AI Investigation
Related terms
Category
Financial Reporting
AI investigation is the automated analysis of account reconciliation variances, anomalies, and reconciling items using artificial intelligence. It involves an AI agent examining supporting data, identifying root causes of discrepancies, generating explanatory narratives, and recommending corrective actions to accelerate the resolution process.
Why it matters
Investigating reconciliation discrepancies is one of the most time-consuming and skill-dependent tasks in the financial close. When a preparer encounters an unexplained variance, they must pull data from multiple sources, trace individual transactions, cross-reference prior periods, and determine whether the difference represents a timing issue, a posting error, a missed accrual, or something more systemic. This investigative work requires deep familiarity with the account, access to relevant data, and the analytical skill to connect disparate pieces of information.
The challenge is that investigation expertise is unevenly distributed across accounting teams. Senior accountants can quickly diagnose common issues, but junior staff may spend hours chasing down a variance that an experienced preparer would resolve in minutes. This bottleneck slows the close and concentrates risk on a small number of individuals whose knowledge is not codified or transferable.
AI investigation addresses this gap by encoding investigative patterns and making them available to every preparer. The AI examines the same data sources a human investigator would, including transaction details, historical trends, peer comparisons, and prior-period resolutions, and produces a structured analysis that identifies the most likely root causes of each discrepancy.
Automated investigation agents address this knowledge gap by encoding expert investigative patterns and making them available to every preparer on the team.
How Arvexi handles this
Arvexi's Investigation Agent operates within Arvexi Cortex and activates automatically when a reconciliation falls below the confidence scoring threshold. The agent pulls transaction-level data from connected sources via the 7 data integration tools, analyzes variance patterns against historical baselines, and generates a narrative investigation report explaining each component of the discrepancy.
The investigation output is embedded directly into the reconciliation work paper, providing preparers with a structured starting point rather than a blank page. Preparers can accept, modify, or override the AI's findings, and each interaction trains the model to improve its analysis for future periods. The result is faster resolution times and more consistent investigation quality across the team.