Financial services leaders know that trust takes years to build and seconds to lose. Recent research reveals the stark reality of AI incidents in financial services: AI incidents cause an average short-term loss of 21% in stock value for affected banks and financial institutions, with negative impacts spreading across the entire financial industry. (3)
The regulatory environment has intensified dramatically. FCA fines tripled to £176 million in 2024 (1), while the EU AI Act now imposes fines of up to 7% of annual global turnover for AI non-compliance (2). Meanwhile, 84% of UK financial firms identify “safety, security and robustness of AI models” as their primary constraint to AI adoption. (4)
At Aveni, we’ve taken a different approach with FinLLM. Rather than retrofitting generic AI with basic safety measures, we’ve built comprehensive protection into every layer of development. Here’s how this directly benefits your organisation.
Why Generic AI Safety Falls Short in Finance
Consumer chatbots focus on avoiding offensive content. Financial AI must navigate complex regulatory requirements, fiduciary duties, and systemic risk considerations. A model that hallucinates mortgage rates, provides unauthorised investment guidance, or fails to detect customer vulnerability poses risks that standard safety training cannot address.
Consider the 2020 case where a major UK bank’s algorithm systematically discriminated against customer segments, resulting in regulatory action and millions in fines. Generic bias detection would have missed this entirely, while financial-specific monitoring would have caught it early.
The consequences in financial services go beyond embarrassment. They include regulatory breaches, reputational damage, and real financial harm to customers. That’s why FinLLM needed a fundamentally different safety approach from day one.
Read how AI hallucinations create specific risks for financial firms and what to do about them →
Our Multi-Layered Safety Approach
Data Collection: Clean Foundations
We go beyond simply scraping the internet. FinLLM learns exclusively from legitimate UK financial sources and regulatory websites. Our data collection includes:
Risk-Based Pseudonymisation: Three levels of personal data protection, from preserving public figure context (Level 0) to full anonymisation of sensitive data (Level 2). This protects privacy while maintaining the factual accuracy your clients depend on.
Toxicity and Bias Filtering: We use industry-leading tools to identify and remove harmful content before it reaches our training data. Our bias detection covers race, gender, religion, ability, and violence across multiple categories.
Regulatory Alignment: Every data source is evaluated for compliance with UK financial regulations, ensuring FinLLM understands your regulatory environment from the ground up.
The result is a foundation built on clean, relevant data that reflects the actual regulatory and business environment your firm operates within.
Training: Purpose-Built for Finance
While generic models learn from everything, FinLLM is trained specifically on financial contexts. This targeted approach means:
Reduced Hallucinations: Financial-specific training data and continuous expert validation dramatically reduce false information. Where generic models might confidently state incorrect mortgage rates, FinLLM understands the importance of accuracy in financial contexts.
Regulatory Awareness: Built-in understanding of FCA principles, Consumer Duty requirements, and EU AI Act compliance. The model learns regulatory language and requirements as part of its core training, not as an afterthought.
Vulnerability Detection: Training on anonymised call transcripts helps identify when customers may be vulnerable, supporting better outcomes and regulatory compliance.
This focused training creates a model that understands financial services from the inside out, not one that requires extensive fine-tuning to work in your environment.
Discover how quality assurance is evolving with AI-powered monitoring across financial firms →
Continuous Evaluation: Measurable Safety
We don’t just promise safety; we measure it. FinLLM is evaluated against specific benchmarks for:
Toxicity: Preventing harmful content generation that could damage client relationships or create liability.
Bias: Ensuring fair treatment across demographic groups, crucial for compliance with equality legislation and regulatory expectations.
Misinformation: Maintaining factual accuracy in financial contexts, where incorrect information can have significant consequences.
Privacy: Protecting sensitive customer information throughout all interactions.
Alignment: Ensuring outputs match your firm’s values and regulatory requirements consistently.
Each risk category requires dedicated evaluation methods, mitigation strategies, and monitoring systems. This separation is critical because addressing one risk category can sometimes create problems in another. Our comprehensive approach ensures all dimensions are covered.
Business Impact: What This Means for Your Organisation
Risk Reduction
A comprehensive safety framework reduces your exposure to regulatory violations, reputational damage, and operational errors. When regulators ask about your AI governance, you have concrete evidence of proactive risk management rather than reactive damage control.
