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Advanced Risk and Credit Management Frameworks for High-Value Trade and Supply-Chain Finance Portfolios in Emerging Markets
Subject area: Science,Engineering and Technology · Area of research: Credit Management Frameworks
Abstract
High-value trade and supply chain finance portfolios play a pivotal role in facilitating economic growth, infrastructure development, and industrial competitiveness in emerging markets. Yet, these markets are often characterized by elevated credit risks, volatile foreign exchange (FX) conditions, limited access to reliable data, and structural inefficiencies that undermine financing efficiency and resilience. Addressing these challenges requires the development of advanced risk and credit management frameworks that integrate data-driven analytics, predictive modeling, and governance structures tailored to the unique characteristics of emerging economies. This explores how such frameworks can be designed and operationalized to enhance both financial stability and portfolio performance. Key components include real-time risk monitoring through artificial intelligence (AI) and machine learning, which enable early detection of credit deterioration and FX exposure. Data-driven credit scoring methodologies incorporating non-traditional data sources?such as transaction histories, supply chain behaviors, and digital payment footprints?expand financial inclusion by capturing the creditworthiness of small and medium-sized enterprises (SMEs) traditionally excluded from high-value financing. Portfolio diversification strategies, combined with scenario-based stress testing, further enhance resilience against macroeconomic volatility and systemic shocks. This highlights the critical role of regulatory harmonization, digital infrastructure, and public?private partnerships in embedding these frameworks into practice. Multilateral development banks, fintech firms, and commercial banks can collaborate to deploy shared platforms that improve transparency, mitigate default risk, and unlock capital at scale. Empirical evidence suggests that advanced frameworks not only safeguard investors and lenders but also enhance liquidity access for SMEs, improve settlement efficiency, and strengthen the resilience of cross-border supply chains. Ultimately, the adoption of advanced, data-driven risk and credit management frameworks represents a transformative pathway for emerging markets, aligning financial innovation with inclusive growth and global trade stability.
Keywords
Advanced Risk Management Frameworks, Credit Management, High-Value Trade Finance, Supply-Chain Finance, Emerging Markets, Portfolio Risk Assessment, Financial Risk Mitigation, Credit Appraisal Techniques, Trade Finance Optimization, Liquidity Management
References
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How to cite this paper
@article{1711197,
author = {Vivian Chilee Osuji, Chizoba Michael Okafor, Omoize Fatimetu Dako},
title = {Advanced Risk and Credit Management Frameworks for High-Value Trade and Supply-Chain Finance Portfolios in Emerging Markets},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {2},
pages = {880-895},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1711197.pdf},
abstract = {High-value trade and supply chain finance portfolios play a pivotal role in facilitating economic growth, infrastructure development, and industrial competitiveness in emerging markets. Yet, these markets are often characterized by elevated credit risks, volatile foreign exchange (FX) conditions, limited access to reliable data, and structural inefficiencies that undermine financing efficiency and resilience. Addressing these challenges requires the development of advanced risk and credit management frameworks that integrate data-driven analytics, predictive modeling, and governance structures tailored to the unique characteristics of emerging economies. This explores how such frameworks can be designed and operationalized to enhance both financial stability and portfolio performance. Key components include real-time risk monitoring through artificial intelligence (AI) and machine learning, which enable early detection of credit deterioration and FX exposure. Data-driven credit scoring methodologies incorporating non-traditional data sources?such as transaction histories, supply chain behaviors, and digital payment footprints?expand financial inclusion by capturing the creditworthiness of small and medium-sized enterprises (SMEs) traditionally excluded from high-value financing. Portfolio diversification strategies, combined with scenario-based stress testing, further enhance resilience against macroeconomic volatility and systemic shocks. This highlights the critical role of regulatory harmonization, digital infrastructure, and public?private partnerships in embedding these frameworks into practice. Multilateral development banks, fintech firms, and commercial banks can collaborate to deploy shared platforms that improve transparency, mitigate default risk, and unlock capital at scale. Empirical evidence suggests that advanced frameworks not only safeguard investors and lenders but also enhance liquidity access for SMEs, improve settlement efficiency, and strengthen the resilience of cross-border supply chains. Ultimately, the adoption of advanced, data-driven risk and credit management frameworks represents a transformative pathway for emerging markets, aligning financial innovation with inclusive growth and global trade stability.},
keywords = {Advanced Risk Management Frameworks, Credit Management, High-Value Trade Finance, Supply-Chain Finance, Emerging Markets, Portfolio Risk Assessment, Financial Risk Mitigation, Credit Appraisal Techniques, Trade Finance Optimization, Liquidity Management},
month = {August},
}