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Algorithmic Lending Decisions: Understanding Fairness, Explainability, and Consumer Trust

Maria Urbell

Department of Information Systems, Wayland Baptist University, Phoenix, USA

Abstract

Artificial intelligence (AI) and machine learning are increasingly transforming lending decisions by enabling financial institutions to analyze large volumes of financial and alternative data, automate credit assessments, predict default risk, and improve the efficiency of loan approval processes. Algorithmic lending has the potential to reduce processing costs, increase decision speed, expand access to credit, and supplement traditional credit-scoring approaches. However, the growing reliance on algorithmic decision-making also raises significant concerns regarding fairness, discrimination, explainability, transparency, accountability, and consumer trust. Biases embedded within historical data, proxy variables, model design, or lending practices may produce unequal outcomes across consumer groups, while complex machine-learning models can make adverse lending decisions difficult for consumers and financial institutions to understand or explain. These challenges are particularly significant because lending decisions can directly influence individuals' access to housing, education, entrepreneurship, and broader economic opportunities. This study develops a conceptual framework for understanding the relationships among algorithmic fairness, explainability, responsible AI governance, and consumer trust in AI-enabled lending. Using a conceptual research design, the study synthesizes multidisciplinary literature available through December 2024 from finance, banking, artificial intelligence, information systems, consumer behavior, and responsible AI research. The proposed framework suggests that fairness and explainability strengthen consumer trust when supported by data quality, transparency, human oversight, and organizational accountability. The study contributes to emerging research on responsible AI in financial services by shifting attention from the predictive performance of lending algorithms toward the broader institutional and consumer conditions required for legitimate algorithmic decision-making. It also provides practical guidance for financial institutions seeking to achieve the efficiency benefits of AI while maintaining fair lending practices, regulatory accountability, and consumer confidence

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0806023

Pages

15478-15500

Published

November 10, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Maria Urbell (%2025). Algorithmic Lending Decisions: Understanding Fairness, Explainability, and Consumer Trust. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15478-15500. https://doi.org/10.15662/IJSRAT.2025.0806023

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