Bloomberg says a poll of more than 100 senior decision-makers at its AI in Finance Summit in London reveals that concerns about inaccurate outputs are the single biggest barrier to the adoption of artificial intelligence in financial services.
Half of respondents (50%) identified hallucinated facts or numerical errors as their primary concern when using AI in financial markets, while a further 27% pointed to a lack of explainability. Together, these findings highlight the importance of accuracy and transparency as AI tools become more embedded in financial decision-making.
These concerns are directly shaping how financial professionals evaluate and adopt AI, says Bloomberg. When asked what gives them the most confidence in AI systems, respondents pointed to features that enable outputs to be verified and controlled: 32% selected AI that attributes its sources, 30% highlighted built-in error checking, and 25% chose human oversight. By contrast, only 9% said sophisticated language and reasoning was the most important factor, and 5% said fast AI outputs.
“The results suggest that trustworthiness depends on whether an AI’s outputs can be interrogated and validated, said Amanda Stent, head of AI strategy & research at Bloomberg. “Solving this challenge depends on attribution, transparency and the quality of the underlying data so outputs can be traced to their sources, validated for accuracy, and confidently used in decision-making. This is exactly what is shaping Bloomberg’s approach to AI development. We are focused on combining high-quality, trusted data with AI that is embedded into real workflows, and designed with accuracy, transparency and control at its core.”
While concerns around accuracy remain front of mind, there is strong appetite for more advanced applications. Nearly two-thirds of respondents (66%) identified full workflow AI assistants as the most exciting next development in financial services, far ahead of other use cases such as personalised portfolio insights (9%) or no-code quant tools (12%).
This points to a clear direction for the industry: growing demand for end-to-end AI integration, but only where systems can deliver reliable, verifiable outputs at scale.
As financial institutions continue to adopt AI, Bloomberg says the findings highlight a defining challenge for the industry: ensuring that innovation is matched by the accuracy and accountability required for high-stakes financial decisions.
At the event, Bloomberg also unveiled a 2026 roadmap for ASKB, its powerful new conversational AI interface, now in beta, that redefines how investors interact with the Bloomberg Terminal of the future to augment their entire investment process. The roadmap outlines the evolution of ASKB into a deeply integrated engine for institutional intelligence using Bloomberg’s AI that is grounded in trusted data and content, embedded in firm-wide workflows, and designed for accuracy and control following Bloomberg’s Responsible AI principles.
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