According to Gartner, around 22% of banks and investment firms have already implemented Generative AI (GenAI) solutions. Another 22% are piloting use, and 30% will be actively implementing the technology well before the end of 2024. As GenAI evolves, so do the associated value propositions. GenAI will add value to existing AI use cases while creating new ones. The technology already adds value to customer support, marketing, and sales. It also contributes to hyper-personalized financial products and brings new capabilities that aid in designing personal financial plans and investment strategies. Emerging value propositions for GenAI include data and analytics, code generation, synthetic data generation, compliance and reporting, fraud monitoring, and management.
But how and where do banking and financial services (BFS) companies begin? The 5-step approach below is a strong starting point for implementing GenAI solutions at scale while meeting new regulatory requirements.
Getting Started with GenAI in BFS
GenAI-based tools will propel employee efficiency everywhere in financial organizations. For firms that took a wait-and-see stance during the initial phase of GenAI hype, the five-step approach below will provide a sound strategy for achieving real business value through GenAI.
1. Get everyone experimenting
Senior leaders should encourage their teams to identify new use cases and experiment to understand the capabilities and potential of GenAI. GenAI interaction may seem user-friendly and easy, but it requires experimentation and exploration to yield the best results, and all users will face a learning curve when it comes to prompt engineering. Providing room for experimentation allows employees to learn how GenAI works, understand how its capabilities can add value in the context of a particular functional role, and explore its advantages over other digital tools. Getting every functional team involved will benefit each team and the entire business.
2. Gather use cases and data across business and IT functions
GenAI needs detailed data on processes and functions to deliver exceptional outcomes. Adding the optimal training data to the model calls for close collaboration between business and IT. GenAI can bring robust new data and analytics tools to the entire company if leadership is committed to breaking down silos and enabling fluid data pipelines.
3. Reimagine processes for transformative outcomes
It’s time to rethink how businesses work. GenAI will impact all business functions horizontally. Business functions should be re-imagined. By always keeping GenAI in mind when thinking through changes to organizational structures and emerging business imperatives, financial firms will be able to produce game-changing outcomes that drive a competitive edge.
4. Redefine data strategy
The accuracy and dependability of GenAI models depend on input data. The large language models (LLMs) that power GenAI rely on large volumes of structured and unstructured data. Banks have proprietary data that the LLMs can leverage to generate insights, recommendations, and decisions. Banks must redefine their data strategy to guarantee that the outputs are explainable and interpretable and comply with the growing number of data regulations.
5. Implement guardrails
GenAI benefits don’t come without associated risks. Explainability and interpretability are a concern, as the models use neural networks with billions of parameters to produce results that are sometimes neither repeatable nor explainable. These issues highlight the need for accuracy and reliability of output; after all, a single prompt can yield multiple responses. Externally, emerging AI regulations will insist on additional guardrails in the training and post-implementation phases. Frameworks to sanitize training data to promote fairness and eliminate bias will be crucial. Human validation of results is required to ensure the desired output. All financial institutions must synthesize these concerns into a responsible GenAI/LLM strategy.
For example, GenAI tools can generate code with high precision while simultaneously creating and producing test cases and synthetic test data covering wide-ranging test scenarios for validation, saving precious time and effort in the software development cycle. This intelligent automation promises to address the problem of test data from volume and test scenario perspectives, which is usually an effort-intensive task. Banks and financial institutions that seize the moment with GenAI will gain a competitive edge through faster time-to-market for new products.
Business models are also changing fast. Fueled by new digital tools, open finance has catapulted the growth of embedded financing activities. With its vast computing capabilities, GenAI will bring advantages in fostering compelling customer experiences. GenAI will enable firms to build client relationships through persona-based approaches using psychometric models, providing real-time individualized recommendations matching the customer’s personality, lifestyle, interests, preferences, and major life events — hyper-personalization at its best.
Innovative leaders in banking and financial services have high expectations for GenAI. Goldman Sachs research indicates that GenAI could drive $7 trillion in global GDP growth over the next decade. The banking sector will likely be one of the most significant beneficiaries of GenAI-driven business value, and the first movers will reap the most significant benefits.
