Ghosts In The Machine

GenAI In Banking & Financial Services

What was an emerging transformation is already underway…

by Elena Christopher

Elena Christopher is Chief Research Officer and Head of Financial Services Research at HFS Research, a global research and analysis firm specialized in the disruptive power of emerging technologies.

The banking and financial services industry stands on the cusp of a transformative era, propelled by the advent of generative AI (GenAI). GenAI is not merely an evolutionary step in technology; it is a revolutionary force capable of redefining industry standards, operational efficiencies, and customer experiences with the power to redefine how financial institutions operate, engage with customers, and manage risks. However, the path to realizing GenAI’s full potential is fraught with challenges—from ethical dilemmas to regulatory hurdles—necessitating a strategic and thoughtful approach to adoption. We’ll cover the roses and the thorns, but first a little context.

The Three Phases Of AI: Foundational, Generative, And Purposeful

While the hype and pace of announcements around all things artificial intelligence have exploded since large language model (LLM) darling ChatGPT 3 was announced in late 2022, AI has been an active element of the financial services sector for decades.

HFS categorizes the modern era of AI into three phases ( Exhibit 1): foundational, generative, and purposeful.

  • Foundational AI (2010-2023): Phase 1 represents the technology building blocks, such as machine learning, computer vision, and natural language processing, that form the foundations of AI capabilities for the enterprise—the essential first steps for extracting greater value from AI. In Phase 1, we learned how to organize and use data in a way that can learn and become useful for workflows and interactions.
  • Generative AI (2023-2027): Phase 2 is when the foundations are ready to exploit the value of AI and data at scale. Projects in this phase involve applying GenAI to vast amounts of information and asking it to generate useful output, such as developing code, identifying new process flows, generating dynamic chatbots that continually update business context, continually correlating data sources to drive probabilistic decision making, and collating cyber threat assessments via accessing industry-specific GenAI networks. As an example, HFS Research has Phase 2 in production on our website, where rather than using a basic web search, visitors to the HFS website can ask our large language model (LLM) a question and receive a natural language response it generates from all the research on our website.
  • Purposeful AI (2025 onward): Phase 3 is where AI operates independently and effectively at scale, and humans set the guardrails by which AI will operate. Phase 3 projects take the principles of Phases 1 and 2 to the next level by leveraging multiple technologies and ecosystems to take the human out of the loop but allow the human to orchestrate the loop. Humans set the goals and boundaries, but the AI is empowered to deliver on the actions. If purposeful AI can navigate the complex apps ecosystem of today’s enterprise, we can imagine asking a digital assistant to onboard an employee and it automatically triggering tasks and executing device procurement, benefits briefing, training, and the like. Or, with a click of a button, one could plan demand, forecast, and optimize the supply chain network.

The Expected Impact Of GenAI In Financial Services Is Overwhelmingly Positive

In a recent survey of banking, financial services and insurance (BFSI) enterprises across the Global 2000, HFS queried respondents on the overall impact GenAI would have on their businesses in the coming year and it revealed positive expectations (Exhibit 2). The vast majority of respondents, 80%, indicated they expect AI to have an overall positive impact. Just 8% expect negative impact. The remaining 12% were split between too early to say and limited impact. While there is always great hope for new technologies, this positive outlook aligns with the future view of purposeful AI.

Exhibit 2. 80% of financial services firms expect genAI to have a positive impact on their business 

The Top Emerging GenAI Use Cases In Financial Services Favor Analytics And Customer Experience

The launch of ChatGPT3 ushered in the world of GenAI and significant hope for impactful use cases. While many financial institutions had phase I foundational data science or applied AI programs established before ChatGPT 3 was announced, firms continue to explore their options and potential use cases for GenAI. In HFS’ growing database of in-production GenAI use cases, BFSI enterprises comprise about a quarter of all entries.

An analysis of the BFSI industry use cases (Exhibit 3) reveals that analytics and insights such as lending credit decisioning and underwriting is the top category (42%). Customer experience (CX), with a heavy focus on better enablement of agents and enhanced chatbot capabilities, and contextual search for internal knowledge management and refining customer-facing search capabilities rounded out the top use cases. These leading use cases are squarely aimed at beating down manual labor and dramatically increasing productivity, yielding tangible cost savings. The strong leverage of existing, phase I foundational AI competencies fuels the development of in-production BFSI use cases.

Exhibit 3: In-production GenAI use cases in BFSI favor analytics and CX 

The broader implications of GenAI extend to productivity enhancements across core business operations, including voice-based work, coding, testing, and transactional processing. This shift towards AI-driven operations supports autonomous decision-making and introduces a new dimension of creativity in traditionally mundane tasks.

