Medication-Aware Financial Exploitation Detection for Alzheimer's Patients Using Edge-Aware Interaction Risk Modeling

2026-05-30

AI Summary

This study addresses the heightened vulnerability of individuals with Alzheimer’s disease to financial exploitation during periods of cognitive instability, a risk inadequately captured by existing fraud detection systems that overlook clinical factors such as medication adherence. To bridge this gap, the authors propose a hybrid monitoring framework that integrates financial behavioral indicators—包括异常交易、商户新颖性、交易频次和时间偏移—with medication adherence, innovatively treating the latter as a contextual moderator of risk. The framework employs interaction-aware modeling to identify transient windows of heightened vulnerability induced by medication patterns. Evaluated on 45-day simulated data from 180 patients, the interaction-aware model significantly improves recall of risk events within these vulnerable windows—from 0.7442 to 0.9070—and achieves the highest mean average precision in ranking high-risk cases.

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, producing 8,100 medication records and 30,855 transactions. The framework evaluates amount anomaly, vendor novelty, transaction frequency, time deviation, and medication adherence through financial-only, additive medication-aware, and interaction-aware logistic models. Results show that the financial-only baseline obtained the highest global F1-score of 0.5000, but the interaction-aware model improved recall during medication-induced vulnerability windows from 0.7442 to 0.9070 and achieved the highest average precision for ranked high-risk cases. The findings suggest that medication adherence is most useful as a contextual modifier of financial risk rather than as an isolated predictor.

Problem

• financial exploitation
• Alzheimer's disease
• medication adherence
• vulnerability detection
• cognitive stability

Innovation

• medication-aware modeling
• financial exploitation detection
• Alzheimer's disease
• interaction risk modeling
• cognitive vulnerability