Every financial services firm is experimenting with generative AI. The potential is undeniable: streamlined operations, enhanced customer service, and unprecedented analytical capabilities. Yet most aren’t ready to govern large language models at scale, especially under the exacting standards of financial regulation.
The disconnect between AI ambition and governance reality represents one of the most pressing challenges facing financial services today. While generic AI models promise transformative benefits, they introduce unique risks that traditional governance frameworks weren’t designed to handle.
LLMs Are Powerful But Risky at Scale
AI adoption in financial services continues to surge, with spending projected to grow at an accelerated pace, reaching an estimated $97 billion by 2027. In 2025, over 85% of financial firms are actively applying AI in areas such as fraud detection, IT operations, digital marketing, and advanced risk modeling.
Yet this rapid adoption comes with significant governance challenges. Traditional Model Risk Management (MRM) practices often struggle with LLM governance, as third-party pretrained models typically provide limited visibility into their internal workings or training data.
The stakes couldn’t be higher. LLMs face issues with misinformation and hallucinations, where they generate inaccurate or seemingly plausible but fabricated information. Such hallucinations or misinformation are unacceptable in deployment and can lead to regulatory violations, substantial monetary losses, and erosion of trust between companies and their customers.
What Makes LLM Governance Different
LLMs present fundamentally different challenges compared to traditional AI systems. Understanding these differences is crucial for effective governance.
Black-Box Reasoning
Unlike traditional rule-based systems, LLMs operate through complex neural networks that make it difficult to understand how specific outputs are generated. LLMs can’t grasp cause-and-effect relationships or understand the logical flow of information. These limitations can lead to hallucinations where the generated text might be grammatically correct but ridiculous.
This opacity creates particular challenges in financial services, where regulatory requirements often demand explainability and auditability of decision-making processes.
Hallucinations and Probabilistic Output
The hallucination issue is recognized as a fundamental deficiency of large language models (LLMs), especially when applied to fields such as finance, education, and law. Our major finding is that off-the-shelf LLMs experience serious hallucinations in financial tasks.
An LLM hallucination occurs when the model “perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate.” Generative AI LLMs predict patterns and generate outputs based on vast amounts of training data.
For financial services firms, hallucinations can have severe consequences:
- Legal liability: Hallucinations can also create legal risk for enterprises, especially in industries with strict regulatory requirements, such as finance and healthcare. In such cases, an AI hallucination “could result in noncompliance and legal penalties.”
- Financial impact: When critical business decisions are made based on output from flawed AI models without human oversight, they can have detrimental financial repercussions.
- Reputational damage: When an AI hallucination produces a plausible but false statement, the reputation of the organization utilizing the LLM can suffer, potentially leading to market-share losses.
Model Drift and Prompt Injection
LLMs can exhibit performance degradation over time as real-world data distributions shift away from training data. Additionally, they’re vulnerable to prompt injection attacks where malicious inputs can manipulate the model’s behavior in unintended ways.
High Variation in Output Leads to High Burden on Oversight
AI hallucinations are the hidden cost drivers and risk factors in agentic AI workflows. A single gap in observability, governance, or security can spread like wildfire through your operations. Hallucinations don’t just point to bad outputs. They expose brittle systems.
The probabilistic nature of LLM outputs means that the same input can produce different results, making traditional quality assurance approaches insufficient and requiring more sophisticated oversight mechanisms.
The Compliance Challenge
Financial services firms face a complex web of regulatory requirements that make LLM governance particularly challenging.
SMCR, Consumer Duty, GDPR, Fair Treatment
The Senior Managers and Certification Regime (SMCR) places personal accountability on senior individuals for firm conduct. The SMCR, which is a highlight of the Update, should accommodate a “know-your-tech” (KYT) duty. Effective governance of AI is needed to ensure that the AI is properly understood, not only by the technology experts who design it but also by the firms who use it.
Consumer Duty requirements add another layer of complexity. The FCA responds to the principle that AI systems should not undermine the legal rights of individuals or organisations, discriminate unfairly against individuals or create unfair market outcomes by pointing to its rules for consumer protection and the recently adopted Consumer Duty regime.
To the extent that firms are using AI in the provision of services to consumers, firms will need to consider how this may impact upon their compliance with the consumer duty. Key questions which a firm may wish to ask itself are: How might the use of AI adversely impact our customers?
Explainability and Record-Keeping
The FCA notes that its regulatory framework does not specifically address the transparency or explainability of AI systems. It points, however, to high-level requirements, and principles under our approach to consumer protection, including the Consumer Duty, may be relevant to firms using AI safely and responsively in the delivery of financial services.
This creates a practical challenge: how do you explain decisions made by systems that operate as black boxes?
Auditability for Internal and External Scrutiny
Financial regulators, such as the European Banking Authority (EBA), require institutions to maintain detailed records of data lineage, model updates, and decision processes. Beyond regulatory requirements, clear, auditable documentation also underpins internal governance and risk oversight.
Governing LLMs: What Good Looks Like
Effective LLM governance requires moving beyond traditional AI risk management approaches to address the unique characteristics of these systems.
Model Carding and Versioning
Comprehensive documentation is essential. This includes:
- Model cards that provide transparency about model capabilities, limitations, and intended use cases
- Version control that tracks changes to models, training data, and configurations
- Lineage tracking that maintains records of data sources and model development processes
Guardrails and Prompt Filtering
To address this, institutions are shifting towards adaptive governance strategies that emphasize continuous monitoring and iterative validation post-deployment. This approach includes rigorous pilot testing within sandbox environments, real-time monitoring to detect performance drift, biases, or anomalies, and collaborative oversight involving compliance, data governance, and IT departments.
