The artificial intelligence (AI) landscape is constantly evolving, and large language models (LLMs) have gained global traction for their bespoke capabilities. Notably, ChatGPT reached 100 million users merely two months after its launch, making it the fastest-growing application in history. These developments showcase generative AI’s abilities, pushing the boundaries of what technology can do with text and language.
However, the utilization of LLMs is controversial and has been the subject of debate among academia, regulatory bodies, and the general public. Skeptics point to hallucination as a significant drawback of AI models, which would be pronounced in cases where the model provides responses based on pattern recognition rather than reasoning. Various entities have urged the government to hasten AI-related regulation in response to the extensive adoption of generative AI models. Moreover, there are significant concerns with privacy, security, trust, and reliability. There is a serious threat of ‘model collapse’ when the knowledge base underpinning generative AI systems is inundated with imperfect information, deliberate misinformation, and uncontrolled synthetic data. The proverbial GIGO is at play — garbage in, garbage out.
Despite these apprehensions, generative AI is a comprehensive technology that can transform work for different sectors. Corporations have been rapidly spending on AI, with several industries investing considerable time, money, and resources. While some organizations are moving at a steady pace, others have shared a multiyear commitment to integrating this technology across their functions. While there are sectors that find the current imperfections of generative AI unacceptable, there are those, such as financial institutions, that have actively experimented and deployed use cases in lower-risk areas.
THE VALUE PROPOSITION FOR FINANCIAL SERVICES
While banks and financial institutions have already been utilizing AI applications for different areas like credit risk and fraud, generative AI could further enhance other services, streamlining a broad array of business functions and uses that can elevate core offerings. Several applications and functions are suitable for AI adoption, including customer marketing, insurance claims processing, and financial planning. Internal services like application development, compliance monitoring, and maintenance also have potential.
Technological advancements help expand business-use cases, particularly when dealing with unstructured data like text. Thus, organizations can create or refine business content using generative AI’s ability to query data in a natural, humanlike manner. However, the technology is still in its nascent stage, meaning AI should be synergized with human expertise to generate accurate insights and create long-term value.
OPERATIONAL EFFICIENCY AND AUGMENTED INTELLIGENCE
Financial services firms could slowly integrate AI in lower-risk areas like augmented intelligence and operational efficiency to minimize risk. Differing views have tempered AI adoption, but organizations have been experimenting with various use cases due to the technology’s reported benefits and strengths. To retain their competitive positioning, firms should assess and leverage AI in controlled environments.
Operational efficiency involves enhancing productivity and reducing costs by automating tasks like information categorization, review, and synthesis. On the other hand, augmented intelligence entails assisting experts by providing content, insights, and recommendations for clients.
CROSS-FUNCTIONAL CAPABILITIES
In the following areas, AI can support operational efficiency, reallocating human effort to other critical tasks:
Tax and legal. Augment tax file generation, streamline contract organization and refine diligence processes for legal teams.
Product, technology and IT. Create new product or service functionalities, generate natural-language-based code blocks and make test cases for evaluating code vulnerabilities.
Risk and compliance. Map risk controls with corresponding regulations and flag missing disclosures or regulatory risks like fair customer treatment and sales practice concerns.
FUNCTIONAL SOLUTIONS
Generative AI can also be leveraged in the following functions to streamline operations and innovate new ways of doing business:
Chatbots and virtual assistants. Provide tailored, end-to-end support using natural language. Specialists can configure this functionality based on internal or external knowledge or information, subject to the organization’s discretion.
Knowledge management and generation. Appropriately sift through and retrieve institutional knowledge and intellectual property. Organizations can utilize generative AI to augment and create content based on internal or external knowledge databases.
Document intelligence. Execute advanced information extractions from unstructured or semi-structured data formats. The process can also focus on specific attributes and elements or generate insights from available information.
THE FUTURE OF AI IN FINANCIAL SERVICES
Generative AI has the power to transform businesses. For financial services firms, transformation entails capitalizing on the technology’s strengths while managing the corresponding risks. Successfully creating value from AI involves a synergy between the latest technology and an organizational culture that invests in various capabilities and develops a framework for risk management.
The financial services sector has a head start with deploying generative AI, given its experience with navigating AI-related regulation. Hence, many financial institutions have become market leaders in devising an AI governance framework, which includes setting policies, standards, and procedures. In the same vein, other industries and organizations should address critical areas like AI governance and oversight frameworks when integrating this technology into their operations.
Lastly, effective board governance is crucial for the management of generative AI.
While these practices will need to be polished and redefined, financial services institutions should use this time to identify and invest in potential applications for the technology. Organizations that successfully integrate generative AI into their organizational makeup can differentiate themselves and remain competitive.
This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.
Christian G. Lauron is the Financial Services Organization (FSO) leader of SGV & Co.
