The biggest headache for fintechs right now is what I call the explainability gap. It's not enough to just show the results anymore. Regulators are moving past simple outcome monitoring; they want a granular look at exactly why the AI made a specific call. The real nightmare is proxy discrimination. An AI might find variables that seem neutral on the surface but actually correlate with protected classes. That creates a black box bias that's incredibly hard to defend when you're sitting through a fair lending audit. Documenting these decisions has also shifted completely. We've moved away from static reports to live, versioned audit trails. If you look at the EU AI Act, using AI for creditworthiness is explicitly labeled high-risk. That triggers a massive need for rigorous data governance and human oversight. We're seeing firms pivot toward automated logging that captures everything--the exact model version, the data lineage, and the confidence scores for every single transaction. You need to be able to reconstruct a decision months after it happened. Navigating this requires a total mindset shift. You aren't just building a smart tool; you're building a defensible process. The reality is that the technology is usually ready long before the governance framework is. That gap is where the real enterprise risk lives for most fintech operators. If the tech outpaces your ability to explain it, you're in trouble.
FinTech's face several compliance challenges when implementing AI-based approaches for financial decision making. With regards to investment management in general, the most important consideration for compliance professionals involves data security and data privacy. For investment management firms, this is fundamental to their fiduciary obligations for clients, namely safeguarding client data. The best practice in this area for investment firms is to apply their existing data security and data privacy standards to all AI-based approaches that are being considered or used within the investment firm. The key question to ask is whether sensitive client data is being exposed to AI-based tools, including large language models (LLMs). Every effort should be made to keep client data private and if this cannot be demonstrated by a given AI-based approach, investment firms should re-evaluate and look to vendor solutions that safeguard client data with demonstratable methods. Next is the area of governance which is key for compliance. AI-based approaches and tools should have a robust set of controls and governance layer built into their solutions. Investment firms should seek to understand how an AI-based approach works on a technical level. Key controls include being able to "shut down" an AI tool on an "ad hoc" basis (i.e. a "kill switch") and be able to specify what a given AI tool can be used for. Use cases for investment firms involving AI include investment research, data analysis, data formatting and output (i.e. reporting) and agentic AI for administrative tasks related to investment management. Firms may seek to have limited application of AI or a comprehensive set of use cases depending on their fiduciary responsibilities to clients and overall risk tolerance. Another important consideration is interoperability of AI-based approaches. For example, different LLMs can be assessed based on the individual investment firm's standard for risk. Based on that, a given LLM may not be fit-for-purpose for a given use case. Additionally, as AI tools like LLMs continue to evolve rapidly, firms need the ability to switch quickly and easily from one provider to another based on industry events and situations that raise the risk profile of a given LLM. By assessing AI through the framework of compliance, financial firms using fintech solutions involving AI will be able to successfully prepare themselves for upcoming AI Act requirements.