Geescore™ AI Fraud Detection Service
🛡️ Geescore AI Fraud Modifier: Resume Authenticity Verification
The Problem
Highly competitive hiring environments drive sophisticated applicants to excessively tailor their résumés for single job descriptions—often using AI tools—resulting in hyper-optimized submissions that perfectly match keywords but potentially mask a lack of genuine, broad expertise. Relying only on a high target match score can lead to wasted interview cycles and costly mis-hires.
🔬 The Science: Embeddings, Sister Jobs, and Semantic Cross-Validation
Our service utilizes cutting-edge Natural Language Processing (NLP), Vector Database technology, and a comparison of your existing Geescore metrics to assess the true semantic meaning of a jobseeker’s experience and detect optimization fraud.
1. Vector Embeddings: The Foundation
We use specialized deep learning models (like transformers) to convert the text of a jobseeker’s résumé and a job description into numerical vectors (or embeddings), which capture the meaning and context of the text. These vectors are stored in a Vector Database (Vector DB), optimized for fast searches to instantly identify a cluster of semantically similar roles, which we call “sister jobs.”
2. The Fraud Modifier Calculation: Geescore Cross-Validation
The core of the Geescore AI Fraud Modifier relies on measuring the consistency of the jobseeker’s Geescore across the target job and its sister jobs.
- Target Geescore ($G_{target}$): This is the existing score for the jobseeker against the specific target posting.
- Sister Geescores ($G_{sister}$): For the cluster of “sister jobs” found via embedding similarity, we re-run the standard Geescore jobseeker scoring service to get individual scores against each of these similar roles. We then calculate the average or median score across this cluster ($\bar{G}_{sister}$).
- Hyper-Optimization Detection: The AI Fraud Modifier compares the deviation between the $G_{target}$ and the average score of the similar sister jobs, $\bar{G}{sister}$. A large deviation (where $G{target}$ is much higher than $\bar{G}_{sister}$) triggers the negative modifier.
🚩 The Geescore AI Fraud Modifier Scale
The resulting AI Fraud Modifier (ranging from 0 to **-7**) is a crucial warning flag to the evaluating professional:
| Modifier Value | Indication | Actionable Insight |
|---|---|---|
| 0 (Neutral) | No discernible hyper-optimization. | The Geescore match rating is a reliable indicator of broad skill fit. |
| -1 to -3 (Low Warning) | Minor signs of narrow optimization detected by score variance. | Proceed with caution; confirm skills are broadly transferable during screening. |
| -4 to -7 (High Warning) | Significant hyper-optimization detected: high deviation between target Geescore and sister job scores. | The high Geescore match may be artificially inflated. Prioritize verification of core competencies and transferable skills. |
🎯 Key Benefits for Hiring Professionals
- Mitigate Interview Waste: Identify and deprioritize applicants whose résumés are artificially inflated for a single role.
- Enhance Geescore Reliability: Add a layer of objective, data-backed authenticity to the primary Geescore, ensuring you trust the match score.
- Improve Quality of Hire: Focus interview time only on candidates with verified, deep, and transferable skills, reducing the risk of a mis-hire.
What it is, and what it isn’t.
Geescore AI Fraud detection identifies when a jobseeker has hyper-optimized a resume for a specific job posting. It’s a warning. It’s a heads-up.
What it does not do, is tell you the hiring professional whether or not to hire or reject the candidate, or whether the jobseeker exaggerated or lied when creating the resume.
Geescore is committed to offering scoring services and additional defensive measures, to help hiring companies make better, no-bias hiring decisions.
Our scoring metagraph and AI Fraud detection should not be the sole basis for making a hiring decision.