About Session
AI copilots can write a memo, but they can’t close your books. Modern finance demands reliability, not creativity and that’s where most copilots fail. The latest research from OpenAI, Anthropic, and Google shows that LLMs still hallucinate up to 8% of factual outputs even under instruction tuning. In accounting, that’s 8% too much. In this talk, Anji Ismail, Co-Founder and CEO of Finnt, unveils how a new wave of deterministic by default AI architecture blends large language models with strict control layers, retrieval, rules, and validation to guarantee consistent, auditable results. For the 1% of messy reality, novel vendors, edge cases, we route through a governed exception layer, the outcome is consistent outputs with proofs: lineage, checksums, and variance analysis so controllers can trust, trace, and explain every number. Learn how this hybrid architecture transforms reconciliations, accruals, and journals from probabilistic guesses into reproducible financial truth.
Learning Objectives
- Identify why prompt copilots fail regulated workflows and how deterministic-by-default systems avoid it.
- Understand how to deal with exceptions in a governed pattern: bounded inference, validations, rollbacks, and tolerance gates.
- See architectures that make reconciliations, accruals, and journals reproducible, explainable, and audit-ready.
