Manuscript Number : GISRRJ23659

AI-Driven Solutions for Payment System Automation: Transforming Credit Scoring and Underwriting Models

Authors(4) :-Tolulope Joyce Oladuji, Ademola Adewuyi, Chigozie Regina Nwangele, Ayodeji Ajuwon

Artificial Intelligence (AI) is revolutionizing the financial services landscape by enabling unprecedented levels of automation, accuracy, and personalization in payment systems. This paper explores the transformative role of AI-driven solutions in automating payment processes while simultaneously redefining traditional credit scoring and underwriting models. Conventional approaches to credit assessment often rely on limited, static datasets and rule-based systems, resulting in inefficiencies, delays, and bias. In contrast, AI technologies such as machine learning, natural language processing, and predictive analytics offer dynamic, data-rich alternatives that enhance decision-making, operational efficiency, and customer experience. The study examines how AI automates and optimizes real-time payment processing, fraud detection, and compliance checks through intelligent workflows and anomaly detection models. It further highlights the integration of alternative data sources ranging from utility payments and e-commerce activity to mobile phone usage and psychometric indicators into next-generation credit scoring systems. These models enable more inclusive and precise borrower evaluations, particularly for underserved and thin-file consumers, promoting financial inclusion and reducing default risk. Additionally, the paper delves into the evolution of AI-enabled underwriting, where systems continuously learn from historical data, adjust risk models in real time, and provide explainable outputs. The role of explainable AI (XAI) and fairness-aware algorithms is emphasized in ensuring transparency and regulatory alignment. Practical implementations from leading fintech firms and financial institutions are presented to illustrate the real-world impact of these innovations. Despite the promise of AI, the paper also addresses critical challenges such as data privacy, algorithmic bias, and the need for robust governance structures. Recommendations are offered for implementing ethical AI frameworks that align technological advancement with consumer protection. In conclusion, AI-driven payment system automation and intelligent credit evaluation mechanisms represent a paradigm shift in financial services. By leveraging AI, institutions can enhance risk assessment, streamline underwriting, and deliver faster, fairer credit decisions.

Authors and Affiliations

Tolulope Joyce Oladuji
Lotus Capital Limited, Lagos State, Nigeria
Ademola Adewuyi
Wema Bank (Alat digital bank ) IdeaX innovation Lab
Chigozie Regina Nwangele
Neveah Limited, Abuja, Nigeria
Ayodeji Ajuwon
Steward Inc., Delaware, USA

Artificial Intelligence, Payment System Automation, Credit Scoring, Underwriting Models, Machine Learning, Alternative Data, Financial Inclusion, Explainable AI, Risk Assessment, Fintech.

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Publication Details

Published in : Volume 6 | Issue 5 | September-October 2023
Date of Publication : 2023-09-12
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 67-100
Manuscript Number : GISRRJ23659
Publisher : Technoscience Academy

ISSN : 2582-0095

Cite This Article :

Tolulope Joyce Oladuji, Ademola Adewuyi, Chigozie Regina Nwangele, Ayodeji Ajuwon, "AI-Driven Solutions for Payment System Automation: Transforming Credit Scoring and Underwriting Models", Gyanshauryam, International Scientific Refereed Research Journal (GISRRJ), ISSN : 2582-0095, Volume 6, Issue 5, pp.67-100, September-October.2023
URL : https://gisrrj.com/GISRRJ23659

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