AI-POWERED INCIDENT RESPONSE MODEL FOR DIGITAL FINANCE IN ZAMBIA

Abstract

Zambia’s digital finance ecosystem including mobile money, fintech platforms, and online payments has expanded rapidly, raising exposure to fraud, insider compromise, and data breaches. Current incident response remains reactive and human dependent, leading to delayed containment. This study relies exclusively on secondary data; peer-reviewed research, industry reports, and policy frameworks to propose a contextualized, AI-powered incident response model for Zambia’s financial sector. The framework integrates machine learning into existing Security Information and Event Management (SIEM) workflows, emphasizing modularity and phased adoption. Synthesized evidence highlights gains in detection accuracy and response speed, while also identifying challenges in data availability, organizational trust, and regulatory clarity. The main contribution is a secondary data-driven conceptual architecture designed for resource-constrained contexts, providing a foundation for pilot evaluations and regulatory engagement. This work contributes to building resilient digital finance systems in Sub-Saharan Africa.