What Is a Large Language Model? And Why It Matters for Compliance Teams
Large Language Models (LLMs) are a form of artificial intelligence that can interpret and produce natural-sounding text by learning from vast amounts of written data. While often associated with consumer applications, LLMs are beginning to reshape industries such as finance, compliance, and risk management.
LLMs are also being rapidly adopted across various industries, demonstrating their versatility and broad impact beyond compliance and finance.
For compliance teams facing increasing volumes of data, evolving regulatory demands, and the pressure to act swiftly, LLMs offer both potential and risk. These technologies are driving innovative solutions in compliance and other sectors. But what exactly is an LLM, and how should regulated firms approach its use?
What Is a Large Language Model (LLM)?
An LLM is a type of artificial intelligence and deep learning model developed using machine learning methods and trained on vast amounts of text. LLMs are trained on extensive datasets and vast training datasets to improve their understanding, capabilities, and overall performance. Unlike traditional rule-based systems, which follow fixed instructions, LLMs identify patterns in language to interpret context, generate responses, and summarise or classify information.
LLMs can be thought of as highly sophisticated prediction engines. They are capable of interpreting entire documents, answering questions, reasoning through complex language tasks, and performing a range of language-driven functions. These models can interpret human language and are designed for language related tasks. They process input data using large training datasets, enabling them to identify linguistic patterns and semantics at scale. LLMs can generate text that is coherent and contextually relevant.
They are built using transformer architectures, which are a type of neural network. Neural networks underpin the structure and training of LLMs, enabling them to process sequential data and manage long-range dependencies for advanced language understanding. Transformer architectures allow LLMs to process and generate language with impressive fluency and relevance. This is how large language models work: they process vast amounts of data, analyse context, and generate text that closely resembles human language.
Examples of widely used LLMs include GPT-4 (OpenAI), Claude (Anthropic), and LLaMA (Meta). These models do not rely on hard-coded rules. Instead, they learn from examples and apply that learning to new, unseen inputs.
Deep Learning and Language Models
Deep learning forms the foundation of how LLMs operate. By leveraging advanced neural networks, especially transformer models, LLMs are trained to understand the structure and meaning of human language.
Traditional deep learning models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been used in areas like sentiment analysis, translation, and summarisation. However, transformer-based LLMs advance these capabilities by processing large datasets more efficiently and accurately.
In compliance contexts, this means LLMs can review and summarise large regulatory documents, identify relevant clauses, and extract actionable insights to support faster and more accurate decision-making.
How Do LLMs Work? (In Simple Terms)
LLMs use transformer neural networks to detect patterns in language. These models are trained on billions of words from books, websites, and other sources.
LLMs generate responses based on user inputs, processing the information provided to produce relevant outputs.
Once trained, an LLM can:
Answer questions
Summarise documents
Classify content (e.g. is this adverse media?)
Extract data from large volumes of text
They do not “understand” language in the way humans do, but they are highly capable at identifying patterns and generating coherent, context-aware responses.
AI Technologies and Language Models
AI technologies have rapidly advanced in recent years, with large language models (LLMs) at the forefront of this transformation. These advanced AI systems are designed to interpret and generate natural language, making them invaluable for a wide range of applications. Through a rigorous training process involving vast amounts of text data, LLMs learn the intricate patterns and relationships that define human language.
In the realm of natural language processing, LLMs excel at complex tasks such as language translation, sentiment analysis, and generating contextually relevant responses. For compliance processes, this means LLMs can efficiently analyse regulatory documents, flag potential compliance issues, and provide clear, relevant answers to queries. By leveraging these advanced AI systems, compliance teams can navigate the complexities of regulatory language and requirements with greater speed and accuracy, ultimately enhancing their ability to manage risk and maintain compliance.
