Use case · Finance & Audit · Finance
Financial Anomaly Detection
This proposed application uses BrainShift Studio and Agentic Workflows to support financial anomaly detection for finance and internal audit teams. Machine learning would identify unusual transaction patterns and organize evidence for review, without treating an anomaly score as proof of fraud. This high-complexity use case requires careful data mapping, model validation, and approval design.
This page describes a proposed application of the BrainShift platform. It is not a description of a specific customer deployment.
Business challenge
Ledger entries, supplier invoices, payment batches, and vendor changes often sit in separate systems. Reviewers must reconcile identifiers and posting dates before deciding whether a transaction is a duplicate, a timing difference, or a control exception.
Fixed thresholds can miss unusual combinations of otherwise ordinary attributes while flagging legitimate seasonal activity. Finance teams need explainable investigation leads, reliable source references, and a documented distinction between model suspicion and confirmed findings.
How BrainShift solves it
BrainShift Studio would support a custom detection application that combines machine-learning scores with configured financial controls. Candidate signals include atypical posting times, unusual account combinations, repeated invoice amounts, and payments following supplier bank-detail changes. Models would be evaluated against historical data and reviewed cases before production use.
Agentic Workflows would coordinate evidence collection, case assignment, clarification requests, and approval checkpoints. Each case would show relevant transactions, contributing signals, and missing information so a finance reviewer can investigate, dismiss, or escalate it.
How it works
Step-by-step workflow
- 01
1. Define review scope
Finance and audit owners select entities, transaction types, materiality rules, review responsibilities, and prohibited automated actions.
- 02
2. Prepare transaction data
Map ledger, invoice, supplier, and payment fields; validate currencies, identifiers, completeness, and reconciliation before scoring.
- 03
3. Establish detection baselines
Build peer groups and seasonal baselines, then validate candidate models against known exceptions and legitimate unusual transactions.
- 04
4. Score and explain
Rank transactions using anomaly signals and control rules, attaching source references and explanations of contributing factors.
- 05
5. Investigate with approval
Route cases to authorized reviewers, collect explanations, and require explicit approval before payment holds, accounting adjustments, or escalations.
- 06
6. Monitor and refine
Track case dispositions, false positives, and drift; submit threshold or model changes for validation and owner approval.
BrainShift components
What each module does
BrainShift Studio
Build the transaction-scoring application, evidence summaries, and reviewer-facing explanations using approved financial datasets.
Agentic Workflows
Coordinate evidence requests, investigation queues, reviewer assignments, approval gates, and case closure.
Enterprise integrations
Systems it can connect to
Configurable integration options, confirmed during implementation.
- SAP: Configurable access to ledger entries, supplier records, invoices, and payment data.
- Oracle ERP: Alternative configurable source for accounting transactions and financial master data.
- SQL Server: Connect approved reporting tables and historical case dispositions.
- Document management: Retrieve authorized invoice images, contracts, and supporting approvals.
- Identity providers: Connect enterprise identities and reviewer group assignments.
Data sources
- General ledger journals and account mappings
- Accounts payable invoices and credit notes
- Payment batches and settlement records
- Supplier master records and change histories
- Approval logs and historical investigation outcomes
Business outcomes
Potential benefits
More focused review
Risk-ranked cases could help reviewers prioritize transactions needing investigation.
Stronger evidence packages
Linked records could reduce manual searches across finance systems.
Broader exception coverage
Pattern-based signals could reveal combinations missed by fixed thresholds.
Consistent case handling
Defined routing could clarify ownership and approval responsibilities.
Traceable decisions
Recorded dispositions could support audit review and controlled model refinement.
Suggested KPIs
Security and deployment
Design deployment around sovereign, on-premises, or private-cloud requirements. Configure SSO, role-based access, entity-level permissions, and audit trails for data access, scoring, evidence changes, and approvals. Apply retention rules and restrict sensitive supplier banking details to authorized reviewers.
Human oversight
Finance retains responsibility for case conclusions. Anomaly scores are investigation leads, not fraud determinations. Payment holds, journal adjustments, external reporting, and other high-impact actions require authorized human review and approval; segregation of duties should govern routing and closure.
FAQ
Frequently asked questions
Which payments look unusual this week?
The system would rank authorized payment records and show contributing signals with supporting references.
Investigate this possible duplicate invoice.
The workflow would compare supplier, amount, invoice identifiers, and payment status, then assemble evidence for review.
Can you stop this suspicious payment?
The workflow would request authorized approval; a model flag alone would not trigger a payment hold.
Does this replace financial auditors?
No. It would support investigation prioritization and evidence gathering; auditors and finance owners retain judgment and accountability.
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