TL;DR
Manual audits stitch Excel, email, and shared folders. Work scatters, evidence drifts, and reviews take longer than they should. Finspectors.ai centralizes the flow: 100% GL screening with explainable flags, structured evidence packets, and a single review trail. You keep auditor judgment, but gain speed, clarity, and consistency.
Manual (Excel + Email + Folders)
Finspectors.ai
Population coverage
Sampling, ad-hoc filters, fragile formulas
Full-file screening with control points + model signals
Evidence handling
Email ping-pong, version drift
PBC → upload → verification → hashed evidence packets
Explainability
Free-form notes
Structured reasons (which rule/model fired and why)
Review trail
Comments across files
Unified log: who flagged, why, what changed, when
Seasonality & scale
Manual trackers, night merges
Queue-based triage, utilization views
Consistency
Varies by reviewer
Standardized rules + configurable thresholds
What really changes in your day-to-day
From sampling to screening. Every JE or AP line is triaged first, so reviewers zoom straight into high-risk slices.
From hunting attachments to assembling evidence. Requests, uploads, validations, and packetization sit in one flow that is easy to re-perform.
From “why was this flagged?” to provable reasons. Each flag carries the triggering rule or model rationale, plus links to replicate the check.
From scattered comments to a single trail. Approvals, thresholds, and overrides are captured in one audit-ready log.
Where manual still fits
Tiny, low-risk engagements where setup costs outweigh gains.
One-off edge cases with bespoke evidence that will not repeat.
Rough planning notes before the GL lands; move into platform once data arrives.
Implementation guardrails (avoid the common pitfalls)
Define success upfront. Pick 2 - 3 measurable outcomes that matter: review time per 1k rows, exceptions closed on first pass, or rework rate.
Standardize inputs. Lock column names, types, and date formats for your GL extracts and evidence lists to reduce friction.
Tune thresholds responsibly. Start with conservative settings, document changes, and keep a change log that reviewers can see.
Separate detection from decision. The platform surfaces anomalies; auditors still own materiality, sampling, and conclusions.
Prove reproducibility. Save the logs, rule versions, and evidence packet hashes so any reviewer can re-perform the work.
