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A leading provider of identity verification and fraud detection services for financial technology institutions set out to eliminate identity fraud for every applicant on the internet by verifying real people in real time. FullContact serves as the core identity spine and digital behavioral data provider behind that provider’s fraud algorithms.
The Challenge
Fraud models need two things that are hard to get from the same source. They need depth for training, meaning enough accurate, person-level history that a machine learning model can learn what a real identity looks like over time. And they need speed at decision time, meaning a lookup fast enough to run while an applicant is still sitting on the account opening screen.
Synthetic identity fraud is the case that makes this difficult. A synthetic identity is assembled from real fragments: a valid email, a working phone number, a real address, a plausible name, combined into a person who does not exist. Each element passes inspection on its own. The fraud only becomes visible when you can ask whether this particular combination has ever belonged to a real person, and whether the relationships between those fragments hold up under scrutiny.
Answering that requires an identity graph maintained at the person level, with enough signal history behind it to express confidence rather than a simple match or no match. Leading fraud algorithms depend on that kind of metadata intelligence and continuous signal analysis to prevent real-time attacks, and they need a provider that can both train their models and integrate live.
The Approach
- A person-level identity spine. All entity data is maintained at the person level using FullContact Resolve, so identifiers resolve to individuals rather than sitting as disconnected records.
- Terrestrial and digital identifiers plus demographics. Entity data elements spanning physical and digital identity, combined with demographic data, are used to curb fraud and combat synthetic account creation.
- Confidence rather than binary matching. FullContact processes activity signals to influence the relationship confidence between entity fragments, and shares that intelligence with the provider so its models can weigh how strongly a set of identifiers actually belongs together.
How the Integration Works
The data reaches the provider two ways. A training path feeds digital identifiers, behaviors, and demographics into the client’s data science models so those models learn from real identity structure. A real-time path answers queries by email, phone, name, or postal address at decision speed.
Both paths feed the same downstream functions: KYC and compliance checks, fraud scoring, identity verification, and document verification. The training path makes the models better over time. The real-time path applies them to the applicant in front of you.
Where This Applies
Any business that opens accounts online faces this problem. Lenders, neobanks, insurers, marketplaces, payment processors, and gaming platforms all have to decide whether an applicant is a real person within seconds, using only the identifiers that person typed into a form.
The pattern holds beyond fraud as well. The same person-level resolution that tells you an identity is synthetic also tells you when two records are the same customer. Risk teams and marketing teams are usually solving the identity problem separately, with separate budgets, against the same underlying gap.
Next Steps
Talk to our experts about identity data for your fraud and verification models.
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