AI USE CASE
Graph Neural Network Synthetic Identity Detector
Detect synthetic identity fraud by mapping hidden relationships across application data using graph AI.
What it is
Graph neural networks analyse connections between personal data elements, names, addresses, phone numbers, SSNs, across loan and account applications to surface synthetic identities that evade traditional rule-based checks. Ongoing model retraining keeps pace with evolving fraud patterns.
Data you need
Historical application records with personal data fields (name, address, phone, national ID), prior fraud labels, and linkage keys to join records across products and time.
Required systems
- crm
- erp
- data warehouse
Why it works
- Rich, cross-product identity linkage data with at least 18 months of historical applications and confirmed fraud labels.
- Dedicated ML engineering and fraud domain expertise working together throughout design and tuning.
- Regular model retraining cycles (monthly or triggered by drift metrics) with a feedback loop from fraud investigators.
- Clear escalation workflows so model alerts integrate seamlessly into existing case management processes.
How this goes wrong
- Insufficient labelled fraud examples lead to an undertrained model that misses novel synthetic identity patterns.
- Data silos across products prevent graph construction, leaving critical relational signals invisible.
- Model drift as fraudsters adapt faster than retraining cadence allows, eroding detection rates within months.
- High false-positive rates alienate legitimate customers and overwhelm compliance teams if precision thresholds are not tuned carefully.
When NOT to do this
Do not pursue this if your organisation cannot reliably join identity records across at least two products, without a unified entity graph, GNN models produce noise rather than signal.
Example vendors
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