Explainable AI in financial technologies: Balancing innovation with regulatory compliance

Andrew Nii Anang 1, *, Oluwatosin Esther Ajewumi 2, Tobi Sonubi 3, Kenneth Chukwujekwu Nwafor 4, John Babatope Arogundade 5 and Itiade James Akinbi 6

1 Graduate Assistant, University of Northern IOWA, USA.
2 Olin Business School, Washington University in Saint Louis USA.
3 MBA, Washington University in St. Louis, USA.
4 Management Information Systems, University of Illinois, Springfield, USA.
5 School of Management, University of Bradford, United Kingdom.
6 School of Politics and International Relations, University of Kent Canterbury Kent, UK.
 
Review
International Journal of Science and Research Archive, 2024, 13(01), 1793–1806.
Article DOI: 10.30574/ijsra.2024.13.1.1870
Publication history: 
Received on 18 August 2024; revised on 30 September 2024; accepted on 02 October 2024
 
Abstract: 
As artificial intelligence (AI) technologies increasingly permeate the financial sector, their adoption raises significant challenges and opportunities regarding regulatory compliance and innovation. This paper explores the critical role of Explainable AI (XAI) in balancing these two aspects, particularly in applications such as fraud detection, credit scoring, and algorithmic trading. We highlight the necessity of XAI for financial institutions to meet regulatory requirements that demand transparency and accountability in AI-driven decisions. The discussion delves into the complexities faced by these institutions, including the inherent biases in algorithms that can compromise fairness and the ethical implications of opaque decision-making processes. Through case studies of successful XAI implementations, we illustrate how transparency can enhance consumer trust and promote a more robust regulatory environment. This examination underscores the importance of fostering innovation while adhering to compliance mandates, providing a roadmap for financial institutions striving to leverage AI responsibly. Ultimately, we advocate for the integration of XAI as a means to mitigate risks associated with algorithmic bias and enhance the integrity of financial technologies, thereby contributing to a more equitable financial landscape.
 
Keywords: 
Explainable AI; Financial technologies; Regulatory compliance; Algorithmic bias; Fraud detection; Consumer trust
 
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