Understanding large language models in financial services.
Curious how large language models (LLMs) work in UK corporate finance? Explore real-world applications and business outcomes, along with best practices for implementing LLMs into your workflow.
With each passing year, financial institutions process more and more sensitive data such as reports, regulatory documents, and customer communications. However, manual processing methods are inefficient, resulting in errors that can cost businesses huge amounts of time and money.
Enter AI tools.
AI helps streamline these tasks through automation, from flagging inconsistencies in compliance reports to identifying patterns that could indicate fraud. That’s part of the reason AI adoption is growing rapidly, with many companies in the UK introducing it as they brace themselves for the new Making Tax Digital (MTD) initiative.
Not all AI tools are created equally, however. LLMs are a type of AI that can process and analyse unstructured data by using neural networks to sift through and find meaning in large datasets. There are many LLM use cases in finance, including risk analysis, financial forecasting, and compliance.
What are LLMs?
An LLM is a specific type of AI model trained on massive amounts of data. By undergoing self-supervised learning on huge datasets of text, LLMs can understand natural human language and analyse, summarise, and generate text as required.
LLMs use a probability-based analysis of data called deep learning. This allows the AI to recognise the differences between content and understand how characters, words, and sentences function without human input.
You can refine LLMs further by training them on specific datasets or providing prompts that help align the model with a particular task. For example, you could train an LLM on thousands of historical financial reports to help it generate more accurate, complete, and consistent reports with new data in the future.
LLMs differ from traditional rule-based AI models as they do not need to stay within specific, predefined parameters. For example, traditional AI and machine learning largely rely on structured data for reliable processing and analysis. However, LLMs train with unstructured data, such as regulatory documents and financial reports. This allows the AI to recognise patterns and generate forecasts without needing fixed rules or heavy up-front data processing.
How LLMs are transforming financial operations.
The UK financial services industry requires significant amounts of data for crucial tasks, such as reducing risk and detecting fraud. AI is much more powerful than a human in its ability to analyse large amounts of data to provide these essential insights.
Embracing AI and LLMs can benefit financial services companies by helping them adapt their strategies and identify new opportunities based on extracted insights.
AI adoption in finance functions within organisations increased from 37% in 2023 to 58% in 2024. Among these, 44% of finance departments utilised AI to enhance the efficiency of their current automation tools.
LLMs provide these benefits without needing to replace human jobs. Research shows that AI-powered automation tools can save employees more than two hours per day. By handling data-heavy tasks, they free employees to focus on more high-value strategic work. This is similar to AI in HR applications and other industries, where automated tools inform decisions rather than replace human expertise.
LLMs vs. traditional financial analytics tools.
Traditional financial analytics tools, such as business intelligence, rely on structured data and predefined rules to analyse historical data and provide insights.
Meanwhile, LLMs in finance are able to analyse historical data while simultaneously analysing current trends to provide real-time insights and future predictions. LLMs do this by leaning on statistical relationships in the data rather than following fixed rules.
Data Type
LLMs
Process structured and unstructured data, such as financial market news and HMRC filings.
BI and Traditional Tools
Primarily work with structured data, such as databases and financial metrics.
Analysis Approach
LLMs
Predictive and adaptive.
BI and Traditional Tools
Historical and rules-based.
Decision-making
LLMs
Recognise context and adjust to new information for intelligence-driven recommendations.
BI and Traditional Tools
Operate on predefined rules and parameters.
Real-time Processing
LLMs
Yes, analyse and interpret data continuously for dynamic insights.
BI and Traditional Tools
Limited; primarily process data in batches for periodic reporting.
Automation and Adaptability
LLMs
Continuously learn from new data to provide up-to-date insights and refine their capabilities.
BI and Traditional Tools
Require manual rule updates for refinement.
Use Cases In Finance
LLMs
Fraud detection, compliance monitoring, risk analysis, and customer service automation.
BI and Traditional Tools
Financial reporting, KPI tracking, and historical trend analysis.
