KYC/KYB Investigation: How to Reduce False Positives Without Letting Fraud Through
This article is written for exclusively informational and educational purposes. It does not constitute legal advice and should not be relied upon as a substitute for professional legal counsel. The information presented reflects the state of applicable laws as of the date of publication and is subject to change.
The KYC/KYB Paradox
In KYC/KYB onboarding, every false positive costs time, conversion, and sometimes legitimate customers. Every false negative costs financial and regulatory risk. The real challenge is therefore not to “maximize a score”, but to manage a defensible operational trade-off.
What the Field Investigation Shows
Across multiple flows, we observe three recurring causes of false positives:
- single thresholds applied to all document types;
- no segmentation by risk profile;
- human escalation that is too broad and not targeted.
In parallel, fraudulent files that get through often exploit documents that are “almost coherent”: visually credible enough, but inconsistent in the links between data (date, income logic, cross-identity checks).
Recommended Method: a 3-Level Pipeline
Level 1: fast triage
- Format/structure/metadata checks.
- Immediate blocking of obviously invalid cases.
Level 2: consolidated score
- Technical score + semantic score + business rules.
- Differentiated thresholds by document type and customer context.
Level 3: targeted expert review
- Escalate only ambiguous or high-impact files.
- Decision assisted by explanatory traces, not just a raw score.
How DeepForgery Helps Reduce False Positives
- Native segmentation: you can adapt analysis depth by flow.
- Operational explainability: triggering signals guide human review.
- Automatic routing: fewer unnecessary escalations on healthy files.
- Improvement loop: analyst feedback informs threshold tuning.
KPIs to Track Over 90 Days
| KPI | Target | Business impact |
|---|---|---|
| False positive rate | Continuous decrease | Less onboarding friction |
| False negative rate | Controlled | Lower residual risk |
| Average decision time | Reduction | Faster conversion |
| Human escalation rate | Targeted | Analyst capacity better used |
Common Mistakes to Avoid
- Treat every file with the same detection aggressiveness.
- Confuse “model accuracy” with “overall operational performance”.
- Fail to measure the commercial cost of false positives.
- Fail to formalize a runbook for disputed cases.
Conclusion
Reducing false positives without opening the door to fraudsters requires active risk governance, not just an initial configuration. DeepForgery provides a technical and operational foundation to evolve this trade-off continuously and in a controlled way.
Next step
Want to measure the real impact on your acceptance rates and your risk?
Start a DeepForgery KYC/KYB pilot: Start now.
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