# Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services

arXiv: https://arxiv.org/abs/2010.04827 · HTML: https://mlsanity.com/paper/2010.04827/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 8 of 8)

- [A Human-Centric Take on Model Monitoring](https://mlsanity.com/paper/2206.02868/) (2022)
- [Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook](https://mlsanity.com/paper/2103.03227/) (2021)
- [Seven challenges for harmonizing explainability requirements](https://mlsanity.com/paper/2108.05390/) (2021)
- [Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice](https://mlsanity.com/paper/2108.06217/) (2021)
- [On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services](https://mlsanity.com/paper/2111.01306/) (2021)
- [Beyond Mahalanobis-Based Scores for Textual OOD Detection](https://mlsanity.com/paper/2211.13527/) (2022)
- [Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings](https://mlsanity.com/paper/2312.02337/) (2023)
- [Ontology drift is a challenge for explainable data governance](https://mlsanity.com/paper/2108.05401/) (2021)

## Abstract (arXiv)

AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance practices have evolved from more traditional financial applications and modeling frameworks. They often struggle with the fundamental differences in AI characteristics such as uncertainty in the assumptions, and the lack of explicit programming. AI model governance frequently involves complex review flows and relies heavily on manual steps. As a result, it faces serious challenges in effectiveness, cost, complexity, and speed. Furthermore, the unprecedented rate of growth in the AI model complexity raises questions on the sustainability of the current practices. This paper focuses on the challenges of AI model governance in the financial services industry. As a part of the outlook, we present a system-level framework towards increased self-regulation for robustness and compliance. This approach aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities. The proposed framework also provides model governance and risk management improved capabilities to manage model risk during deployment.