# A Conceptual Model for AI Adoption in Financial Decision-Making: Addressing the Unique Challenges of Small and Medium-Sized Enterprises

arXiv: https://arxiv.org/abs/2512.04339 · HTML: https://mlsanity.com/paper/2512.04339/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls](https://mlsanity.com/paper/2107.04851/) (2021)

## Built on by (most cited first, 1 of 1)

- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)

## Abstract (arXiv)

The adoption of artificial intelligence (AI) offers transformative potential for small and medium-sized enterprises (SMEs), particularly in enhancing financial decision-making processes. However, SMEs often face significant barriers to implementing AI technologies, including limited resources, technical expertise, and data management capabilities. This paper presents a conceptual model for the adoption of AI in financial decision-making for SMEs. The proposed model addresses key challenges faced by SMEs, including limited resources, technical expertise, and data management capabilities. The model is structured into layers: data sources, data processing and integration, AI model deployment, decision support and automation, and validation and risk management. By implementing AI incrementally, SMEs can optimize financial forecasting, budgeting, investment strategies, and risk management. This paper highlights the importance of data quality and continuous model validation, providing a practical roadmap for SMEs to integrate AI into their financial operations. The study concludes with implications for SMEs adopting AI-driven financial processes and suggests areas for future research in AI applications for SME finance.