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

Researchers propose a medication-aware AI framework that detects financial exploitation of Alzheimer's patients by combining transaction monitoring with medication adherence data. The interaction-aware model significantly improves detection of fraudulent transactions during periods of cognitive vulnerability, suggesting that clinical context enhances fraud detection accuracy beyond financial patterns alone.

Financial exploitation of Alzheimer's patients represents a critical vulnerability in healthcare and financial systems that traditional fraud detection overlooks. This research addresses a genuine gap: conventional transaction monitoring systems operate blind to cognitive status, treating high-risk periods identically to periods of normal decision-making capacity. The study's innovation lies in treating medication adherence as a contextual modifier rather than a standalone predictor, recognizing that missed doses or medication timing correlate with cognitive vulnerability windows when patients become susceptible to exploitation.

The framework's performance metrics reveal important nuances often missed in AI research. While the financial-only baseline achieved a higher global F1-score of 0.50, the interaction-aware model dramatically improved recall during vulnerability windows from 74% to 91%, capturing genuine high-risk cases. This trade-off reflects a fundamental principle in clinical AI: optimizing for overall accuracy may underperform when protecting vulnerable populations requires sensitivity to context-specific risk factors.

Beyond academic interest, this approach has direct applications for healthcare providers, financial institutions, and elder care systems. Banks and payment processors could integrate medication adherence signals from healthcare providers or wearables to implement dynamic transaction thresholds. The framework suggests that protective systems must evolve beyond one-size-fits-all rules toward adaptive models that account for physiological and cognitive states.

Future implementation faces challenges around privacy integration, inter-institutional data sharing, and regulatory compliance in healthcare-fintech collaboration. Success requires establishing secure channels between medical and financial systems while maintaining HIPAA and data protection standards.

• →Interaction-aware models combining medication adherence with financial data improve fraud detection recall to 91% during cognitive vulnerability windows
• →Medication adherence functions most effectively as a contextual risk modifier rather than as an isolated predictor of financial exploitation
• →Traditional fraud detection systems miss critical cognitive status information relevant to elder financial vulnerability
• →The framework demonstrates trade-offs between global accuracy metrics and vulnerable-population-specific performance improvements
• →Implementation requires secure healthcare-fintech data integration while maintaining privacy and regulatory compliance standards