Multi-layered safety systems should include:
- Input filtering to detect and block potentially harmful prompts
- Output validation to check responses for accuracy and appropriateness
- Content moderation to ensure compliance with regulatory and ethical standards
Risk-Based Access Controls
Not all LLM applications carry the same risk. The future of AI oversight in financial services is moving toward a “sliding scale” approach, where the level of regulatory scrutiny correlates with the risk, sensitivity, and potential impact of each AI use case.
Access controls should reflect this risk-based approach:
- High-risk applications (credit decisions, regulatory reporting) require enhanced oversight
- Medium-risk applications (customer service, document drafting) need standard monitoring
- Low-risk applications (internal productivity tools) can operate with lighter governance
Human-in-the-Loop Escalation
Fine-tuning LLMs with high-quality, domain-specific datasets significantly reduces hallucinations. By narrowing the model’s focus, you ensure its responses are more accurate and aligned with your specific needs, especially in areas like healthcare, legal research, or finance.
However, human oversight remains crucial. Effective escalation procedures should:
- Define clear triggers for when human intervention is required
- Establish response timeframes for different types of issues
- Maintain audit trails of human decisions and interventions
Why This Matters in FS
The stakes in financial services are uniquely high because decisions have immediate legal and financial consequences.
Decisions Aren’t Optional – They Have Legal and Financial Consequences
Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs’ performance in understanding and interpreting financial regulations has rarely been studied.
When an LLM makes a recommendation about credit approval or investment strategy, that output directly impacts customer outcomes and regulatory compliance. There’s no room for “experimental” results in production systems.
Compliance Teams Must Trust Model Outputs, Not Just Monitor Them
Traditional monitoring approaches focus on detecting problems after they occur. In financial services, prevention is paramount. Validation of LLMs in financial contexts demands moving beyond traditional metrics like precision or recall, which often fail to capture nuances such as model hallucinations—outputs that are plausible yet incorrect.
Compliance teams need systems that provide confidence in model outputs before they’re used for decision-making.
Governance Needs to Be Built-In, Not Bolted On
AI oversight, risk management, and compliance must be embedded from the earliest stages of AI development—not bolted on as an afterthought.
Retrofitting governance onto existing AI systems is both expensive and ineffective. The most successful implementations integrate governance considerations from the beginning of the development process.
How FinLLM Solves for Scale
The challenges outlined above require purpose-built solutions designed specifically for the regulatory environment of financial services.
Transparent Architecture, Explainable by Design
FinLLM addresses the black-box problem through systematic transparency. As detailed in Aveni’s technical documentation, the model provides:
- Complete model documentation including training data sources, architectural decisions, and performance characteristics
- Explainable outputs with clear reasoning chains for key decisions
- Transparent updating with documented version control and change management
Unlike generic models where firms have no visibility into training data or architectural choices, FinLLM provides full transparency into its development and operation.
Full Audit Trail of Prompts and Responses
Every interaction with FinLLM is logged and traceable, providing:
- Complete interaction history for regulatory audit purposes
- Prompt and response logging to understand decision pathways
- User attribution to maintain accountability
- Temporal tracking to understand how responses change over time
This comprehensive audit trail supports both regulatory compliance and internal quality assurance processes.
Tuned for FS-Specific Risk and Compliance Use Cases
FinLLM is specifically designed for financial services applications, including:
- Regulatory compliance with built-in understanding of FCA, PRA, and EU regulations
- Consumer Duty alignment through systematic bias detection and fair treatment protocols
- Risk-appropriate responses calibrated for different types of financial decisions
- Industry-specific training on UK financial services data and regulatory requirements
Optionality for Private Deployment and Control
Even when public-cloud services are acceptable, it is critical that contracts explicitly address security standards, data residency requirements, and business continuity obligations.
FinLLM offers deployment flexibility:
- On-premises deployment for firms with strict data residency requirements
- Private cloud options for enhanced security and control
- Hybrid architectures that balance security with operational efficiency
- Full data sovereignty ensuring sensitive information never leaves controlled environments
This deployment flexibility allows firms to balance innovation with regulatory requirements and risk tolerance.
The Path Forward
Instead of fighting hallucinations, treat them as diagnostics. They reveal exactly where your governance, observability, and policies need reinforcement—and how prepared you really are to advance toward agentic AI.
Financial services leaders should ask themselves:
- Do we have full lineage tracking for our AI systems?
- Can we trace where every decision or error originated and how it evolved?
- Are we monitoring in real time for concept drift, prompt injections, and data quality issues?
- Have we built strong intervention safeguards that can stop risky behavior before it scales?
The FCA has further underscored its regulatory commitment to fostering the safe and effective implementation of artificial intelligence (AI) within financial services. “Our goal is to give firms the confidence to invest in AI in a way that drives growth and delivers positive outcomes for consumers and markets, while at the same time offering us insights into what is needed to design and deploy responsible AI.”
The regulatory direction is clear: innovation is encouraged, but only when accompanied by robust governance frameworks that protect consumers and maintain market integrity.
The question for financial services leaders isn’t whether to adopt LLMs—it’s whether to adopt them responsibly. The firms that get this right will gain significant competitive advantages. Those that don’t may find themselves facing regulatory scrutiny, operational failures, or worse.
The time for experimentation is ending. The era of governed, enterprise-grade AI deployment has begun.
Ready to explore how FinLLM can support your firm’s AI governance requirements? Learn more about our comprehensive approach to responsible AI deployment in financial services.