Additional examples of emerging genAI use cases include:

  • Enhancing Customer Experience – GenAI can revolutionize customer interactions by providing personalized financial advice, automating customer service inquiries, and offering predictive insights to help customers make informed decisions. This level of personalization and efficiency can significantly enhance customer satisfaction and loyalty.
  • Streamlining Operations – By automating routine tasks and processes, GenAI can dramatically increase operational efficiency, reduce costs, and free up human resources to focus on more strategic initiatives. This includes automating document processing, risk assessments, and compliance reporting.
  • Innovating Products and Services – GenAI’s ability to analyze vast datasets can uncover new customer needs and market opportunities, driving the development of innovative financial products and services. This includes personalized financial products, real-time risk management tools, and more efficient trading algorithms.
  • Improving Risk Management and Compliance – GenAI can transform risk management and compliance by enhancing predictive analytics, automating monitoring processes, and providing more accurate fraud detection mechanisms. This not only reduces the risk of financial losses but also ensures stricter adherence to regulatory requirements.

GenAI Is Not A Silver Bullet – Key Considerations For Success

The adoption of GenAI in the banking and financial services industry, however, is not without its challenges.

Key among these are:

  • Ethical Considerations and Professional Obligations: As financial institutions navigate the integration of GenAI, they must carefully balance the technology’s capabilities with ethical considerations and the nuances of human expertise. The profound impact of GenAI on operations and content generation necessitates a thoughtful approach to its deployment, ensuring that professional obligations are met without compromising on the subtleties that define human judgment and ethics.
  • Skill Gaps: Success of GenAI hinges on availability of human talent with the right skills. Despite the technology’s potential to automate and enhance various functions, the need for skilled professionals to manage, interpret, and govern these systems remains paramount. This underscores the importance of investing in talent development and acquisition to fully leverage GenAI’s capabilities. Purposeful AI does not happen without the human talent to provide the guardrails.
  • Data Quality and Trust: Effective GenAI deployment is challenged by issues related to data quality and the inherent trust in the outputs generated by these systems. The “black box” nature of AI, coupled with concerns over the accuracy and reliability of its outputs, necessitates a robust governance framework. Ensuring high-quality data and establishing clear mechanisms for oversight and accountability are critical to building trust in GenAI applications.
  • Regulatory and Legal Constraints: The banking and financial services industry operates within a tightly regulated environment. Legal and regulatory constraints pose significant challenges to the adoption of GenAI, with concerns around privacy, cybersecurity, and compliance taking center stage. Navigating these constraints requires a proactive approach to regulatory engagement and deep understanding of the legal implications of GenAI deployment. Effective governance and guardrails are essential to success.
  • Cost and Infrastructure Requirements: The implementation of GenAI solutions demands substantial investment in IT infrastructure and talent. Costs associated with data management, prompt engineering, and the operation of foundational models can be significant. The environmental impact of AI, particularly its carbon footprint, adds another layer of complexity to the adoption process. Building a viable long-term cost model and securing the necessary resources are crucial steps to harness the power of GenAI.

And while most firms, across all industries hate to admit it, the biggest challenge is a deeply rooted resistance to change. We can interrogate the efficacy and potential of GenAI till the cows come home, but if we are not willing to conceptualize new ways of working, GenAI will remain a cool widget. Addressing these challenges requires a strategic approach that balances pursuit of innovation with realities of ethical, regulatory, and operational constraints – and openness to change.

The broader implications of GenAI extend to productivity enhancements across core business operations, including voice-based work, coding, testing, and transactional processing...

The Bottom Line: AI Is A Matter Of “When” Not “If”. Smart BFSI Enterprises Need A Plan And An Open Mind

The banking and financial services industry is at the forefront of a GenAI-driven transformation. The technology’s potential to streamline operations, enhance customer experiences, and bolster risk management practices heralds a new era of efficiency and innovation. As the industry navigates this journey, the early adoption and integration of GenAI into existing frameworks will be pivotal in realizing its full potential and securing a competitive advantage in the rapidly evolving financial landscape. AI, be it foundational, generative, or purposeful, is here to stay. The future leaders in BFSI will be those who choose to embrace AI and the myriad of ways it will change ways in which we work and the very fabric of financial services.

The AI And Human Collaboration Process

This article was generated in part by using HFS Research’s own GenAI engine, Humata.ai, which is trained on, and leverages HFS Research’s latest research, on AI and other IT topics. The prompt used was “What is the expected impact of generative AI on the banking and financial services market? Please cover opportunities and challenges.” In a true example of collaboration between AI and humans, HFS Research’s Chief Research Officer and Head of Financial Services Research, Elena Christopher, leveraged the content as a starting point, weaving in additional insights from CEO and Chief Analyst Phil Fersht, and added her own flair and turn of phrase.

For detailed definitions on all things AI, please refer to HFS Research’s snackable AI glossary to enhance your understanding of the new tech shorthand.

 

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