Use Case

Catch fraud rings before
they cost you millions

Fraudsters create dozens of synthetic identities — but they reuse phones, devices, and addresses. Tilores links them all in real time so your decisioning engine sees the full picture.

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The Problem

Fragmented data is a fraudster's best friend

Without Tilores

A fraud ring submits 40 loan applications across your channels:

✗Each application uses a different name and SSN

✗Same phone number shared by 12 applicants — undetected

✗Same device fingerprint across 8 submissions — missed

✗All 40 applications approved. Loss: $2.4M

With Tilores

Same ring, different outcome:

✓Application 1 resolves all linked records in <150ms

✓Shared phone and device flagged as a network signal

✓Ring structure surfaced before underwriting begins

✓All 40 applications routed to manual review or declined

How It Works

Real-time identity graph at every decision point

Event Trigger

A new application, transaction, or login hits your system and triggers a Tilores query.

Identity Resolution

Tilores searches all your data sources and resolves every record linked to this identity in <150ms.

Graph Linkage

Connections to other entities — shared attributes, known bad actors, flagged accounts — are surfaced.

Enriched Decision

Your fraud engine or rules platform receives a complete risk context. Humans review what matters.

Coverage

Every major fraud vector, one API

🎭

Synthetic Identity Fraud

Detect fabricated identities built from real and fake data. Tilores surfaces thin-file patterns and attribute reuse across your portfolio.

🕸

Fraud Ring Detection

Link seemingly unrelated applicants through shared phones, devices, addresses, and emails. Surface coordinated networks before a single application is approved.

🔓

Account Takeover

Identify when a compromised account suddenly shows attributes that resolve to a different identity cluster — a strong takeover signal.

📋

New Account & Application Fraud

Catch first-party and third-party NAF by resolving the applicant against all prior interactions and channels — not just a bureau pull. Includes cross-geography loan shopping.

💸

Bust-Out Fraud

Spot the slow-build pattern: accounts that appear clean for months then max out and vanish. Tilores links behavioral and identity threads across the full account lifecycle.

🔗

Third-Party Network Abuse

Detect referral fraud, promo abuse, and multi-accounting by resolving device, behavioral, and identity signals into one entity view.

Technical Fit

Plugs into your existing stack

Latency	<150ms p99 on AWS, around 1ms self-hosted — fast enough for real-time decisioning at the point of application
Data sources	Connect CRM, core banking, device intelligence, bureau data, and internal watchlists in one graph
Deployment	AWS-native by default, infrastructure-agnostic by design. Managed on AWS, in your own AWS account, on another cloud, or on-premise. Same engine and same API in every case.
API	GraphQL API. Query by any attribute — name, phone, email, device ID, SSN fragment
Explainability	Every match returns the source records and matched attributes — investigators see exactly why two identities were linked
Integration	Works alongside your fraud rules engine, ML models, or case management platform

Example — resolve applicant at point of application

query FraudCheck($phone: String!) {
  search(input: {
    parameters: { phone: $phone }
  }) {
    entities {
      id
      records {
        name
        email
        address
        deviceId
      }
      score
      hitScore
    }
  }
}

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