Artificial Intelligence for Elder-Scam Defense: A Review of Detection Methods for Human- and AI-Generated Fraud and a Layered Protection Framework

Scams targeting older adults increasingly combine social engineering with generative technology such as voice cloning, video deepfakes and text produced by large language models (LLMs). Adults aged 60 and over reported losses of USD 7.7 billion to the FBI Internet Crime Complaint Center in 2025, 59% more than in 2024. This review, supported by a documented search of four databases (2019–2026), examines how such scams are structured, why older adults are susceptible, and how artificial intelligence (AI) can detect or mitigate them. Of 992 screened records, 60 studies met the eligibility criteria, and three more were identified through citation chasing. Most evaluations with older users measured knowledge, confidence or intentions after an educational intervention; where detection performance or real-world exposure was measured, results were mixed and often null. None of the included studies evaluated an AI-based scam-detection system with older users in a field setting, and the only study testing audio-deepfake detectors on older speakers found that detectors trained on standard benchmarks roughly doubled their error rate. We propose a six-layer conceptual framework, not yet validated, for engineering elder-scam protection as a socio-technical system, and identify age-diverse evaluation, field trials of real-time assistance, privacy preservation and trust calibration as the principal open challenges.