Ledger Reconstruction for Investigations for Internal Audit Managers with AuditBoard

AuditBoard Internal Audit Manager Internal Audit

The Problem

Internal Audit forensic engagements start with incomplete books, missing backup, and thousands of transactions to reclassify by hand before any analysis can run.

What We Build in AuditBoard

AI ingests bank feeds, statements, and AuditBoard data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry. Purpose-built for teams running AuditBoard — uses the native API or agent integration so nothing leaves the system of record.

AuditBoard Integration Approach

1

Audit your AuditBoard configuration

We map the specific AuditBoard objects, custom fields, and workflows the automation needs to touch for your internal audit practice.

2

Build on the AuditBoard API or agent

Integration happens inside AuditBoard — no data leaves the system, no parallel tool for your team to learn, no license changes.

3

Human-in-the-loop handoff

Every automation routes exceptions back to a human in AuditBoard with enough context to act — AI handles the 80%, your team owns the judgment calls.

See this running in your AuditBoard instance

30-minute call. We'll look at your actual AuditBoard setup and show exactly how this workflow fits.

More About This Workflow

Ledger Reconstruction for Investigations for Internal Audit Managers

AI ingests bank feeds, statements, and AuditBoard data, rebuilds a clean transaction timeline, and flags suspicious patterns — investigators start at analysis, not data entry.

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