS
- The capital markets industry sees artificial intelligence (AI) as a disruptive technology that can truly transform the business landscape.
- They are experimenting with generative artificial intelligence (GenAI) by running proofs-of-concepts for specific use cases like digital assistants and knowledge management.
- To make the most of the transformational potential of GenAI, companies should go beyond use cases and integrate AI into their enterprise strategy, specifically in revenue growth, quality enhancement, and client experience.
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AI leads the way
The banking, financial services, and insurance (BFSI) industry considers artificial intelligence (AI) a key lever for disruptive transformation.
The BFSI industry report of the TCS AI for Business Study reveals that BFSI business executives want to innovate and make money with AI, without just restricting it to a cost-saving tool. According to the report, 67% of executives surveyed tilted toward leveraging AI primarily for innovation and revenue growth compared with just 18% solely focused on using AI to lower costs and optimize operations. We observed a similar trend in the securities and investment services space with three in five B2B finance executives strongly inclined toward using AI for innovation rather than optimization.
In capital markets, predictive AI, data analytics, and automation technologies have been traditionally used in areas such as client onboarding, back-office operations, straight-through processing (STP), and risk management. However, with the advent of generative artificial intelligence (GenAI), the industry is looking at possibilities for transformative business impact by leveraging composite AI—a combination of these technologies. Technology teams are experimenting with GenAI, running proofs-of-concepts (PoCs) for specific use cases like digital assistants and knowledge management. Firms are considering setting up horizontal GenAI platforms to experiment across multiple business segments such as asset and wealth management, investment management, financial markets, and so on.
Given the significant benefits it offers, leading capital markets firms are beginning to embrace GenAI:
- A global leader in business and financial information has launched a proprietary model trained using over 700 billion tokens from its 40-year repository of financial documents combined with general purpose data. This model will utilize natural language processing (NLP) tools to improve sentiment analysis, named entity recognition, news classification, and frequently asked questions (FAQs).
- A leading global bank has introduced a new product that builds quantitatively selected thematic indices for institutional clients. It uses NLP to define the constituents of a thematic portfolio.
- A leading wealth management firm has launched an AI assistant for financial advisors and support staff. It offers speedy access to a database of over 100,000 research reports and documents to bring new efficiencies in customer service.
Capitalizing on transformational opportunities
There is a significant opportunity for AI enablement across capital markets firms.
According to findings from the aforementioned study, nearly three-quarters of securities and investment services industry leaders are focused on establishing an enterprise-wide AI strategy or are exploring, preparing, or doing pilots with AI (see Figure 1).
Figure 1: Nearly three-quarters of securities and investment services leaders are focused on leveraging AI
Business leaders at top B2B finance firms are looking at adopting GenAI for innovation and revenue growth. In fact, the AI study data shows that 82% of securities and investment services executives say they have AI projects aimed at growing revenue.
During the initial stages of any disruptive technology, the art of the possible is often demonstrated by leveraging it for specific use cases, and GenAI has been no exception to this trend. But to successfully scale GenAI adoption in the capital markets domain and realize its true potential, it is imperative to move beyond engaging in point solutions to enhance specific use cases. Business stakeholders must sponsor initiatives aimed at reimagining key functions and existing value chains to deliver strategic advantage and unlock transformative business value.
However, convincing business leaders to invest in transformation programs using GenAI is proving to be a challenge. Gaining buy-in from business leaders will demand air-tight business cases, meticulous planning, establishing key business metrics, developing transparent cost structures, and addressing concerns around technology maturity. Above all, identifying high-impact business problems, ideas, and opportunities is crucial as such AI implementations are more likely to get funding.
To accomplish this, the discussion must move beyond use cases to areas where GenAI can transform business outcomes (see Figure 2).
Figure 2: From revenue to customer experience – the many areas that will derive benefit by using GenAI
We believe that the key themes of revenue growth, quality enhancement, and improved client experience will drive GenAI adoption in capital markets. Cost and efficiency optimization are important but will not be primary drivers. However, efficiency and cost optimization will become relevant in fast-growing areas that can leverage GenAI to scale operations without significant increase in expenses.
Capital markets organizations must define an enterprise-wise AI approach, incorporating composite AI solutions centered on a business transformation view instead of a technology view. An enterprise-wise AI approach will usher in enterprise-grade transformation by offering comprehensive solutions to business problems—an approach which is certain to capture the attention of CXOs and other top executives rather than a technology-specific point solution approach. An effective AI solution must include capabilities from GenAI, predictive AI, and data analytics. Additionally, while establishing well-defined key performance indicators (KPIs) is crucial to demonstrate the value of AI implementations and gain top management buy-in, our study reveals that only 19% of B2B finance executives believe they have ’good enough’ metrics and KPIs.
