Winning with GenAI: Strategy Considerations for Banks

With the advent of ChatGPT, AI has ascended into mainstream awareness, sparking a global recognition of the immense potential of Generative AI. Banking and financial services firms are accelerating their GenAI adoption trajectories. Morgan Stanley recently launched an advanced chatbot powered by OpenAI’s GPT-4 to support the bank’s financial advisors. Goldman Sachs, meanwhile, is developing a dozen projects that will incorporate Generative AI into its business practices.

Clearly, banks see Generative AI as a powerful new tool to drive innovative products and services. They are delving into transformational use cases, including algorithmic portfolio optimization, automated customer support, personalized financial planning, and dynamic pricing models to revolutionize both operational efficiency and customer engagement.

At the same time, as with any much-hyped new technology, Generative AI comes with its own set of risks: compliance risks, model hallucination, privacy concerns, data copyright issues, biases, and misinformation. Business leaders are caught between two extremes: Should they interpret the buzz around Generative AI as a reason to invest heavily? Or should they be skeptical about returns on investment, push back against the sudden deluge of proposals for Generative AI projects, and tread cautiously?

Building a Data-centric Generative AI Strategy

Over the years, banks and financial institutions dedicated substantial investments to construct algorithms and machine learning models for market differentiation. Today, with best-in-class Generative AI models equally accessible to every enterprise, the true differentiator is in how enterprise data is leveraged to train these models. The emphasis is shifting from building models to establishing a robust data foundation that uniquely and strategically trains large language models (LLMs).

Banks are increasingly pivoting towards a data-centric approach to leverage the capabilities of Generative AI effectively. Central to this strategy is the integration of both internal and external data sources, a convergence that not only enriches the data landscape but also unlocks transformative value. For instance, by incorporating external market data and consumer behavior insights, banks can gain a competitive edge in crafting personalized financial products and tailored customer experiences. Moreover, the fusion of structured data from traditional banking systems with unstructured data (such as social media sentiment analysis or real-time transaction monitoring) offers a holistic view that enhances risk assessment and fraud detection capabilities. Consequently, fostering a comprehensive data foundation becomes pivotal. A strong data foundation equips Generative AI algorithms with the depth and breadth of information needed to drive innovation, optimize operational processes, and deliver unparalleled value across the banking spectrum.

Wipro's distinctive approach to data strategy involves the implementation of a metro map framework. Like a metro map guiding passengers through various interconnected stations, Wipro's framework facilitates the incremental development of the data foundation, allowing for a systematic and progressive approach towards embracing the capabilities of GenAI technologies.

The Imperative of Establishing a Robust Governance Framework

In the rapidly evolving banking landscape characterized by frequent M&A activities and increasing regulatory scrutiny, data quality stands as a non-negotiable cornerstone for sustainable growth and compliance. As a heavily regulated sector, every facet of banking, from customer segmentation to compliance reporting, necessitates a rigorous data management and governance framework. The imperative for a robust data foundation becomes even more pronounced when integrating disparate data ecosystems post-M&A, requiring meticulous attention to data integrity, consistency, and traceability.

In addition to laying a robust data foundation, governance of AI models emerges as a critical imperative for banks aiming to harness the transformative potential of GenAI. The following considerations underscore the importance of a structured governance framework tailored for Generative AI applications:

1. Information Security: To safeguard confidential information, it is paramount to restrict direct access to Generative AI interfaces such as ChatGPT. Banks should instead deploy purpose-built business applications that leverage Generative AI models within a fortified information security framework.
2. Purposeful Restriction: The ubiquitous nature of general-purpose Generative AI models, trained on vast and diverse data sets, necessitates stringent measures to ensure that responses align with specific business objectives. Banks should develop specialized applications atop Generative AI models, programmatically evaluating each request's relevance to the intended purpose before generating a response
3. Custom Training and Fine-tuning: To harness the full potential of Generative AI, banks can leverage proprietary knowledge through custom training or fine-tuning of models. This approach not only enhances transparency and credibility but also facilitates the creation of a robust framework for traceability, enabling banks to trace responses back to the source documents used for training.
4. Response Moderation: As banks integrate Generative AI into diverse use cases, from virtual assistants to marketing initiatives, implementing rigorous response moderation becomes indispensable. This involves deploying advanced content detection mechanisms and leveraging custom-trained classification models to identify and mitigate potentially harmful elements in generated responses, including plagiarism or copyrighted content, and bias.

Wipro’s Enterprise Generative AI Studio (WeGA) accelerator enables a tailored and vigilant approach for establishing guardrails to govern AI models. By adopting WeGA, a structured governance framework encompassing information security, purposeful restriction, custom training, and response moderation, banks can navigate the complexities of Generative AI effectively, ensuring alignment with regulatory mandates and driving sustainable innovation.

The Outlook for Banks and Financial Institutions

With a clear focus on building a robust data foundation and laying proper governance mechanisms, banks are well-positioned to harness the potential of the Generative AI revolution effectively.

Banks can envision a future in which GenAI models seamlessly analyze new regulatory documents in real time, automate updates to training materials, and conduct sentiment analysis on corporate customers to inform underwriting decisions. Furthermore, these models hold the promise of enhancing customer service interactions, facilitating rapid response times, and empowering representatives to address more complex queries. The evolution of virtual banking assistants, characterized by human-like responsiveness and "always on" availability, further underscores the transformative potential of GenAI.

However, it is crucial to recognize that achieving reliable GenAI capabilities transcends the user-friendly allure of platforms like ChatGPT. Building a secure, accurate, and responsible enterprise-grade GenAI function demands meticulous planning, customization, and alignment with industry-specific data and regulatory frameworks. While the journey towards realizing the full potential of GenAI in banking may be complex and nuanced, the rewards — enhanced efficiency, customer experience, and innovation — are undeniably worth the investment and strategic focus.