Medication-Aware Financial Exploitation Detection for Alzheimer's Patients Using Edge-Aware Interaction Risk Modeling
arXiv:2606.00672v1 Announce Type: cross Abstract: Financial exploitation is a growing concern for people with Alzheimer's disease, especially during periods of reduced cognitive stability. Conventional fraud detection systems usually rely on financial behavior alone and ignore clinically relevant factors that may alter vulnerability. This paper proposes a medication-aware framework that synchronizes medication adherence with transaction-level monitoring to improve detection of cognitively risky financial events. A hybrid simulation dataset was constructed for 180 patients across 45 days, produ
The increasing prevalence of AI in healthcare and financial sectors, combined with a growing aging population, necessitates more sophisticated fraud detection methods.
This development addresses a critical vulnerability for a demographic often targeted by financial exploitation, demonstrating AI's capacity for protective applications beyond traditional financial security.
Fraud detection systems can now integrate clinical data like medication adherence, moving beyond purely financial metrics to offer more nuanced and effective protection for vulnerable individuals.
- · Alzheimer's patients and their families
- · Financial institutions (reputational and regulatory compliance)
- · AI developers specializing in healthcare and finance
- · Elder care services
- · Exploiters targeting vulnerable populations
- · Conventional fraud detection systems reliant solely on financial data
Improved detection rates for financial exploitation among cognitively impaired individuals.
Increased trust in financial systems for vulnerable populations and their caregivers, potentially leading to greater financial autonomy for some patients.
Development of broader AI frameworks that integrate diverse data types (medical, social, behavioral) for predictive risk modeling across various life domains.
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Read at arXiv cs.LG