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This paper analyzes why enterprise AI deployments stall in regulated firms, proposing a production bar that includes accuracy, reproducibility, groundedness, and detectability. It measures the human review burden across model and tool configurations, showing that confidence signals and source citation can cut review from 100% to 49% but self-verification adds latency without improving error tolerance.
TanML is an MIT-licensed automated model-validation toolkit for tabular machine-learning models, designed for regulated environments. The developers seek feedback on its features and reports.
Summarizes a deterministic, constraint-based approach for building AI agents in regulated finance, where the LLM only generates prose, numbers are cryptographically sealed, and auditability is ensured through separated layers.
OpenAI Academy launches a dedicated Financial Services resource hub with curated prompts, GPT templates, and guidance to help banks, asset managers, and insurers evaluate, deploy, and scale AI in regulated environments.
Erste Group Chief Platform Officer and COO Maurizio Poletto shared in OpenAI's Customer Ignite Talk their experience of adopting AI at scale in a regulated banking environment, emphasizing connecting customer data from day one, embracing iterative trial and error, and striving to serve the 80% of customers who never receive financial advice.