Generative AI in finance offers dynamic solutions for enhancing operations and driving progress. It holds significant promise in finance, offering functionalities including text summarisation, generation, data analysis and information retrieval. This article will explore how banks can effectively leverage generative AI to streamline operations and drive innovation.
Across the banking sector, institutions are increasingly intrigued by the capabilities of Generative AI, particularly Large Language Models (LLMs), such as GPT-3.5 and Gemini (ex BARD).
These models, trained on vast amounts of text data, enable tasks like language translation, text summarisation, and question answering, revolutionising our interaction with data and computers. Harnessing LLMs in finance streamlines tasks that once took minutes into mere seconds.
Explore 17 generative AI use cases in banking in our blog article.
- Text summarisation
- Text generation
- Data structuring and analysis
- Information retrieving and Q&A
Let’s dive into the details of using generative AI in banking and finance!
In finance, producing reports is crucial but extremely time-consuming. Our experience with financial clients revealed a significant obstacle: the laborious task of summarising extensive financial documents and reports from multiple sources and teams. Manually analysing, summarising, and generating insights from this data is not just slow—it’s daunting.
The solution lies in utilising LLMs for the summarisation of financial documents. By integrating LLMs into their workflow, finance professionals can overcome the challenges associated with traditional report generation. These sophisticated AI models can extract essential information from lengthy documents and transform it into concise, accessible summaries.
This improvement saves time and facilitates quicker decision-making, allowing professionals to dedicate more attention to strategic planning and innovation rather than getting bogged down in data analysis.
Employees often spend much time addressing standard, repetitive questions within the financial services department. Introducing an AI-powered Frequently Asked Questions (FAQ) bot offers an innovative solution to this challenge.
Designed for the finance team, this bot would pull information from various financial data sources to efficiently manage common and repetitive queries. These may include questions about financial transactions, account details, billing, and other standard matters.
The true value of an FAQ chatbot for banking lies in its versatility and accessibility. Making this resource available across different departments could act as a centralised tool for addressing common questions. This cross-departmental access ensures swift and effortless access to information for staff from different sectors.
Implementing such a solution doesn’t just offer a faster way for teams to access information. The time traditionally spent hunting for specific documents or digging for particular information can be significantly reduced. Overall, it directly translates to cost and time savings.
Integrating generative AI solutions into onboarding processes can significantly streamline the transition for new employees. Onboarding typically involves an influx of information, from company policies to procedures and equipment manuals, which can overwhelm newcomers.
With LLMs, financial organisations can create interactive and personalised training modules tailored to each team member’s needs, making information assimilation more manageable and efficient.
LLMs can be a comprehensive resource, providing access to onboarding documents, training materials, and essential procedural guides. This centralised approach simplifies the learning curve, enabling new employees to find information quickly and understand their roles better.
So, deploying generative AI in finance for onboarding aims to provide new hires with an efficient, clear path to becoming fully integrated and productive members of their new teams.
Data coming into tables and reports from various sources often creates processing bottlenecks.
An LLM could help analyse various sources and create a draft of a financial roadmap and plan. This approach offers several key benefits:
- Would allow stakeholders to quickly model complicated scenarios without having to go through the data manually and pick out the different parameters or change the scenarios.
- Speeds up the testing and analysing of different scenarios for a more dynamic understanding of potential financial futures.
- Expands scenario modelling beyond finance, allowing tailored models and prompts to be shared across departments. This broadens the inclusivity of financial planning, leading to more comprehensive strategies.
Ultimately, generative AI streamlines financial planning and analysis while democratising complex financial modelling. The result is more informed decision-making and reduced manual data processing overhead.
5. Forecasting and Strategic Planning with AI
Generative AI offers a powerful solution by sourcing information to enhance forecasting, covering short-term and long-term perspectives. It can analyse various documents, integrating insights from internal processes and external market conditions for strategic planning.
Users can input a specific set of documents and define their forecasting timeline. Utilising this data and a deep understanding of the company’s background and team dynamics, LLMs craft tailored forecasts, from immediate plans to long-term strategies.
While beneficial across multiple sectors, finance, legal and corporate development departments, in particular, have recognised the potential of generative AI to streamline their operations.
6. Market Research and Gathering Insights
LLMs extract crucial information, including pricing details such as current and historical market prices and sales prices. They excel at generating comparative analysis reports and providing pricing insights.
Also, generative AI in banking can analyse competitor data, helping departments identify trends and develop marketing strategies. They sift through unstructured data like text-heavy documents, reports, and articles, extracting insights and patterns to understand better market trends, competitor activities, and customer sentiments.
7. Budget Forecasting and Expense Analysis with LLMs
GenAI tools can analyse historical project data for budget forecasting, predicting overruns and suggesting adjustments based on past performance. They also categorise expenses and analyse cash flow patterns in project reports for finance departments, predicting potential liquidity challenges and identifying cost optimisation areas.
8. Financial Reporting Automation and Feedback Gathering
Another advantage of generative AI in finance is automating textual report generation by summarising key indicators and market trends. It understands stakeholders’ needs and creates customised and accurate reports in less time.
AI-powered FAQ bot, tailored to individual reports, accompanies reports to address follow-up queries. ELT and Sales teams gain rapid access to accurate information, saving time and enabling swift responses. For added convenience, this bot can be shared in a Teams channel, serving as a specialised resource for inquiries about reports.
Integrating LLMs into the system can automate invoice processing. They extract pertinent information from invoices, cross-reference it with project data, and ensure financial transaction accuracy.
Finally, generative AI can ensure regulatory compliance by monitoring financial documents and transactions against laws and regulations. They can flag potential compliance issues, assisting finance departments in a prompt resolution.
Discover how banks already leverage GenAI in Generative AI in Finance: Real-World Examples of LLMs in Banking blog article.
Generative AI offers diverse applications for banks and finance. Most traditional financial institutions may initially prioritise implementing employee-facing generative AI tools, considering the technology’s novelty and associated risks.
We at Tovie AI extend our expertise to help your organisation develop its AI strategy. We explore your organisation’s teams, roles, and functions through a consultancy project, collaborating to generate ideas. This process ensures stakeholders understand where LLMs and generative AI can add value before committing resources.
Following the pilot project, stakeholders gain a comprehensive understanding of the use cases of large language models within the business. They also witness real-world applications, enabling the evaluation of broader investments in AI for automation, efficiency gains, and cost reduction.
Discover how Tovie AI can help your organisation in developing a Generative AI strategy