aceApplications of AI-Based Models for Online Fraud Detection and Analysis
Abstract Background Fraud is a prevalent offence that extends beyond financial loss, impacting victims emotionally, psychologically, and physically. Advances in online communication technologies continue to create new opportunities for fraud, and fraudsters increasingly using these channels for deception. With the progression of technologies like Generative Artificial Intelligence (GenAI), there is a growing concern that fraud will increase in scale using these advanced methods, with offenders employing deep-fak…
AI and NLP models trained on text data can detect and analyze patterns across multiple categories of online fraud.
Fraud-detection models trained for specific scam types often fail to generalize to new fraud types and lose effectiveness when trained on outdated data, with inconsistent performance reporting.
Review finds primary studies often omit data limitations and training biases, use outdated training data that reduces effectiveness as scams evolve, and report performance metrics inconsistently.
Evidence
- Peer-reviewedCrime Science2025-06-13
How should this claim be treated?
Truvace Impact Record TRV-2026-0521, v1: “Applications of AI-Based Models for Online Fraud Detection and Analysis.” Truvace, 2026-07-24. /record/TRV-2026-0521 (accessed at citation time). sha256 2dbcae213594f252…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0521 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.