Technical Investigation: What an Anti-fraud E2E Smoke Test Reveals (and What Most Teams Forget)

Why an E2E Smoke Test Changes the Game

Many anti-fraud projects fail not on the AI model, but on real integration: authentication, asynchronous queues, credit management, legacy endpoints, parsing heterogeneous responses. A well-designed E2E smoke test is the best way to reveal these failures before production.

Scope of the Investigation

We analyzed a complete internal smoke test (date: 2026-02-23) covering:

• API Gateway communication;
• file upload and worker dispatch;
• polling on main endpoint and legacy alias endpoint;
• credit pre-authorization and handling of insufficient funds.

Overall Result: PASS, but Informative

The E2E technical workflow is validated. This is an excellent foundation, but the real value of the investigation lies elsewhere: it shows the corrections needed to make the system operational under real conditions.

What Was Validated

• Upload: OK
• Worker dispatch: OK
• Polling GET /api/v1/analyze/{id}: OK
• Alias /api/v1/analyze/{id}/status: OK
• Credit check: 402 when balance insufficient, 200 when authorized

Example of Real Case Observed

• Organization without credits: POST refused in 402 (expected behavior).
• Credited organization: POST 200, generation of request_id, then completed status in polling.

This behavior is crucial: it locks in the financial governance of the service and prevents "ghost" consumption.

The Corrected Incidents That Really Count

The list of corrections made during the smoke test is particularly interesting because it reflects the real-world problems encountered in production:

• Legacy/new module compatibility on the client side.
• Robust parsing of the results field (dict or JSON string).
• Alias endpoint status for historical compatibility.
• Metering bug fix (file_name not defined).
• Credit verification linked to the actual transactions table.
• Reliable Redis/Celery authentication outside of single dependency on REDIS_URL.

These points may seem technical, but they directly impact the customer experience, operating costs, and trust in the service.

Residual Risks: The Real Subject Before Scaling Up

The investigation highlights two runtime risks that must not be minimized:

1. Module not loaded due to a missing system dependency (libxcb.so.1).
2. ClamAV not reachable at runtime, implying a fallback without active antivirus scan.

Operational translation: the pipeline works, but some protections still need to be hardened for complete enterprise trust.

What Most Teams Forget

• A high-performance AI model does not compensate for fragile orchestration.
• Legacy compatibility is often the first point of friction for customers.
• Production monitoring must cover safety, latency, and business consistency.
• Without usable logs, it is impossible to defend decisions in an audit.

How DeepForgery Transforms This Finding into a Solution

DeepForgery's value is not just in providing a fraud score. It consists of delivering a workable decision-making system:

• structured authentication and access control;
• integrated credit governance;
• asynchronous processing and job monitoring;
• progressive compatibility (legacy to modern standard);
• complete traceability for compliance and risk management.

Recommended Plan Before Scaling

1. Correct all critical runtime dependencies (modules and antivirus).
2. Define SLOs/SLAs per endpoint and analysis queue.
3. Set up weekly E2E non-regression tests.
4. Formalize an incident runbook (detection, rollback, communication).

Next step

Want to avoid surprises at go-live?

Launch a technical framing with DeepForgery: Start your pilot.

We can help you build a "ready-for-prod" checklist aligned with security, cost, and business performance.