UK financial services firms are rapidly adopting Large Language Models (LLMs) as they look to reduce operational costs, enhance customer experience, and meet increasing regulatory demands. Beyond back-office automation, these technologies hold real potential to close the long-standing advice gap by delivering affordable, scalable support. 

At the same time, challenger banks and fintechs, unburdened by legacy infrastructure, are accelerating innovation. In this context, adopting LLMs is no longer experimental; it is essential for firms aiming to remain competitive and resilient in a fast-evolving market.

However, these benefits come with risks. While LLMs are powerful, they are far from perfect. Without appropriate safeguards, they can hallucinate facts, expose sensitive data, or reflect embedded biases.  These issues can carry serious regulatory and reputational consequences across all financial services operations. In such a tightly governed sector where every transaction and interaction carries inherent risk, it is imperative to adopt foundation models embedded with a structured risk framework.

At Aveni, we prioritise safety and compliance throughout the development lifecycle, implementing industry-leading AI governance practices inspired by the EU AI Act and guidance from the FCA and Bank of England. From responsible data sourcing and training to rigorous evaluation and continuous monitoring, we ensure transparency and domain relevance at every stage. While no AI system is entirely risk-free,  we help firms navigate and optimise trade-offs, balancing performance with control, and innovation with accountability. 

The path forward for LLM adoption is not about perfection (humans make mistakes too) but about delivering measurably better outcomes for more people, backed by robust safeguards. Aveni’s approach works within established regulatory and ethical boundaries, ensuring that solutions are not only performant but also trustworthy, resilient, and fit for purpose in impactful financial applications.

Understanding the real-world risks of LLMs in finance: 

These risks, if not addressed early, can impact everyone including customers, employees, firms, and even content owners. Even more, the initial impact often snowballs into larger consequences.  That is why FinLLM is built with deliberate design choices to mitigate these risks from the ground up.

Base model selection
We take a transparency approach right from the beginning, starting with the models we choose to build on. To make smart, informed decisions, we created a scoring system that helps us evaluate different model families against 10 key transparency criteria inspired by the Foundation Model Transparency Index which includes training data transparency, training process disclosure, licensing openness and safety benchmarking.

Responsible and transparent data collection
It all starts with data. FinLLM is constantly being pre-trained on AveniVault – our custom-built, finance-focused dataset made up of responsibly sourced, publicly available content. We stick to strict data governance standards, making sure everything we use complies with copyright rules, avoids toxic content, and respects opt-out requests. Where needed, we license proprietary data directly, and we also generate synthetic data to strengthen coverage in key financial areas. To expand our finance training corpus, we developed a custom classifier that accurately filters and tags general financial text with high relevance—enabling more efficient scaling of domain-specific data for LLM training.

Evaluation 
Evaluation is key to measuring model performance. We evaluate all models using AveniBench, our bespoke benchmark which includes use-case relevant finance-specific, general, and safety datasets to assess both performance and risk. Our safety evaluations zero in on the big risk areas like hallucinations, bias, toxicity, privacy breaches, IP issues, and jailbreak vulnerabilities. This helps us spot and fix problems before anything goes live. 

We use AveniBench, our own custom evaluation framework, which is constantly evolving to stay aligned with client needs. It brings together the most reliable and relevant datasets out there, from both the open-source community and our own bespoke collections, to make sure we are testing against real-world scenarios that matter.

Additionally, we have embedded the following essential architectural design attributes that support the responsible development and deployment of our models. 

• Use of domain specific, fine tuned models 
  FinLLM is not a single general-purpose model, but a suite of domain-specific, fine-tuned LLMs. Each model varies in size, transparency, and capability to align with different use cases, whether focused on tabular data, financial reasoning, or summarisation. This reduces dependency on large, opaque foundation models and enhances safety, performance, and control.

• Guardrails and filtering
  Guardrails act as safety filters between the model and the end user, preventing exposure to harmful, biased, or sensitive content. In FinLLM, we embed these controls early in the data pipeline, using comprehensive filtering to detect biased, toxic language and personal data, aligned with ICO guidance. Where needed, anonymisation is applied to protect privacy before training. Guardrails are also applied on model input and outputs to ensure safe responses to users. 

• Prompt engineering
  Prompt design directly shapes model behaviour. Standardised, well-tested prompts reduce variability, keep output aligned to tasks, and guard against prompt injection. In FinLLM, we work with subject matter experts to tailor system prompts based on user personas, workflows, regulatory constraints, and brand tone—ensuring each model is aligned to the environment and task it serves.

•  Retrieval-Augmented Generation (RAG)
  Retrieval-Augmented Generation (RAG) grounds model outputs in real-time, trusted data, reducing hallucinations. In FinLLM, our RAG layer ensures responses are based on up-to-date, customer records, product-specific information or internal vetted sources rather than static training data—critical for accuracy in financial use cases.

• Alignment layers (e.g. RLHF)
  Techniques like reinforcement learning from human feedback (RLHF) help align LLMs with human expectations, ethical standards, and business values. These post-training adjustments teach the model to behave in a more predictable and human-like way. In FinLLM we have identified training and evaluation datasets which will teach our models these human values.

• Explainability 
  Explainability features help trace the steps that a model took to reach its output, supporting auditability and accountability, especially in regulated environments. In FinLLM, this includes attribution in RAG (e.g. linking responses to source documents) and traceability in classification tasks, such as highlighting specific points in a call transcript where customer vulnerability was detected.

• Audit logging and traceability
  Right from the start, FinLLM has embedded good practices of maintaining comprehensive logging of user inputs, model responses, user IDs, and model versions to  support active monitoring of models during deployment for potential performance drift and/or safety degradation—particularly under regulations like SS1/23 and the EU AI Act.

•  Continuous monitoring and feedback loops
  LLM deployment risk varies by use case, and many clients will choose to adopt a co-pilot approach to maintain control. For each use case, we complete a Responsible AI User Impact Assessment to consistently assess and define its intended use, risk level, and acceptable outcomes, guiding the level of validation and monitoring required. Where appropriate,  we embed human-in-the-loop steps to review outputs (e.g. summaries, recommendations) for accuracy, IP risk, or to enable feedback for iterative  improvement.

What responsible AI looks like in practice

Responsible AI is not just about what a model can do—it is about how it is built and governed. In finance, where the stakes are high, foundation models bring huge promise. But tapping into that promise safely demands:

• Thoughtful design
• Multi-layered defense mechanism
• Clear trade-offs
• Continuous oversight
• Iterative development 

That is exactly what we have embedded into FinLLM, from domain-specific architecture and responsible data sourcing to rigorous evaluation and safety-first deployment. With new techniques emerging rapidly, we can not afford to stand still. At Aveni Labs, we stay ahead by collaborating with the University of Edinburgh and tracking the AI research community, applying the latest insights, data, and training methods to our work. 

Following up with a four-part technical series, we will take you behind the scenes of FinLLM to show how safety and governance are built into every layer of the model lifecycle. From responsible data sourcing to alignment, rigorous evaluation, and real-time guardrails at deployment—we will explore how each stage is engineered for trust, control, and compliance.

Read the original article here.