LLMs and traditional tools both have their place in the finance industry. Business intelligence tools handle structured reporting and KPI tracking well. However, the real-time data processing of LLMs makes them more suitable for dynamic tasks such as fraud detection, risk analysis, and compliance monitoring.
Five business benefits of LLM implementation in finance.
One of the major roadblocks to implementing LLMs at an organisational level is stakeholder buy-in. That’s where a solid understanding of the potential benefits is pivotal. Here are five areas where LLMs are driving major results in finance:
1. Accessibility for financial institutions of all sizes.
You don’t need AI or data science experts on your team to take advantage of most modern AI tools. Many software vendors integrate LLMs into their products to optimise everyday business applications, such as financial planning tools and CRMs.
This natural shift to more AI-heavy operations has made LLMs more accessible to businesses of all sizes. Hiring a dedicated team to work with the AI tools is no longer necessary – with the right LLM, your entire workforce will be AI experts.
2. Faster processing = lower costs.
Think of all the manual tasks that your team spends hours on each week, such as compliance reporting and contract analysis. LLMs process massive datasets, documents, and reports in seconds. This reduces the need for manual work, which can help finance teams work more efficiently.
According to Insightful AI, targeted AI automation in customer service, admin, and inventory can slash UK SMB operational costs by 20-45%, resulting in a substantial gain over competitors.
3. Better experiences with AI-driven insights.
An overwhelming 84% of customers prefer companies that provide customised experiences. LLMs power chatbots and automated customer service tools that deliver quick and tailored assistance. These chatbots not only provide useful information to users with limited friction, but they can also escalate issues to a human agent when required.
Meanwhile, banks and financial firms can use LLMs to offer personalised financial advice based on customer data and goals. The opportunities for LLMs to provide insights across the customer journey are genuinely limitless.
4. Smarter decision-making with predictive analytics.
LLMs analyse transaction histories, customer sentiment and economic trends to identify hidden patterns in customer behaviour and financial markets. This helps financial businesses make data-driven decisions regarding operations, investments and future planning.
Half of finance businesses using AI note a 20% decrease in forecasting errors, and 25% see an error reduction of 50% or higher. Companies that fail to implement AI in a meaningful fashion risk getting left behind.
5. Stronger compliance monitoring and risk management.
UK financial institutions are facing a rise in regulatory complexity. They must now track law changes to prevent regulatory breaches regarding consumer protection, such as the Financial Conduct Authority's (FCA) Consumer Duty. Reporting requirements must be updated on an ongoing basis. LLMs can help meet this challenge by analysing compliance documents and legal filings to flag potential risks and fraud as soon as possible.
Financial institutions of all sizes, trade associations, and anti-fraud providers have successfully reduced fraud activity by as much as 50% by implementing AI-driven models that analyse their proprietary historical data.
Navigating LLM adoption challenges in financial services.
It’s not just stakeholder buy-in that can make implementing LLMs in finance functions a challenging process. Before adding an LLM to your financial operations, consider these potential challenges.
- Technical concerns: LLMs require clean, accessible, and accurate data. Data filled with errors or scattered across different systems can interfere with their performance. A lack of cloud systems and high-performance CPUs can also make it difficult for LLMs to access and analyse data.
- Integration challenges: It can be tempting to replace entire systems with LLMs. However, they typically work best when embedded into targeted workflows, such as fraud detection and compliance.
- Expertise gap: Scaling and monitoring LLM systems requires specialised skills and expertise that financial firms may lack.
- Governance and oversight: Adopting LLMs without a clear, structured plan can lead to biased decision-making and regulatory risks. An established AI governance framework can reduce errors and maintain trust and transparency.
- Ethical concerns: Biased AI models can affect equitable lending decisions, investment recommendations, and other financial services that your customers rely on your company for. This is where a trusted partner with responsible AI makes all the difference.
Essential elements of an LLM Strategy for financial services organisations.
