One way to gauge emerging trends is to observe the flow of capital toward specific technologies. This often signals current interest and future leaders who will drive the next wave of disruption. According to researchers at CB Insights, investments in Generative AI (GenAI) have exploded. Funding for AI startups jumped 5X in 2023, going up from $4.3 billion across 257 deals to $21.8 billion across 426 deals compared to the previous year.[i]
Late last year, Reuters reported that about 60% of GenAI startups have hit Unicorn status.[ii] These numbers paint an exciting picture of the shape of things to come. The question is, how will these investments impact the financial services landscape? We believe that financial services will be a significant beneficiary of GenAI.[iii]
According to McKinsey, GenAI could add the equivalent of $2.6 trillion to $4.4 trillion in value to the industry.[iv] The McKinsey report says banking has the potential to unlock new value of between $200 billion and $340 billion (equivalent to 9–15% of operating profits) across all segments and functions of the industry. Corporate and retail banking and software engineering are forecasted to accrue the most significant gains from the technology. [v]
In addition, global patent filings often indicate the direction of emerging of trends. The latest Patent Landscape Report on GenAI of the World Intellectual Property Organization (WIPO) is revealing. Only 12 patents were filed in the banking and finance segment in 2014. This went up 30X in 2023, with 352 patent filings. The banking and finance industry is in the top 10 segments for filing GenAI patents.[vi] These patents will result in new products and services, dramatically reshaping the industry.
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Moving toward center stage
Industry leaders are already using GenAI effectively. One banking giant has launched a customized internal tool for its financial advisers and support teams that lets them access 100,000 research reports and documents using OpenAI’s GPT4.[vii] Another global bank uses the technology to help portfolio managers make better investment decisions by leveraging 40 years of historical data and identifying potential biases in their strategies, avoiding decisions that end up selling stocks prematurely.[viii]
From a strategic perspective, GenAI is quickly moving toward center stage in the end-to-end transformation of financial services. The technology will enable users to automate tasks such as cheque processing and new product development. In the next 12 to 18 months, most of the industry will focus on three critical areas. They will use the technology to drive revenue, increase market share and improve productivity.
Three critical areas
- Drive revenue
The first step to driving revenue is personalization. Today’s GenAI models and tools can quickly provide customers with personalized messaging and offers driven through channels (such as online marketing and in-app interventions) with the potential to deliver the highest customer engagement. Speed and scale are critical elements for elevating the customer experience and lowering the cost of engagement.
HCLTech is creating a demo of a virtual agent that will help sales executives interact with the CRM and analytics platform to understand customer behavior, generate content, onboard customers and understand invoicing and billing details. An executive can use the virtual agent instead of interacting with multiple platforms. An embedded AI engine takes over to intelligently and flawlessly deliver custom-crafted insights, suggestions, and solutions, resulting in higher revenue, better customer retention and scalable operations at lowered costs.
- Increase market share
The financial services industry must quickly decipher economic trends, regulations and new customer behavior to design new products and services that serve changing needs. GenAI can analyze thousands of industry reports, company filings, policy reports, social media streams, podcasts and customer interactions across channels — in a variety of formats — trawling them for insights that result in identifying new markets, aiding in product discovery, and supporting it with customized messaging to gain the attention of potential customers.
- Improve productivity
Large Language Models (LLMs) used in GenAI can make complex enterprise data accessible using natural language processing (NLP). This ensures executives have instant and easy access to information and insights from across the enterprise, along with recommendations and decision support. Executives in banks spend endless hours synthesizing information and data to create summaries and reports. GenAI can speed up the process, thereby improving productivity across functions. Often, tasks that require a day to execute can be completed with a couple of prompts within a few minutes with the help of GenAI.
