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Advancements in Financial Forecasting Models: Using AI for Predictive Business Analysis in Emerging Economies
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Financial forecasting is a cornerstone of strategic decision-making in businesses and governments alike, yet it remains a persistent challenge in emerging economies due to data scarcity, market volatility, and informal economic structures. Traditional statistical models often struggle to deliver accurate predictions in such environments. Recent advancements in artificial intelligence (AI) offer transformative potential by enabling predictive business analysis that is more adaptive, scalable, and responsive to non-linear economic patterns. This paper explores the evolution of financial forecasting from conventional time series and regression models to AI-driven techniques, including machine learning algorithms, deep learning frameworks, and natural language processing. This examines how AI enhances forecasting accuracy by incorporating diverse data sources ranging from transactional and financial statement data to alternative inputs like mobile usage, satellite imagery, and real-time news sentiment. Emphasis is placed on practical applications within emerging economies, particularly in supporting small and medium-sized enterprises (SMEs), informal businesses, and public sector planning. Case studies from Africa, South Asia, and Latin America highlight how AI-powered models are being deployed to assess credit risk, predict cash flows, estimate market demand, and optimize resource allocation. Despite these advancements, significant challenges remain, including limited digital infrastructure, data quality concerns, and regulatory uncertainties. Moreover, ethical issues such as algorithmic bias and lack of model transparency present barriers to widespread adoption. The paper concludes by proposing a conceptual framework for responsible AI integration into financial forecasting systems, tailored to the needs and constraints of emerging markets. By leveraging AI effectively, stakeholders in these economies can improve business resilience, enhance access to credit, and foster more inclusive economic growth. The findings underscore the importance of interdisciplinary collaboration, local context sensitivity, and policy support in scaling the benefits of AI-driven forecasting in under-resourced regions.
Keywords
Advancements, Financial, Forecasting models, AI, Predictive, Business analysis, Emerging economies
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How to cite this paper
@article{1708934,
author = {Chigozie Regina Nwangele, Omoniyi Onifade, Abiola Oyeronke Akintobi, Tolulope Joyce Oladuji},
title = {Advancements in Financial Forecasting Models: Using AI for Predictive Business Analysis in Emerging Economies},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {4},
pages = {223-236},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708934.pdf},
abstract = {Financial forecasting is a cornerstone of strategic decision-making in businesses and governments alike, yet it remains a persistent challenge in emerging economies due to data scarcity, market volatility, and informal economic structures. Traditional statistical models often struggle to deliver accurate predictions in such environments. Recent advancements in artificial intelligence (AI) offer transformative potential by enabling predictive business analysis that is more adaptive, scalable, and responsive to non-linear economic patterns. This paper explores the evolution of financial forecasting from conventional time series and regression models to AI-driven techniques, including machine learning algorithms, deep learning frameworks, and natural language processing. This examines how AI enhances forecasting accuracy by incorporating diverse data sources ranging from transactional and financial statement data to alternative inputs like mobile usage, satellite imagery, and real-time news sentiment. Emphasis is placed on practical applications within emerging economies, particularly in supporting small and medium-sized enterprises (SMEs), informal businesses, and public sector planning. Case studies from Africa, South Asia, and Latin America highlight how AI-powered models are being deployed to assess credit risk, predict cash flows, estimate market demand, and optimize resource allocation. Despite these advancements, significant challenges remain, including limited digital infrastructure, data quality concerns, and regulatory uncertainties. Moreover, ethical issues such as algorithmic bias and lack of model transparency present barriers to widespread adoption. The paper concludes by proposing a conceptual framework for responsible AI integration into financial forecasting systems, tailored to the needs and constraints of emerging markets. By leveraging AI effectively, stakeholders in these economies can improve business resilience, enhance access to credit, and foster more inclusive economic growth. The findings underscore the importance of interdisciplinary collaboration, local context sensitivity, and policy support in scaling the benefits of AI-driven forecasting in under-resourced regions.},
keywords = {Advancements, Financial, Forecasting models, AI, Predictive, Business analysis, Emerging economies},
month = {October},
}