In capital markets, GenAI offers big opportunities for better business outcomes through disruptive transformation.
- Investment research: GenAI can disrupt this area by improving the quality of research and efficiency. By gathering information from a much larger pool of sources and preparing research reports faster, without requiring the assistance of experts, GenAI tools can enable a steep change in investment results.
- Reporting: GenAI can enable new features such as querying report data, graphs, and charts in natural language, pre-generated reports, key points summary, and call-to-action generation, among others. Business leaders can intuitively gather insights from reports to arrive at actions to be taken and spot anomalies for remedial action.
- Investment and risk management: GenAI techniques can be applied to unstructured content such as news reports and social media posts to alert firms in real time about an upcoming investment opportunity, which might otherwise remain hidden. Similarly, GenAI tools can alert firms when exposure to a customer or counterparty rises to risky levels, in turn improving risk management.
- Wealth management: GenAI can disseminate enterprise knowledge and information to internal stakeholders and customers in an efficient and intuitive manner. Financial advisors, research analysts, and portfolio managers, among others, will benefit through the capability to deliver positive outcomes to wealth customers.
Breaking through the barriers
The study has identified the top challenges to GenAI adoption in securities and investment services.
The foremost is ensuring ethical and responsible use of AI (see Figure 3).
Figure 3: Top concerns in GenAI adoption
Ethical and responsible AI use: According to the study, 41% of B2B finance executives surveyed expect more than half their employees to be using GenAI daily within the next three years. Consequently, firms will need to enhance focus on ethical and responsible use of AI. This can pose difficulties given the inherent biases of AI systems and the possibility of GenAI hallucinations. Additionally, firms will face problems in ensuring data protection and privacy, accuracy of information provided by the AI tool, and regulatory compliance. Firms must adopt technology solutions that offer guard rails to protect data and eliminate biases.
AI research and development: AI is evolving fast, with exponential increase in capabilities with every new release - this is particularly true for GenAI. With every release, firms will need to put in significant research and development effort to understand and integrate these capabilities in a manner that positively impacts business outcomes and customer experience. Keeping pace with the rapid evolution of this technology can, therefore, pose challenges for business leaders.
Lack of IT readiness: Business leaders are concerned about the readiness of the technology as well as their infrastructure to accommodate large scale adoption of GenAI. As the technology is evolving rapidly with continuous expansion of capabilities, baselining them is proving difficult. At the same time, a lack of organizational readiness across data, integration, cloud, and cybersecurity can be detrimental for meaningful adoption of AI and GenAI.
Business taking the driver’s seat: A game changer
Sponsorship from business leaders will sharpen the focus of GenAI programs in capital markets firms.
Backing from business stakeholders will not only ensure alignment with business objectives and customer needs, but also help identify risks and possible mitigation approaches. Most importantly, it will enable a shift in focus in the GenAI adoption approach from technology themes to business themes such as novel products and services, new customer segments, experience, improved service, and cost transparency.
To implement GenAI for transformational business impact, firms must navigate the following stages (see Figure 4).
Figure 4: The three stages in using GenAI for business transformation
Explore: Business leaders must understand the potential of AI, and GenAI in particular, and what it can do for them. Awareness and ideation sessions will help determine the art of the possible for specific functions such as operations, marketing, research, or customer services. Technology demonstrations using PoCs will help business leaders visualize the potential. Transformative ideas will emerge from this and PoCs can be run for a few key ones.
Experiment: This stage goes beyond PoCs to develop solutions that generate contextual business value. Tools that assist the research team to create a better report or help product teams to quickly design superior brochures or enable service teams to effectively respond to customers can be a great starting point. These solutions must be implemented in a limited and non-intrusive manner across entire value chains. Business leaders must take a decision on whether to scale up after assessing the impact of such solutions using well-defined metrics and KPIs.
Enhance: In this phase, business leaders must sponsor transformation programs, providing direction on the key risks associated with the AI solution as well as the mitigation measures to be incorporated. A well-developed business case supported by a roadmap with defined milestones forms the foundation of AI and GenAI programs. IT teams must design scalable solutions that address associated technology risks.
Harnessing AI and GenAI technology for specific use cases will have limited impact. A business transformation perspective is imperative for GenAI programs to gain momentum and result in adoption at scale in the capital markets industry. Buy-in from business leaders will be key to success. To realize the true potential of AI, capital markets firms must develop solutions that bring together GenAI, predictive AI, and analytics as part of a wider enterprise AI strategy that effectively leverages its unique capabilities while also addressing risks.
