Home / Current Issue / Paper 1708930
Advancements in Financial Performance Modeling for SMEs: AI-Driven Solutions for Payment Systems and Credit Scoring
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Small and Medium Enterprises (SMEs) play a vital role in driving economic growth, innovation, and employment globally. However, their access to efficient financial services, particularly in payment systems and credit assessment, remains constrained by limited financial histories, informal operations, and high perceived risk. This paper explores the transformative role of Artificial Intelligence (AI) in advancing financial performance modeling for SMEs, with a particular focus on AI-driven innovations in payment systems and credit scoring mechanisms. The study reviews key AI methodologies including machine learning, natural language processing, and anomaly detection?and their application in analyzing alternative data sources such as transaction records, utility payments, and behavioral indicators. These approaches offer more nuanced and inclusive insights into SME financial health, enabling more accurate credit risk assessments and faster, secure payment processing. The integration of AI in payment platforms facilitates intelligent transaction routing, fraud prevention, and real-time analytics, optimizing cash flow management and operational efficiency for SMEs. Furthermore, the paper addresses the challenges and ethical considerations of AI adoption, including data privacy concerns, algorithmic bias, regulatory uncertainty, and the digital divide. Case studies of fintech innovations such as Tala, Kabbage, and Stripe illustrate successful implementations that have improved SME financial access and resilience. The paper also outlines recommendations for policymakers, financial institutions, and technology developers to support inclusive AI adoption?emphasizing the need for standardized data frameworks, collaborative ecosystems, and capacity-building initiatives. AI offers a paradigm shift in SME finance by enabling data-driven, scalable, and equitable financial services. However, responsible innovation, supported by interdisciplinary research and policy alignment, is essential to harness its full potential. This research highlights a path forward for leveraging AI to bridge financial gaps and promote sustainable SME development across diverse economic contexts.
Keywords
Advancements, Financial performance, Modeling, SMEs, AI-driven, Solutions, Payment systems, Credit scoring
How to cite this paper
@article{1708930,
author = {Tolulope Joyce Oladuji, Ademola Adewuyi, Chigozie Regina Nwangele, Abiola Oyeronke Akintobi},
title = {Advancements in Financial Performance Modeling for SMEs: AI-Driven Solutions for Payment Systems and Credit Scoring},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {5},
number = {5},
pages = {471-486},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708930.pdf},
abstract = {Small and Medium Enterprises (SMEs) play a vital role in driving economic growth, innovation, and employment globally. However, their access to efficient financial services, particularly in payment systems and credit assessment, remains constrained by limited financial histories, informal operations, and high perceived risk. This paper explores the transformative role of Artificial Intelligence (AI) in advancing financial performance modeling for SMEs, with a particular focus on AI-driven innovations in payment systems and credit scoring mechanisms. The study reviews key AI methodologies including machine learning, natural language processing, and anomaly detection?and their application in analyzing alternative data sources such as transaction records, utility payments, and behavioral indicators. These approaches offer more nuanced and inclusive insights into SME financial health, enabling more accurate credit risk assessments and faster, secure payment processing. The integration of AI in payment platforms facilitates intelligent transaction routing, fraud prevention, and real-time analytics, optimizing cash flow management and operational efficiency for SMEs. Furthermore, the paper addresses the challenges and ethical considerations of AI adoption, including data privacy concerns, algorithmic bias, regulatory uncertainty, and the digital divide. Case studies of fintech innovations such as Tala, Kabbage, and Stripe illustrate successful implementations that have improved SME financial access and resilience. The paper also outlines recommendations for policymakers, financial institutions, and technology developers to support inclusive AI adoption?emphasizing the need for standardized data frameworks, collaborative ecosystems, and capacity-building initiatives. AI offers a paradigm shift in SME finance by enabling data-driven, scalable, and equitable financial services. However, responsible innovation, supported by interdisciplinary research and policy alignment, is essential to harness its full potential. This research highlights a path forward for leveraging AI to bridge financial gaps and promote sustainable SME development across diverse economic contexts.},
keywords = {Advancements, Financial performance, Modeling, SMEs, AI-driven, Solutions, Payment systems, Credit scoring},
month = {November},
}