By Abi Wareing, Senior Manager, Airwalk Reply.

The McKinsey Global Institute (MGI) estimates that generative AI (gen AI) could inject between $200 billion and $340 billion into the global banking industry yearly, representing 9% to 15% of operating profits. What we are seeing in this domain is AI’s growing ability to boost productivity, streamline operations, and drive innovation, creating significant economic benefits. AI is no longer a distant concept; it’s now actively reshaping the financial sector, influencing everything from traditional institutions to the latest Fintechs. There’s now increasing recognition of AI’s transformative potential, with particular focus on gen AI.

Financial services institutions (FSIs) are automating complex tasks like detecting fraud and enhancing risk management processes, while improving productivity by reducing time spent on repetitive tasks like loan eligibility analysis or market trend forecasting. Beyond efficiency, gen AI is catalysing innovation by unlocking entirely new possibilities. Firms can now develop highly personalised financial products tailored to individual customer needs and simulate complex market scenarios to refine investment strategies. Additionally, its ability to generate creative solutions, such as optimised portfolio structures or new credit scoring methodologies, is paving the way for breakthroughs in how financial products are designed and delivered.

Strategic implementation is essential to harness the opportunity here. FSIs must move beyond superficial applications if they want to fully capitalise on AI’s potential. Integrating AI requires a broader, more holistic approach, ensuring the technology is seamlessly woven into the fabric of existing operations. The real challenge lies in navigating risks while embedding these advanced capabilities into business systems without causing too much disruption.

The engine behind financial transformation

The significant potential of AI to revolutionise financial services is powered by foundational and large language models. These highly sophisticated neural networks, trained on vast datasets, have revolutionised how we approach machine learning. Models like FinGPT and BloombergGPT are already making impressive strides in the finance sector, offering solutions that are finely tuned to the needs of the industry. As mentioned, the success of these models ultimately depends on how seamlessly they are built into existing processes.

To implement AI smoothly, FSIs can use techniques such as Retrieval Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF). RAG enhances the accuracy and relevance of AI models by incorporating data from internal or external sources, while RLHF makes AI more intuitive by refining models based on human feedback. These techniques are essential for continuously improving AI-driven financial solutions, helping them to evolve and remain relevant in a rapidly changing environment.

Taming AI with intelligent agents and guardrails

If large language models are the engines in our AI equation, then intelligent agents are the drivers. These systems interact with their environment, make informed decisions and execute tasks to achieve specific goals. In other words, intelligent agents keep AI systems on track and focused on their objectives. By breaking down complex processes into smaller, more manageable tasks, intelligent agents help ensure that each part of the system functions properly.

Despite the immense potential of generative AI, its inherent risks must be managed carefully. Issues like inadequate training data, incorrect assumptions and biases in AI models can lead to unethical outcomes and inaccurate results, such as data hallucinations. Additionally, gen AI models often operate as “black-box” opaque systems, making it challenging to explain their outputs or decisions – this lack of transparency can undermine trust and complicate regulatory compliance. FSIs must apply robust monitoring systems and explainability tooling and establish ethical guardrails to mitigate these risks. These measures are essential for maintaining the integrity of AI systems and ensuring they operate within ethical and regulatory boundaries.

Handling the speed of AI development

FSIs must adopt a modular and adaptable approach to integrate AI into their digital infrastructures to cope with the breakneck speed of the technology’s evolution. Modularity allows for the seamless adoption of new technologies without requiring a complete system overhaul. Instead of starting from scratch, institutions can build on their existing systems, gradually adding microservices as needed. This approach preserves the value of prior investments and ensures a smoother transition to AI-enhanced operations.

By scaling AI implementation, financial institutions can test and refine individual AI components at each implementation stage. This method minimises downtime and improves system resilience, allowing institutions to quickly adapt to new technological advancements and maintain a competitive edge in the marketplace.

Redefining the future of finance with AI

It is now business-critical for financial services firms to achieve greater efficiency, reliability, and flexibility to meet the evolving needs of their customers and stay competitive. However, the true competitive edge will belong to those firms that move beyond merely transforming existing workflows and fully embrace AI-driven innovation to reimagine their offerings and business models.As AI continues to develop, its impact on the financial sector will only deepen. According to the World Economic Forum, AI may have the power to identify the patterns that predict financial crises before they happen and take pre-emptive action to mitigate or even avert them. Automated crisis prevention represents a revolutionary shift in how the industry manages risk. Gen AI, still in its infancy, is already a powerful tool for the present – but as it matures, it is poised to reshape and define the future of financial services.