Secure AI-Driven Marketing Data Platforms for Financial Services: Architecture, Compliance, and Fraud Prevention
DOI:
https://doi.org/10.63282/3117-5481/WFCMLS26-109Keywords:
Artificial Intelligence, Marketing Data Platforms, Fraud Detection, Identity Graph Analytics, Financial Data Security, Privacy-Preserving Analytics, Machine LearningAbstract
Financial institutions increasingly rely on AI‑driven marketing data platforms to acquire and engage customers through personalized digital interactions. These systems integrate large‑scale consumer datasets, machine learning models, and real‑time decision engines to optimize marketing performance and product recommendations. However, the integration of artificial intelligence within marketing infrastructure introduces substantial risks related to cybersecurity, regulatory compliance, and fraud exploitation. Digital marketing channels frequently process sensitive financial and identity data including consumer identifiers, behavioral interactions, and transaction signals. These datasets represent valuable targets for malicious actors and fraud schemes such as synthetic identity attacks, bot‑driven application fraud, and account takeover campaigns. Consequently, organizations must design marketing analytics platforms that not only deliver intelligent decisioning capabilities but also enforce strong security controls and regulatory safeguards. This paper proposes a secure architecture for AI‑driven marketing data platforms designed for financial services environments. The framework integrates cloud‑native data engineering pipelines, identity resolution systems, privacy‑preserving analytics, and machine‑learning‑based fraud detection mechanisms. The architecture emphasizes secure data ingestion, scalable distributed processing, governance controls aligned with financial data regulations, and graph‑based analytics capable of detecting identity fraud patterns. Experimental evaluation using simulated financial marketing data demonstrates the benefits of integrating identity graph analytics with machine learning models for improved fraud detection and marketing decision intelligence. [2][5][7]
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