Many financial authorities worldwide are exploring the potential of generative artificial intelligence (Gen AI) to enhance their supervisory functions, but several barriers are slowing adoption.
According to a new stocktake report, while there is growing interest in using gen AI for supervisory purposes, concerns over IT infrastructure, data security, and skill shortages remain significant obstacles.
The report finds that most financial authorities are seeking to leverage gen AI to improve how they locate and process information, aiming for greater efficiency in handling growing volumes of data. However, their efforts are often hindered by legacy IT systems that struggle to support the demands of modern AI technologies. Alongside these technical limitations, data security risks and a lack of specialised expertise are also slowing progress.
Gen AI applications currently in use by supervisory authorities tend to focus on basic document processing tasks, which include functions such as summarising and extracting information from standard documents. Development work is ongoing across three key categories identified by the report: basic document processing, knowledge management, and document review. Although live applications are mostly concentrated in the first category, experimentation and development are actively progressing in the areas of knowledge management and document review as well.
The report highlights that one of the most pressing challenges financial authorities face is user acceptance. Some supervisory staff remain cautious about relying on AI-generated outputs, especially as these systems occasionally produce inaccuracies. The risk of incorrect or misleading information becomes even more concerning as authorities consider using gen AI for more complex supervision tasks.
As financial authorities continue their experimentation with gen AI, the complexity of its applications is expected to grow, which will likely amplify existing challenges. Successful integration will depend not only on upgrading technical infrastructure and enhancing data security protocols but also on addressing the human factors that influence the adoption of emerging technologies.
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