Why human centaurs and data provide the advantage in the AI age
by Sergio Gago, Managing Director, AI and Quantum Computing at Moody’s Analytics
Generative AI and large language models (LLMs) like ChatGPT are poised to revolutionise aspects of finance, from risk management to investment all the way to customer service. While risk and opportunity abound – it’s essential to understand how these technologies work, where they fall short and where they can augment humans as ‘co-pilots’.
While other forms of AI are not new; they previously involved large investment in scarce specialist skills. Now, ChatGPT has brought about a “no-code” version of machine learning, and anybody can create their own classifier, fraud identification machine, or data normalizer, without specific skills. We call it ‘prompt engineering’, or ‘knowing how to ask a chatbot the right questions’.
Note, the essential skill remains knowing how to make the right request – humans remain crucial for providing context and creativity, not to mention validation and verification of data. Here, we lean into the concept of “centaurs” – human-AI teams as coined by the chess master Gary Kasparov. By leveraging human intuition and creativity combined with AI’s processing power, centaurs have the potential to outperform purely human or machine-driven solutions.
For example, an investment manager can utilise AI for real time updates but relies on human expertise for final decisions. A customer service agent can produce fully customised responses to users based on the whole history of that relationship, in a way a human could not.
To effectively integrate LLMs into their processes, companies must be systematic – first identifying specific use cases where AI can add value, using easily available LLM models, while also investing in research and development to fine-tune AI models for their requirements. Finally, deployment requires ongoing monitoring and maintenance to ensure optimal performance and adaptability.
Despite their promise, generative AI models come with a few critical challenges. Ensuring these models produce accurate and reliable results is essential for building trust in their capabilities. ChatGPT is famous for its “hallucinations” – the technical name for when a bot gives a random answer with 100% confidence, but absolute inaccuracy.
Bias seen in training data can also lead to unintended consequences when applied in real-world situations. On top of that, existing models are trained with public content up to a certain date (2021 for ChatGPT), so you can’t ask questions about recent events or any piece of information that is licensed or behind a paywall.
The rise of generative AI has sparked concerns about job displacement- but history shows us that technology also creates opportunities. The key lies in embracing new skills and taking an agile approach – every team member should have a ‘hacker’ mindset, where the ability to innovate is encouraged.
As generative AI becomes more sophisticated, we may see the emergence of an “everywhere co-pilot” strategy: individuals will have access to personalised AI agents that provide real-time guidance and support across various aspects of their lives – both personal and professional. Such a ubiquitous presence has the potential to enable humans to make more informed decisions and optimise productivity.
But, don’t forget that anything one company is doing to find efficiencies through generating text are likely to be matched by competitors. The real game changer will be user-facing use cases in which a company’s competitive advantage can be enhanced through Generative AI.
This could be in the form of proprietary data assets. Data provides the essential fuel for training and refining AI models. Firms that can harness this resource effectively will be well-positioned for success in an increasingly competitive landscape.
LLMs will be a commodity – some will be small, domain level agents, others will be generic and multipurpose (like ChatGPT). If all these models feed on real, verifiable data, this will be the real trade in the “AI economy”.
As generative AI continues to evolve, its impact on the financial industry is poised to be transformative. By adopting a human-centric and data driven approach, companies can leverage these advancements while mitigating potential risks. The future belongs to those who embrace this powerful synergy between humans and machines, unlocking unprecedented opportunities for growth and innovation.