Behavioral Relationship Intelligence for Lenders and Banks
The layer that detects coordinated behavior, not just connections. Catch fraud rings before funding, not after losses.
71% of Fraud
Is Organized Networks
Cross-Borrower
Relationship Graph
3-Second
API Response
Every Fraud Tool Evaluates Applicants One at a Time. Coordinated Fraud Requires a Different Approach.
71% of fraud is now organized networks. Existing tools check identity and documents — not relationships. Lenders are blind to inter-borrower connections until it's too late.
The Coordinated Fraud Problem
8-15% losses in SMB/MCA lenders
- Fraudsters create "independent" entities that are secretly connected
- Existing tools check identity and documents — not relationships
- Lenders are blind to inter-borrower connections until it's too late
- Every tool validates identity. None validate coordinated behavior
Detect Coordinated Behavior Across Entities
Syval identifies inter-borrower money flows, shared counterparties, coordinated timing patterns, and entity relationships masked by EIN/name variations.
Stop Fraud Rings Before They Extract Millions
Real case: A lender lost $10M to a single fraud ring of 20 Amazon stores. All passed identity, document, and credit checks. Only cross-borrower analysis would have caught it.
Catch Fraud Early
Detect fraud at store #3, not store #20. Automated detection before funding prevents catastrophic losses.
Real-Time Detection
Online learning adapts instantly to new fraud patterns. No waiting for monthly batch retraining.
Cross-Lender Intelligence
Fraud rings spanning multiple lenders become visible. Fraudsters can't hop lenders anymore.
Team From
From Transaction Data to Fraud Ring Detection
Built on TikTok-scale ML infrastructure. The same architecture that powers real-time recommendations — applied to fraud detection.
Behavioral Relationship Graph
Ingest bank transaction data for borrowers and construct real-time entity embeddings. Pre-computed embeddings enable O(log n) relationship queries.
Applicant Check
New applicant connects bank account → cross-borrower graph analysis → deliver risk score + evidence in 3 seconds.
Alerts + Explainability
Underwriters see behavioral evidence: "Applicant A is linked to Borrower B and C through abnormal transfers and shared counterparties."
TikTok-Scale Infrastructure for Fraud Detection
Real-Time Online Learning
New fraud patterns detected instantly. Expiring embeddings surface dormant→active fraud rings. Streaming engine delivers sub-second processing.
Cross-Entity Intelligence
Detect inter-borrower money flows, shared counterparties, coordinated timing patterns, and behavioral similarities invisible to single-entity tools.
Every Existing Tool Failed. Only Cross-Borrower Analysis Would Have Caught It.
A real case: 20 Amazon stores, different owners, EINs, documents, revenues — all "independent" on paper. Actually shifting ad spend + money between stores. Extracted ~$10 million before all defaulted simultaneously.
Why Every Tool Failed
The blind spot in underwriting
Identity verification: ✓ PASS — All identities were real
Document verification: ✓ PASS — Documents genuine
Credit checks: ✓ PASS — Real credit histories
Cross-borrower (Syval): ✗ CATCH — Inter-borrower transfers + relationships
Add Cross-Borrower Intelligence to Your Underwriting Stack
Schedule a demo to see how Syval detects coordinated fraud that identity, document, and credit tools miss. 3-second API response with explainable risk scores.
Schedule a personalized demo to explore how behavioral intelligence can drive measurable ROI for your financial institution.