Context: In Insurance, reserve accuracy isn't won in spreadsheets — it is won in moments when claims behavior starts to shift and someone chooses whether to act. When reserves drift, it's rarely because the math failed.

Challenges: Claims evolve faster than reserving action. Loss behavior shifts between review cycles, and adjustments queue up for governance forums, so by the time reserves are revisited, exposure has already settled into the books.

Solution: The Reserve Accuracy Guidance Agent, an AI agent built using Microsoft Copilot Studio, watches for early deviation — unexpected development, behavior shifts, concentration changes — and calls for action immediately rather than waiting for quarter-end.

Impact: Insurers following this approach see reserve accuracy stabilize as fewer surprises are allowed to grow, with confidence improving because decisions arrive when exposure is still adjustable.