Use case · Finance & Audit · Audit
AI Audit Evidence Analysis
This proposed application supports Audit teams reviewing financial transactions, control documentation, and supporting records. Using Document AI, BrainShift could assemble traceable evidence packs and draft observations while auditors retain responsibility for scope, materiality, conclusions, and reporting.
This page describes a proposed application of the BrainShift platform. It is not a description of a specific customer deployment.
Business challenge
Evidence arrives as ledger exports, scanned invoices, bank statements, contracts, and email approvals. Different formats, missing references, and inconsistent naming make it difficult to establish whether documents support the same transaction or control assertion.
Auditors must distinguish missing evidence from control failures and reconcile conflicting records without losing provenance. Manual copying into workpapers can obscure document versions, while Arabic and English records add interpretation requirements.
How BrainShift solves it
BrainShift Studio would configure an audit analysis application around the engagement’s approved scope, control matrix, and testing procedures. Document Intelligence would extract Arabic and English text, tables, and relevant visual content, preserving source references for review.
Agentic Workflows would coordinate extraction, matching, exception assessment, and reviewer handoffs. The application could propose evidence gaps and draft workpapers, but would not treat an automated flag as a finding or issue an audit conclusion without human approval.
How it works
Step-by-step workflow
- 01
1. Define the audit scope
The audit lead approves the period, entities, control assertions, sampling approach, and evidence requirements before analysis begins.
- 02
2. Collect authorized evidence
Import approved documents and exports, recording source locations, versions, owners, and engagement access restrictions.
- 03
3. Extract and normalize
Read tables, amounts, dates, signatures, and approval references; flag uncertain extraction and retain links to source pages.
- 04
4. Match and test
Compare evidence against approved procedures, linking invoices to ledger entries and approvals while flagging unmatched or contradictory records.
- 05
5. Prepare review materials
Draft workpapers with test criteria, evidence references, exceptions, and open questions; separate documented facts from provisional interpretations.
- 06
6. Approve and finalize
Auditors resolve flags, request further evidence, and approve findings before workpapers are finalized or any consequential action proceeds.
BrainShift components
What each module does
BrainShift Studio
Build the audit application, evidence-grounded prompts, and engagement-specific analysis logic.
Document Intelligence
Interpret Arabic and English audit records, including tables and visuals, with source-linked extraction.
Agentic Workflows
Orchestrate evidence processing, exception triage, reviewer assignments, and approval checkpoints.
Enterprise integrations
Systems it can connect to
Configurable integration options, confirmed during implementation.
- SAP: Configurable retrieval of authorized ledger and purchasing records.
- SharePoint: Configurable access to engagement folders and supporting documents.
- SQL Server: Configurable access to approved transaction datasets and control registers.
- Document management systems: Configurable retrieval of retained evidence and document version metadata.
- Identity providers: Configurable authentication and mapping of audit engagement access.
Data sources
- General ledger extracts and journal entries
- Invoices, purchase orders, and payment records
- Bank statements and reconciliation schedules
- Policies, control matrices, and testing procedures
- Approval records and prior audit workpapers
Business outcomes
Potential benefits
Traceable workpapers
Source-linked drafts could make evidence easier to verify.
Earlier gap identification
Missing approvals or supporting records could surface before review deadlines.
Focused exception review
Grouped discrepancies could help auditors prioritize investigation.
More consistent testing
Approved procedures could be applied more consistently across evidence sets.
Less repetitive preparation
Automated extraction could reduce manual transcription and document cross-referencing.
Suggested KPIs
Security and deployment
The proposed design should use a sovereign deployment model, including on-premises or private cloud options, with SSO, role-based access, and engagement-level segregation. Audit trails should capture evidence access, analysis versions, reviewer decisions, and approvals. Retention and model-processing boundaries must follow institutional policy.
Human oversight
The audit lead owns testing criteria and materiality judgments. Reviewers validate extraction, investigate conflicting evidence, and approve findings, external requests, report release, and consequential system changes. Missing evidence remains an unresolved gap rather than proof of noncompliance; signature detection does not establish authenticity.
FAQ
Frequently asked questions
Which sampled payments lack approval evidence?
The system compares sampled payments with supplied approval records and lists unmatched items with references for auditor validation.
Compare this Arabic invoice with its ledger entry.
It extracts invoice fields, compares amounts and dates, and highlights discrepancies or uncertain readings alongside source evidence.
Draft a workpaper for this reconciliation test.
It prepares a procedure-based draft with evidence links, exceptions, and unresolved questions for reviewer approval.
Can the system determine whether an audit opinion is justified?
It can organize supporting evidence, but qualified auditors retain responsibility for professional judgment and the audit opinion.
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