International Journal of Social Science & Economic Research
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Title:
Artificial Intelligence and Machine Learning in Financial Services: A Systematic Literature Review based on the TCCM framework

Authors:
Aarushi Mahesh Agarwal

Journal: International Journal of Social Science and Economic Research (IJSSER)
ISSN: 2455-8834
Volume: 11
Issue: 10
Publication Date: October 2026
DOI: 10.46609/IJSSER.2026.v11i10.007

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Aarushi Mahesh Agarwal
Chatrabhuj Narsee School

MLA 8
Agarwal, Aarushi Mahesh. "Artificial Intelligence and Machine Learning in Financial Services: A Systematic Literature Review based on the TCCM framework." Int. j. of Social Science and Economic Research, vol. 11, no. 10, Oct. 2026, pp. 5471-5498, doi.org/10.46609/IJSSER.2026.v11i10.007. Accessed Oct. 2026.
APA 6
Agarwal, A. (2026, October). Artificial Intelligence and Machine Learning in Financial Services: A Systematic Literature Review based on the TCCM framework. Int. j. of Social Science and Economic Research, 11(10), 5471-5498. Retrieved from https://doi.org/10.46609/IJSSER.2026.v11i10.007
Chicago
Agarwal, Aarushi Mahesh. "Artificial Intelligence and Machine Learning in Financial Services: A Systematic Literature Review based on the TCCM framework." Int. j. of Social Science and Economic Research 11, no. 10 (October 2026), 5471-5498. Accessed October, 2026. https://doi.org/10.46609/IJSSER.2026.v11i10.007.

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ABSTRACT:
Purpose: The literature on Artificial Intelligence (AI) and Machine Learning (ML) in financial services remains dispersed across finance, computer science, management, and law, with no single study integrating these strands. This paper performs a systematic review to consolidate this knowledge and propose further areas of research.
Design/methodology/approach: This study applies the Theory, Context, Characteristics and Methods (TCCM) framework (Paul and Rosado-Serrano, 2019) to synthesise the literature. Scopus served as the sole database; an initial pool of 550 records was reduced to a final sample of 37 papers through successive rounds of subject, document-type, language, and relevance screening.
Findings: The reviewed literature consists predominantly of publications in high-quality journals, with almost three-quarters in Q1/Q2 outlets across finance, economics, management, and law, confirming its multi-disciplinary nature. The geographic focus is mostly multi-country and developed-market, reflecting where digital financial infrastructure is most advanced. The underlying theories are almost entirely long-standing frameworks such as EMH and UTAUT rather than AI-native theories, indicating that AI/ML is more often used to re-examine existing theories than to generate new ones.
Practical implication: The results give researchers an aggregated map of theoretical, contextual, and methodological gaps, and offer financial institutions and regulators a guide to where AI/ML deployment and research is concentrated.
Originality/value: This is the first paper to apply the TCCM approach to systematise the scattered literature on AI/ML-based solutions in financial services.

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