Applications in Regulatory Compliance and Risk Management
While LLMs are general-purpose models, their value becomes most apparent when applied to specific industry needs. In compliance and risk functions, LLMs can support tasks such as:
Adverse media screening – Analysing news and online sources to flag reputational risk
Name screening support – Helping analysts assess whether flagged matches are relevant or false positives
Regulatory intelligence – Monitoring regulatory changes and compliance across multiple jurisdictions
Alert triage – Categorising and summarising alerts for quicker response
They are particularly helpful in environments where unstructured data dominates, such as client onboarding documents, emails, case files, or media content.
LLMs can help identify potential compliance issues and mitigate compliance risks by interpreting regulations, analysing data, and flagging areas of concern. They also improve compliance efficiency by streamlining regulatory monitoring and reporting processes.
LLMs can also enhance the review of internal policies and external regulations, helping compliance teams to spot issues earlier and reduce oversight gaps.
Data Analysis and Large Language Models
Large language models are powerful tools for data analysis, capable of processing and interpreting large datasets to uncover valuable insights. By utilising sophisticated machine learning models, LLMs can identify trends, predict outcomes, and offer actionable recommendations that support informed decision-making. Their ability to automate routine tasks, such as data cleaning, categorisation, and preprocessing frees up compliance professionals to focus on more complex and strategic activities.
To maximise the benefits of LLMs in data analysis, it is crucial to use high-quality, diverse, and relevant training data. This ensures that the models can improve accuracy and deliver reliable results when analysing large datasets. By integrating LLMs into compliance workflows, organisations can enhance efficiency, reduce human error, and gain deeper insights from their data, all while maintaining a strong focus on regulatory requirements.
Benefits for Compliance Teams
Introducing LLM-powered tools can offer several key benefits:
Reduced manual work through document analysis and summarisation
Improved accuracy in screening and decision support
Faster response to emerging risks and changes
Multilingual processing for global operations
Support for human analysts, while keeping decision-making in human hands
Strengthened data handling, data management, and compliance monitoring processes
Enhanced alignment between policy and regulatory expectations
Automating complex tasks in compliance processes, improving productivity and efficiency
Improved data security and strengthened security measures throughout compliance operations
In some sectors, such as healthcare, LLMs also help reduce errors and streamline regulatory workflows. They support healthcare professionals by streamlining compliance, data management, and ensuring data security, which is critical for protecting sensitive medical information. While not exclusive to finance, the same benefits apply when handled responsibly.
Human Expertise and LLMs
Despite their power, LLMs are not a replacement for compliance professionals. Human judgement is essential to interpret outputs, provide regulatory context, and manage risk.
Experts also play an important role in ensuring the training data is unbiased and representative. Their input helps avoid introducing risk through poorly generalised or inaccurate outputs.
Combining AI with expert oversight enables more reliable outcomes, balancing speed and accuracy with regulatory integrity.
Risks and Limitations
As with any technology, LLMs have limitations. In regulated sectors, these risks must be clearly understood and actively managed:
Inaccuracy – LLMs may generate plausible but incorrect content
Lack of transparency – Their reasoning is difficult to trace in audit contexts
Data privacy – Especially relevant under GDPR when handling sensitive information
Regulatory uncertainty – Legal frameworks for AI are still evolving
Inconsistency – Results may vary depending on prompts or model versions
Legal and security risks – Legal risks such as bias, data misuse, and infrastructure vulnerabilities can introduce liabilities
Sensitive data exposure – Without safeguards, sensitive or personal data could be at risk
Compliance failure – LLMs may overlook obligations such as GDPR or sector-specific standards
Robust data governance, clear audit trails, and access controls are essential when deploying LLMs in compliance processes. The 'black box' nature of LLMs can create significant challenges for transparency and accountability in legal proceedings, especially during regulatory audits or judicial review.
Data Privacy Considerations
When deploying large language models, data privacy must be a top priority. LLMs require vast amounts of training data, which can sometimes include sensitive or confidential information. To safeguard data privacy, organisations should implement strong access controls, robust data protection measures, and adhere to all relevant regulatory requirements, such as those set by the European Medicines Agency.
