AI integration in financial services: a systematic review of trends and regulatory challenges
Abstract
The integration of Artificial Intelligence (AI) into financial services represents a developmental shift in the industry, presenting unprecedented opportunities and challenges. This scientometric review examines the evolution of AI in finance from 1989 to 2024, analyzing its pivotal applications in credit scoring, fraud detection, digital insurance, robo-advisory services, and financial inclusion. The analysis reveals significant trends, particularly the growing adoption of machine learning, natural language processing, and blockchain technologies in reshaping financial operations and decision-making processes. The review addresses critical regulatory and ethical challenges, emphasizing the imperative for explainable AI (XAI) and robust governance frameworks to ensure transparency, fairness, and accountability in AI-driven systems. Despite rapid advancements, persistent gaps remain, the most notable of which is the lack of standardized frameworks for AI implementation across financial sectors. The findings support the need for a balanced approach that promotes innovation while addressing ethical, regulatory, and societal concerns. This comprehensive synthesis maps the trajectory of AI in finance, identifies key areas for future research, and recommends interdisciplinary collaboration to advance responsible and sustainable AI integration within the financial ecosystem.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.