He also shares three major challenges banks will face as they adopt generative AI. The first is making huge datasets visible, manageable and available for the data collection and preparation phase of AI. “Data is the fundamental DNA of intelligence. Most financial institutions have geographically distributed data that are both on-premise and [in the cloud]. If you have accessibility or availability issues with your data, you will not create intelligence.”
Financial institutions in Asia are banking on generative AI, with the International Data Corp expecting the sector to spend US$4.3 billion ($5.87 billion) on generative AI by 2027. The technology will be used to enhance operations efficiency, automate repetitive tasks, and optimise back-office processes such as fraud detection and the creation of intricate documents.
Unlocking the vast potential of generative AI requires the support of a robust and resilient data infrastructure. “Generative AI models consume massive amounts of unstructured data, typically at the terabyte level. This is the consumption of information for training the models. By doing so, it increases the accuracy and ultimately intelligence [of the generative AI solution like ChatGPT]. And generative AI needs to generate those accurate results quickly,” says Brian Gallagher, executive consultant at Global Solutions. He was speaking at the Innovative Data Infrastructure Forum, which is part of the Huawei Digital and Intelligent Apac Congress in Bangkok, yesterday.