The framework provides documented evidence of due diligence, systematic risk assessment, and ongoing monitoring that regulators increasingly expect from AI deployments in financial services.
Regulatory Confidence
FinLLM’s alignment with FCA principles and EU AI Act requirements means you’re ahead of regulatory expectations, not scrambling to catch up. Our transparent approach to safety provides the documentation regulators increasingly demand.
The EU AI Act classifies many financial AI systems as “high-risk,” imposing strict requirements for transparency, accountability, and ongoing monitoring. FinLLM was designed with these requirements in mind from the start.
Operational Efficiency
Safe AI is reliable AI. By preventing errors, bias, and hallucinations at the source, FinLLM reduces the need for constant human oversight while maintaining the quality standards your clients expect.
This reliability means your teams can focus on strategic work rather than constantly checking AI outputs for errors or compliance issues.
Learn how comprehensive AI governance accelerates deployment while ensuring compliance →
Client Trust
Your clients trust you with their financial future. Using AI systems built specifically for financial contexts, with comprehensive safety measures, demonstrates your commitment to their wellbeing and regulatory compliance.
When clients see that your firm uses purpose-built AI rather than generic tools, it reinforces your professionalism and attention to detail in all aspects of your service.
Beyond Compliance: Building Competitive Advantage
While competitors struggle with generic AI that requires extensive customisation and risk mitigation, FinLLM provides a foundation built for your industry. This means:
Faster Implementation: Less time adapting systems for financial use cases means quicker time to value and competitive advantage.
Lower Risk: Purpose-built safety reduces regulatory and operational exposure, allowing for more confident deployment.
Better Outcomes: AI that understands financial contexts delivers more relevant, accurate results that clients and regulators can trust.
Regulatory Leadership: Demonstrate proactive risk management to regulators and auditors, positioning your firm as a responsible innovator.
The safety framework becomes a business enabler rather than a constraint, allowing you to capture AI benefits while managing risks appropriately.
Practical Governance Architecture
Effective AI governance requires clear accountability structures and operational processes. We’ve established a tiered approach:
AI Governance and Ethics Board: Provides ethical oversight, safety assurance, and regulatory compliance across the entire AI development pipeline.
Specialised Working Groups: Focus on specific domains including safety evaluation, information security, and data governance.
Operational Teams: Execute governance decisions with clear responsibilities and reporting structures that ensure accountability flows from board level to day-to-day operations.
This structure enables thorough oversight while maintaining operational efficiency, ensuring governance enhances rather than impedes business objectives.
Looking Ahead: AI Safety in Financial Services
AI safety isn’t a destination; it’s an ongoing process. Our roadmap includes enhanced bias mitigation, expanded preference optimisation, and deeper integration with emerging regulatory requirements.
We’re also collaborating with industry leaders like Lloyds Banking Group and Nationwide Building Society to ensure our safety measures evolve with real-world needs and regulatory developments.
The regulatory landscape continues to evolve rapidly, and our governance framework is designed to adapt and stay ahead of changes while learning from industry best practices.
The Aveni Difference
While other AI companies treat safety as an afterthought, we’ve made it fundamental to how FinLLM operates. This isn’t about limiting AI capabilities; it’s about channelling them responsibly to serve your clients and protect your firm.
When regulators ask tough questions about AI governance, you’ll have concrete answers backed by measurable results. When clients depend on your advice, they’ll benefit from AI systems designed specifically for their needs. And when your team uses FinLLM, they can focus on delivering value rather than managing risk.
Financial services demands accountability at every level. FinLLM delivers it by design.
The question isn’t whether your firm will adopt AI – it’s whether you’ll adopt AI that was built for your industry’s unique requirements and risks. The difference will determine your competitive position and regulatory standing in the years ahead.
Ready to see how FinLLM’s safety-first approach can protect and empower your organisation? Get in touch to learn more about our approach to responsible AI in financial services.
References
- https://ifamagazine.com/guest-insight-fcas-record-breaking-year-of-fines-how-can-companies-stay-compliant/
- https://artificialintelligenceact.eu/article/99/
- https://scholarworks.sjsu.edu/faculty_rsca/5723/#:~:text=By%20analyzing%20five%20U.S.%20banks,day%20loss%20of%20%2D0.13%20%25.
- https://www.bankofengland.co.uk/report/2024/artificial-intelligence-in-uk-financial-services-2024
