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A Review of Data-Driven Risk Evaluation Models for Emerging Market Financial Institutions
Subject area: Science,Engineering and Technology · Area of research: Risk Evaluation Models
Abstract
Emerging market financial institutions face heightened levels of uncertainty due to volatile macroeconomic conditions, weak regulatory environments, data scarcity, and structural market imperfections. Advances in data-driven modelling spanning statistical learning, machine learning, early warning systems, credit scoring, stress-testing frameworks, and macro-prudential forecasting offer significant potential to strengthen risk evaluation and supervisory oversight. This paper provides a comprehensive review of data-driven risk evaluation models applicable to banks, microfinance institutions, insurance firms, and capital-market intermediaries operating in emerging economies. Drawing solely on pre-2019 scholarship, the review evaluates the evolution, methodological foundations, strengths, and limitations of major quantitative approaches used in credit risk assessment, liquidity risk prediction, operational risk detection, and systemic vulnerability monitoring. The article identifies persistent challenges such as data quality issues, modelling instability, weak governance, and context adaptation gaps. A set of implications for institutional practice and future research is outlined.
Keywords
Data-Driven Modelling, Emerging Markets, Financial Institutions, Risk Evaluation, Machine Learning, Early Warning Systems.
How to cite this paper
@article{1712258,
author = {Olawole Akomolafe, Michael Uzoma Agu},
title = {A Review of Data-Driven Risk Evaluation Models for Emerging Market Financial Institutions},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {6},
pages = {433-448},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712258.pdf},
abstract = {Emerging market financial institutions face heightened levels of uncertainty due to volatile macroeconomic conditions, weak regulatory environments, data scarcity, and structural market imperfections. Advances in data-driven modelling spanning statistical learning, machine learning, early warning systems, credit scoring, stress-testing frameworks, and macro-prudential forecasting offer significant potential to strengthen risk evaluation and supervisory oversight. This paper provides a comprehensive review of data-driven risk evaluation models applicable to banks, microfinance institutions, insurance firms, and capital-market intermediaries operating in emerging economies. Drawing solely on pre-2019 scholarship, the review evaluates the evolution, methodological foundations, strengths, and limitations of major quantitative approaches used in credit risk assessment, liquidity risk prediction, operational risk detection, and systemic vulnerability monitoring. The article identifies persistent challenges such as data quality issues, modelling instability, weak governance, and context adaptation gaps. A set of implications for institutional practice and future research is outlined.},
keywords = {Data-Driven Modelling, Emerging Markets, Financial Institutions, Risk Evaluation, Machine Learning, Early Warning Systems.},
month = {December},
}