Supporting regulatory and compliance standards
GenAI is increasingly being deployed in the regulatory and compliance landscape necessitated by the increasing regulatory headwinds. As companies become digital and adopt cloud, compliance becomes even more significant. It’s no longer only about following rules but also about staying agile and perceptive to these dynamic trends. Integrating GenAI helps companies optimize compliance monitoring through automated alerts and case management, preventing compliance lapses and ensuring that organizational practices remain up-to-date. Enhancing the accuracy and timeliness of reporting on suspicious activities mitigates financial crime risks, strengthening the institution’s regulatory posture with both regulatory mandates and stakeholder expectations.
Implementing GenAI in financial services with HCLTech
In the financial services industry (FSI), we believe two critical challenges must be addressed for successful GenAI integration: cloud infrastructure and data readiness.
- Cloud infrastructure: Many FSIs still rely on outdated or fragmented on-premise infrastructure, which limits their ability to scale GenAI solutions. A robust, scalable and secure cloud infrastructure is essential for the deployment of large-scale AI models. The flexibility and computing power required for GenAI, particularly in handling complex data sets and performing high-volume transactions, are best supported by cloud environments.
- Data readiness: GenAI is heavily dependent on vast amounts of high-quality data to function effectively. However, many financial institutions struggle with data silos, inconsistent data quality and legacy systems that hinder seamless data access and integration. For GenAI to deliver accurate, actionable insights, FSIs need to ensure that their data is clean, well-organized and readily accessible. Additionally, compliance and regulatory considerations require that the data used in AI models is traceable, explainable and secure.
While there is significant excitement around Generative AI (GenAI), large-scale enterprise adoption remains limited. To successfully scale GenAI across the enterprise, certain key guardrails must be in place.
GenAI offers tremendous potential for scaling, but the cornerstone of successful enterprise-level adoption is ensuring traceability and responsibility.
- Traceability and responsibility: As AI systems become more embedded in business operations, particularly in highly regulated sectors like financial services, ensuring transparency and traceability is paramount. Organizations must be able to track how decisions are made, ensuring that AI models are explainable and auditable. This is especially critical in compliance-heavy environments where accountability is required at every stage of the value stream. Establishing traceability not only supports regulatory adherence but also builds trust, enabling companies to deploy AI responsibly while minimizing risk and ensuring alignment with governance frameworks.
As organizations progress in their AI journey, they should also consider several key aspects to ensure success:
- Cognitive infrastructure: AI systems are highly demanding in terms of computational power, making chip-level performance critical. It’s essential to utilize advanced, efficient hardware to ensure AI processes run smoothly and deliver accurate outcomes, without compromising on speed or scalability.
- Interoperability: AI requires systems to work together seamlessly, demanding interoperability across various platforms.
- Training AI: Unlike traditional technologies, AI isn’t programmed but taught. This requires different approaches in development and deployment.
- ROI for AI use cases: Many projects stall due to cost inefficiencies, particularly with large language models (LLMs). Organizations need to carefully evaluate ROI before scaling AI use cases.
By addressing these factors, financial institutions can ensure that their AI initiatives are sustainable, compliant and capable of delivering long-term value.
[i] https://www.cbinsights.com/research/generative-ai-funding-top-startups-investors-2023/
[ii] Unicorn = valuation of $1B or more: https://www.reuters.com/technology/genai-startups-biggest-driver-unicorns-says-vc-firm-accel-2023-10-17/
[iii] https://siliconangle.com/2024/06/12/generative-ai-in-financial-services-awsfinserv/
[iv] Across 63 use cases that were analyzed
[v] https://www.mckinsey.com/industries/financial-services/our-insights/capturing-the-full-value-of-generative-ai-in-banking
[vi] https://www.wipo.int/web-publications/patent-landscape-report-generative-artificial-intelligence-genai/en/index.html
[vii] https://www.forbes.com/sites/qai/2023/09/19/morgan-stanleys-ai-assistant-marks-new-era-for-finance-sector/?sh=7ba70f1c1ff2
[viii] https://finance.yahoo.com/news/jpmorgan-play-more-moneyball-wall-180718978.html
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