 Predictive Analytics Systems for Investment Risk Monitoring in SME FinTech Companies and Cross-Border Capital MarketsEmmanuella Ebubechukwu Eboh and Chime Aliliele International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 2, 3969-4010 Abstract: This study develops a predictive analytics systems framework for investment risk monitoring in SME-focused FinTech companies operating within increasingly complex cross-border capital markets. Small and medium-sized enterprises rely heavily on FinTech platforms for payments, lending, crowdfunding, digital trade finance, and investor access, yet these channels are exposed to volatile market conditions, currency shocks, regulatory fragmentation, cyber threats, fraud, liquidity mismatches, and counterparty uncertainty. The study addresses the growing need for intelligent systems that can identify, quantify, and communicate investment-related risks before they escalate into major financial losses or systemic instability. The proposed framework integrates predictive modeling, anomaly detection, behavioral analytics, and real-time dashboarding to strengthen risk visibility across multi-jurisdictional transactions and investment flows. It combines structured financial indicators, transaction histories, investor behavior signals, macroeconomic variables, compliance records, and external market intelligence to generate early-warning outputs for decision makers. Machine learning techniques such as classification algorithms, time-series forecasting, clustering, and ensemble models are conceptually positioned to detect hidden patterns associated with default risk, fraud exposure, portfolio stress, capital flight, and operational disruption. The system also emphasizes explainability, allowing regulators, investors, and FinTech managers to interpret model outputs and respond with confidence. Beyond technical detection, the study highlights governance requirements for deploying predictive analytics responsibly in SME FinTech environments. These include data quality assurance, regulatory interoperability, privacy protection, model validation, cross-border reporting alignment, and ethical oversight. The framework is designed to support investment screening, portfolio surveillance, transaction monitoring, and strategic planning, thereby improving financial resilience and transparency. It also offers value for venture fund managers, compliance teams, digital lenders, and policy institutions seeking to reduce uncertainty in emerging and interconnected markets. The study concludes that predictive analytics systems can significantly improve proactive investment risk management in SME FinTech companies by transforming fragmented data into actionable intelligence. By enabling earlier detection of vulnerabilities and more informed intervention strategies, the framework contributes to stronger investor protection, better capital allocation, and more sustainable participation in cross-border capital markets. Future research may validate the framework empirically across diverse FinTech ecosystems and regulatory regions worldwide. Keywords: Predictive Analytics; Investment Risk Monitoring; Sme Fintech; Cross-Border Capital Markets; Machine Learning; Financial Resilience; Early-Warning Systems; Regulatory Compliance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i2:id:1906 Access Statistics for this article More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology |
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