AI analyzes payment patterns, invoice documents, and vendor data 24/7. Flags statistical anomalies, duplicate vendors, altered documents, and suspicious behaviors that warrant investigation.
Using only bank transactions, accounting data, and invoice documents, TieStream identifies suspicious patterns and control weaknesses that warrant investigation.
What it tests: Are there multiple vendor records for the same entity?
Detects:
What it tests: Do payment patterns suggest manipulation?
Detects:
What it tests: Do payment amounts follow expected statistical patterns?
Detects:
What it tests: Are invoice documents authentic and unaltered?
Detects:
What it tests: When are transactions processed?
Detects:
What it tests: Are required documents present?
Detects:
These are real patterns that warrant investigation. TieStream doesn't prove fraud—it identifies red flags that need human review.
Pattern: Multiple vendor records with similar characteristics but different names.
TieStream flags:
What it could indicate: Splitting payments to avoid approval thresholds, or legitimate vendor name change that needs cleanup.
Pattern: Invoice document shows signs of modification.
TieStream flags:
What it could indicate: Invoice tampering, or vendor correcting a billing error (needs verification).
Pattern: Payments consistently just under approval limit.
TieStream flags:
What it could indicate: Intentional splitting to avoid oversight, or coincidental timing of legitimate purchases.
Pattern: Unusual frequency of exact round-dollar amounts.
TieStream flags:
What it could indicate: Shell company / fake vendor fraud, or legitimate retainer-based services billed monthly.
Pattern: Payments processed during unusual times when oversight is weak.
TieStream flags:
What it could indicate: Exploiting reduced oversight periods, or legitimate catch-up work by dedicated employee.
Pattern: Multiple vendors share same bank account.
TieStream flags:
What it could indicate: Shell companies set up to split payments, or subsidiaries of same parent company (needs verification).
AI extracts data from bank transactions, accounting software (invoices, POs, vendor records, GL entries), and invoice documents. Builds complete picture of payment patterns and vendor relationships.
Applies multiple analytical techniques: Benford's Law, threshold clustering, round-dollar frequency, payment timing patterns, vendor relationship mapping.
Analyzes invoice PDFs for metadata anomalies, font inconsistencies, modification timestamps, and image quality variations that suggest alteration.
Each anomaly gets a risk score based on severity and frequency. High-risk patterns trigger immediate alerts. Medium-risk patterns queued for periodic review.
AI provides the evidence and risk assessment. You investigate the flagged patterns with your team to determine if they represent fraud, policy violations, or legitimate business activity that needs clarification.
TieStream detects fraud patterns in real-time, before payments process. Typical detection time: 0-3 days.