Rushing into LLM adoption without a clear strategy can drain resources and cause compliance issues, such as biased lending decisions, financial reporting mistakes, and privacy breaches. Worse, those errors will likely compound over time, rapidly reducing the efficacy of the LLM.
The solution? Start small when adopting LLMs. Identify specific problems that AI models can solve, such as analysing risk patterns and automating compliance reporting. This allows teams to test LLM capabilities in a more controlled environment, refine models, and scale to include more complex tasks.
AI models only perform as well as the data they learn from, so high-quality data is necessary for them to work properly. Your data should be clean, consistent, unbiased, and consolidated for accurate LLM financial analysis, fraud detection, and compliance reporting. Establish a governance framework to check for bias and monitor AI decisions to maintain reliability and trust in your tools.
As businesses expand, they need more computing power to process data efficiently. Cloud-based platforms provide this power through remote access to high-performance computing, optimised storage, and scalable processing capabilities. They don’t require physical hardware to work, so they can reduce up-front and maintenance costs while adapting to growing AI workloads.
Finally, build a team of finance and data professionals who collaborate to ensure your tools align with your business needs. Your team can refine AI solutions to keep them accurate, practical, and trustworthy while delivering real business value. When the right people are in place, consider how AI can play a part in future recruitment within your organisation.
Measuring LLM success in financial operations.
The real test of your LLM investment is its measurable value. LLMs in banking and finance law, risk assessment, and other disciplines should simplify financial operations processes, enhance decision-making, and reduce human error. Track success by looking at the impact of LLMs on your business:
- Return on investment (ROI): You need to invest in LLMs to implement them, so they should save money or drive revenue to justify their cost. If an AI-powered process saves 500 manual labour hours monthly, translate that into GBP to see the financial benefit.
- Efficiency gains: Monitor how much faster LLMs can complete tasks. For example, it’s a clear win if LLMs reduce compliance review times by 60%.
- Accuracy: Compare the accuracy rates of LLM outputs to previous methods. You should see errors reduce significantly in forecasting, fraud detection, and other areas to know your models are working.
- Adoption and engagement: To get the most out of AI tools, people need to feel comfortable using them. Track employee adoption rates, usage, and feedback to understand their value to your teams.
- Critical KPIs: Monitor the impact of your LLM tools on your company’s key metrics, such as gross profit margin or incident response time, which can help you determine if your AI investment is delivering real value.
How Workday leverages LLMs to transform financial operations.
At Workday, AI is at the core of our finance systems. That means no awkward integrations or lengthy implementation, ensuring your financial teams can work smarter and faster straight out of the gate.
Our LLM-powered tools analyse financial data to detect risks and automate reporting in real time. Workday AI integrates seamlessly with financial planning and analysis tools and workflows, providing businesses with deeper insights for better decision-making.
Workday helps you manage your data and adapt to changing regulations to maintain its tools’ compliance. New to AI? Join our AI masterclass to help you become comfortable with AI tools and processes. This training can help teams feel more confident about incorporating AI into their everyday operations.
Many companies have seen reduced costs and gains in efficiency and risk mitigation from Workday and our AI-powered tools. One example is Salesforce, which uses Workday to automate its financial data integration and reporting. With the help of Workday, the company set up a new legal entity structure in minutes rather than months.
The future of LLMs in financial services.
The financial industry is swiftly adopting LLMs. GenAI already accounts for 10% of all data produced, rising rapidly as more organisations adopt the technology. Financial institutions will likely see major efficiency gains as AI tools become standard in everyday operations.
Leaders are recognising what an LLM in corporate finance is and finding new ways to use it. LLMs can automate report writing, summarise financial data, and enhance risk analysis, making processes more efficient and accurate.
However, responsible AI use must coincide with this growth. The European Union’s AI Act regulates how UK finance teams must develop and use AI. Keeping up-to-date with new regulations can help you use AI responsibly and legally.
AI is swiftly becoming more integrated into all industries. Thoughtfully embracing Workday AI can position you to stay competitive.
Workday AI moves you forever forwards