This post was originally published on Fin Extra
How important are open source models in overcoming the challenges banks face regarding data security and compliance when adopting AI? In what ways does generative AI bring business value, and how can it be differentiated from traditional AI? With efficiency and governance being key concerns for banks, how can synthetic data help to train models while balancing AI infrastructure with customer value? How crucial are new regulations like the EU AI Act in light of the need for proper data and model management? What role can Agentic AI or AI Agents play in revolutionising banking operations while ensuring compliance and safety
Despite Open AI’s ChatGPT being launched two years ago and mainstream use of LLMs ensuing, many banks and other organisations remain in the early stages of their use of large language models. They are identifying the controls needed to effectively manage their risk, but they are also using new methods to improve their accuracy in real world scenarios. An example is using Retrieval-Augmented Generation (RAG), which optimises the output of a LLM by referencing an authoritative knowledge base before generating a response.
In turn, RAG enhances large language models by improving their accuracy and reducing the
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