Continuous monitoring and human oversight are essential to ensure that LLMs are used responsibly and that confidential data is not inadvertently exposed. By establishing clear security protocols and regularly reviewing model outputs, organisations can mitigate security risks and maintain compliance with data privacy regulations. Ultimately, a proactive approach to data protection helps build trust and ensures that the use of LLMs aligns with both legal obligations and ethical standards.
Ethical Considerations
The adoption of large language models brings important ethical considerations to the forefront. One of the primary challenges is the risk of biased data, which can lead to unfair or skewed outputs. To address this, it is essential to use diverse and representative training datasets, ensuring that the model reflects a wide range of perspectives and experiences.
Transparency and explainability are also critical. Organisations should provide clear information about the data sources, algorithms, and limitations of their LLMs, enabling users to understand how decisions are made. Additionally, LLMs should be deployed in ways that respect human autonomy and dignity, avoiding misuse or exploitation. By prioritising fairness, transparency, and accountability, organisations can ensure that their use of large language models upholds the highest ethical standards.
Regulatory Standards and Compliance
Firms using LLMs must align with industry standards and legal obligations. In finance, this includes GDPR and other data protection laws. In healthcare, it means respecting regulations such as HIPAA.
Compliance teams should work closely with technology providers to ensure models are trained and deployed in line with these requirements. This includes:
Using compliant training data
Embedding explainability into the system
Regularly validating outputs and flagging anomalies
By aligning development with regulation from the start, organisations can adopt LLMs without compromising data protection or legal obligations.
Responsible Use in Compliance Settings
LLMs should support compliance professionals, not replace them. Responsible use includes:
Keeping humans involved in all decisions
Ensuring outputs are reviewed and validated
Maintaining transparent audit trails
Aligning with the GDPR, AI Act, and other relevant standards
Using targeted fine-tuning to increase reliability
Generative AI and Code Quality
Generative AI, powered by large language models, is transforming the way organizations approach code quality and software development. LLMs can automate routine programming tasks, generate code snippets, suggest improvements, and even identify potential errors before they become issues. This not only enhances operational efficiency but also helps reduce the risk of human error in codebases.
To ensure that generated code meets organisational standards and is free from vulnerabilities, it is important to train LLMs on high-quality code datasets and fine-tune them for specific programming languages and tasks. Human expertise remains essential, reviewing and validating AI-generated code ensures alignment with business objectives and regulatory requirements. By combining the strengths of generative AI and skilled professionals, organisations can achieve higher code quality, streamline development processes, and support ongoing compliance efforts.
The Future of Compliance and LLMs
The adoption of LLMs will continue to grow, especially as regulatory frameworks mature and confidence in AI tools increases. In compliance, these models will help automate high-volume, repetitive tasks while improving risk detection and regulatory awareness.
At the same time, ethical and legal concerns will remain front of mind. Organisations must invest in upskilling compliance teams and developing responsible AI policies to ensure AI supports, rather than undermines, their regulatory obligations.
Emerging approaches such as retrieval-augmented generation (RAG) may further improve accuracy, transparency, and trust in AI systems, making them more suitable for compliance-critical environments.
Conclusion
LLMs represent a major step forward in AI capability. For compliance teams, their ability to process, summarise, and interpret unstructured data holds great promise. But responsible implementation is critical.
By combining these tools with human oversight and strong regulatory alignment, organisations can harness LLMs to increase efficiency, improve decision-making, and remain compliant in a rapidly evolving regulatory landscape.
Harness the power of Large Language Models responsibly.
If your compliance team is navigating complex regulations and data volumes, now is the time to explore LLMs. When deployed with care and oversight, these tools can significantly enhance efficiency, accuracy, and risk management.
Talk to us about implementing AI you can trust.