Groundbreaking synthetic fraud detection
Our standard for defining synthetic identities allows us to detect and predict them throughout the customer lifecycle with automated detection rules that evaluate a variety of behaviors.
Powerful decisioning supported by advanced analytics capabilities
Resolve identities and isolate synthetic traits to reduce false positives and fraud losses with custom scores and decisioning strategies. Link fraud scores with device intelligence and relationship history to gain a holistic view of each identity.
Industry-leading data sources
Our expansive and comprehensive credit data, alternative credit data, partner credit data and involvement in the electronic Consent Based Social Security Number Verification (eCBSV) pilot combine to give you a complete view of your customers’ identities so you can definitely resolve identities and isolate synthetic traits.
Multilayered approaches
Perform synthetic identity checks at onboarding, first-time login, and return visits to ensure that no false identities are embedded in your portfolio.
Lower operational expenses
Reduce false positives, referrals, and manual reviews while mitigating losses associated with synthetic identity attacks.
Better consumer experiences
Provide a seamless experience for known consumers while applying appropriate friction to riskier transactions.
Frequently asked questions
Synthetic identity fraud occurs when someone uses a combination of real and fake personal information to create an identity and commit fraud. A synthetic identity might include a stolen Social Security number with a fake name, date of birth, and address to create a “new” identity.
Synthetic identity fraud, despite the common misconception, isn’t a victimless crime. Individual consumers, especially children or older adults, may not realize that their identity information has been abused to create these identities, and businesses can face large charge-offs when synthetic identities bust out, sometimes stealing thousands of dollars individually or as a part of a larger fraud ring.
Preventing synthetic identity fraud requires a robust fraud management solution that looks beyond the standard credit report. Key indicators that an identity may be falsified include the length of credit history, relationships to other identities and accounts, and mismatched addresses and device locations.
Advanced Analytics & Modeling
Cloud Applications & Services
Collections & Debt Recovery
Commercial solutions
Credit Decisioning
Credit Profile Reports
Customer Management
Data Quality & Management
Data Reporting & Furnishing
Data Sources
Fraud Management
Identity Solutions
Marketing Solutions
Regulatory Compliance
Risk Management
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- Solutions
- Advanced Analytics & Modeling
- Collections & Debt Recovery
- Customer Management
- Credit Decisioning
- Credit Profile Reports
- Data Reporting & Furnishing
- Data Sources
- Data Quality & Management
- Fraud Management
- Identity Solutions
- Marketing Solutions
- Regulatory Compliance
- Risk Management
- Workforce Management