Identity Verification
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We are thrilled to introduce an exciting new solution to screen your applicants—BMTX Identity Verification™.
Detecting fraudulent submissions early in the admissions process is important to prevent financial and risk burdens. Fraudulent applications can increase your costs and negatively impact acceptance and retention rates and course decisions.
BMTX Identity Verification™ can streamline the admissions process and enhance security by verifying the identity of your prospective students with ease.
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Colleges and universities grapple with a recurring issue: verifying student identities. Detecting these fraudulent submissions early in the application process is a challenge, resulting in substantial financial burdens for universities.
The associated costs incurred and the risk of disbursing aid to undeserving individuals not only strain university budgets, but also compromise the equitable distribution of financial aid to deserving students.
Colleges and universities face challenges due to fraud vulnerabilities during the student enrollment process, and BMTX can help! We created a data-driven, student identity verification solution which will have a key role within the university enrollment process.
As a result, universities will have enriched identity information to inform university-driven business rules to accept or reject student enrollment applications well before any fraudulent disbursements can occur.
Impact
Trusted by 4 of 5 top banks and 7 of top 10 credit card issuers
85% reduction
in fraud rate compared to traditional services
Up to 95% reduction
in third-party and synthetic identity fraud
Up to a 13x reduction
in false positives
Save an average of 15-20 hours/week of manual, peak-season work
The cost of acquiring a new student is increasing every year, with an increase of up to 32% from 2020 to 2022 alone.
How It Works: Data Source
- Multi-layered fraud controls
- Human-in-the-loop labeling
- Fabricated vs. manipulated classification
- Consortium reported/cleaned synthetic data
- Credit header/inquiry and velocity data
- Synthetic-specific features and patterns
- Models trained to think like fraudsters