Based on conversations with over 50 leading financial institutions across North America and Europe, I believe—with cautious optimism—that with LLMs, this time really couldTo understand why, consider the reasons behind ‘traditional’ AI’s modest impact on the industry: lack of adequate or reliable data, talent constraints, cultural barriers or resistance to change, and “last-mile” operationalization challenges .•LLM applications work with unstructured data.
•OpenAI’s decision to make ChatGPT widely available has democratized AI. Rightly or wrongly, many business leaders and their staff now feel confident enough about the technology to reimagine their products and processes using LLMs, making adoption much easier. Second, LLMs can expand the applicability of AI to areas that have been seen to be unsuitable for automation in the past. For instance, LLMs can be used to read and write software code. They can help generate reports or summaries in standard formats, such as initial drafts of model validation, customer due diligence or financial crime investigation reports.
add new dimensions to these risk considerations. This includes concerns around inaccurate or false outputs , security and privacy implications of using models built and owned by third parties, uncertainty over intellectual property rights, reputational risk of customer-facing AI, and the inherent risk of unjust bias in models that have been trained on unrepresentative, public data sets.
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