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May 1, 20260 citationsOpen Access

Artificial Intelligence and Machine Learning in Financial Services: A Systematic Literature Review

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ANAnthony Chidi NzomiwuFOFrancisca Uzooyibo Okoye

Key Points

  • This review aims to analyze the implementation and performance of AI and ML in financial services, alongside regulatory concerns.
  • Conducted a systematic literature review of AI and ML in financial services from 2018 to 2024.
  • Identified 86 relevant studies focusing on implementation, performance, ethics, and market impacts.
  • Reviewed findings on trading, credit assessments, and fraud detection.
  • Machine learning techniques in fraud detection achieved over 90% accuracy.
  • Artificial intelligence improvements in credit scoring resulted in prediction error reductions of 15-25%.
  • Identified persistent challenges in explainability, bias, and regulatory frameworks.

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have changed how financial services operate, improving areas like trading, credit assessments, fraud detection, and customer service. These technologies enhance efficiency and customer experience but raise concerns about fairness and regulations. This review looks at research on AI and ML in financial services, focusing on their use, performance, and regulatory challenges from 2018 to 2024. A search of academic databases found 86 relevant studies that analyzed technical implementation, performance, ethics, and market impacts. Results showed that ML in fraud detection can exceed 90% accuracy, and AI in credit scoring can lower prediction errors by 15-25%. Despite these advancements, challenges remain, especially in explainability, bias, and regulation. Successful adoption of AI and ML requires addressing these issues through responsible frameworks and governance structures.

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Cite This Study

Nzomiwu et al. (2025) studied this question.

synapsesocial.com/papers/69f443e8967e944ac55670cchttps://doi.org/10.5281/zenodo.19894406
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