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AI-Driven Financial Intelligence Systems: A New Era of Risk Detection and Strategic Analysis

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  • Green, Alicia

Abstract

The integration of artificial intelligence (AI) into financial intelligence systems enables automated risk detection and strategic decision support in African markets. This paper examines the technical architectures and AI methodologies (supervised learning, anomaly detection, natural language processing) employed in real-world African financial applications. We discuss data pipelines combining structured and unstructured data (market transactions, social media, news, macro indicators) and outline algorithmic models for credit risk, market risk, systemic risk, and financial crime detection. Specific cases from Nigeria, Kenya, and South Africa illustrate AI use in fraud/AML detection, credit scoring with alternative data, and portfolio stress-testing. Quantitative indicators (e.g., Nigeria’s NGN1.56 quadrillion digital payments in H1 2024 and 468\% surge in fraud cases) underscore the scale of data and risks. Regulatory contexts (e.g., CBN’s AI‑AML framework, SARB guidelines) and infrastructure constraints (limited data connectivity, power) are highlighted. The paper proposes a system framework comprising data integration, machine learning engines, continuous risk scoring, and visualization dashboards. Key applications include dynamic capital allocation, real-time AML monitoring, and scenario-based stress testing. We conclude by identifying ethical challenges (data privacy, model bias, transparency) and suggesting future directions such as hybrid AI-rule systems, localized language models, and cross-border data sharing platforms.

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  • Green, Alicia, 2025. "AI-Driven Financial Intelligence Systems: A New Era of Risk Detection and Strategic Analysis," OSF Preprints ynph2_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:ynph2_v1
    DOI: 10.31219/osf.io/ynph2_v1
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    1. Jingru Wang & Wen Ding & Xiaotong Zhu, 2025. "Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG," Papers 2504.06279, arXiv.org.
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