Generative AI expanded with mainly B2C applications like ChatGPT. However, enterprise generative AI, particularly in the financial planning sector, has unique challenges and finance leaders are not aware of most generative AI applications in their industry which slows down adoption. This unawareness can specifically affect finance processes and the overall finance function.
In this article, we explain top generative AI finance use cases by providing real life examples. These examples illustrate how generative artificial intelligence is revolutionizing the field by automating routine tasks and analyzing historical finance data. If your focus is just banking, a subset of these use cases are listed in generative AI use cases in banking.
Front office
1- Conversational finance
Generative AI is a class of AI models that can generate new data by learning patterns from existing data, and generate human-like text based on the input provided. Conversational AI specifically focuses on simulating human-like conversations through AI-powered chatbots or virtual assistants, by using natural language processing (NLP), natural language understanding (NLU) and natural language generation (NLG). This capability is critical for finance professionals as it leverages the underlying training data to make a significant leap forward in areas like financial reporting and business unit leadership reports.
In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses, as they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource.
Conversational finance provides customers with:
- Improved customer support
- Personalized financial advice
- Payment notifications
For more on conversational finance, you can check our article on the use cases of conversational AI in the financial services industry.For the wide range of use cases of conversational AI for customer service operations, check our conversational AI for customer service article.
2- Generating applicant-friendly denial explanations
AI plays a significant role in the banking sector, particularly in loan decision-making processes. It helps banks and financial institutions assess customers’ creditworthiness, determine appropriate credit limits, and set loan pricing based on risk. However, both decision-makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application denials, to foster trust and improve customer awareness for future applications.
A conditional generative adversarial network (GAN), a generative AI variant, was used to generate user-friendly denial explanations. By organizing denial reasons hierarchically from simple to complex, two-level conditioning is employed to generate more understandable explanations for applicants (Figure 3).
Figure 3. AI generated loan denial explanations
Source: Generating User-Friendly Explanations for Loan Denials Using Generative Adversarial Networks1
Back office
3- Automation of accounting functions
Specialized transformer models help finance units in automating functions such as auditing, accounts payable including invoice capture and processing. With deep learning functions, GPT models specialized in accounting can achieve high rates of automation in most accounting tasks.
4- Legacy software maintenance
Banks still rely on software from the past (e.g. 70s or 80s) written in legacy programming languages like COBOL. It is hard to find developers for legacy languages but these software need to be maintained. Generative AI models can be fluent in all languages and can speed up development and reduce technology costs which make up ~10% of a typical banks’ costs.2
Explore more on how generative AI can contribute to software development and reduce technology costs, helping software maintenance.
5- Application modernization
Banks want to save themselves from relying on archaic software and have ongoing efforts to modernize their software. Enterprise GenAI models can convert code from old software languages to modern ones and developers can validate the new software saving significant time.
6- Document analysis
Generative AI can be used to process, summarize, and extract valuable information from large volumes of financial documents, such as annual reports, financial statements, and earnings calls, facilitating more efficient analysis and decision-making.
Explore more on generative AI in data and document processing.
7- Financial analysis and forecasting
By learning from historical financial data, generative AI models can capture complex patterns and relationships in the data, enabling them to make predictive analytics about future trends, asset prices, and economic indicators.
Generative AI models, when fine-tuned properly, can generate various scenarios by simulating market conditions, macroeconomic factors, and other variables, providing valuable insights into potential risks and opportunities.
Real-life example: An Asian financial institution is running a PoC to provide prompt-to-report functionality to 2,000 analysts and users.3
8- Financial report generation
Generative AI can automatically create well-structured, coherent, and informative financial reports based on available data. These reports may include:
- Balance sheets
- Income statements
- Cash flow statements
This automation not only streamlines the reporting process and reduces manual effort, but it also ensures consistency, accuracy, and timely delivery of reports.
Moreover, generative AI models can be used to generate customized financial reports or visualizations tailored to specific user needs, making them even more valuable for businesses and financial professionals.
Learn AI text generation use cases and real-life examples.
9- Fraud detection
Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities. These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data.
The enhanced understanding of fraud patterns allows these models to identify suspicious activities more accurately and effectively, leading to faster detection and prevention of fraud. By incorporating generative AI in fraud detection systems, financial institutions can:
- Improve the overall security and integrity of their operations
- Minimize losses due to fraud
- Maintain consumer trust
Explore how generative AI legal applications can help take actions against fraudulent activities.
10- Responding to regulator requests
As highly regulated industry players, banks get regular requests from regulators.
Real-life example: Banks are running PoCs to see if they can use LLMs to respond to simple and less critical queries from regulators. 4
11- Portfolio management and risk management
Another financial application of generative AI can be portfolio optimization. By analyzing historical financial data and generating various investment scenarios, generative AI models can help asset managers and investors identify optimal asset and wealth management, taking into account factors such as:
- Risk tolerance
- Expected returns
- Investment horizons
These models can simulate different market conditions, economic environments, and events to better understand the potential impacts on portfolio performance. This allows financial professionals to develop and fine-tune their investment strategies, optimize risk-adjusted returns, and make more informed decisions about managing their portfolios. This ultimately leads to improved financial outcomes for their clients or institutions.
12- Synthetic data generation
Since customer information is proprietary data for finance teams, it introduces some problems in terms of its use and regulation. Generative AI can be employed by financial institutions to produce synthetic data that adheres to privacy regulations such as GDPR and CCPA. By learning patterns and relationships from real financial data, generative AI models are able to create synthetic datasets that closely resemble the original data while preserving data privacy.
These synthetic datasets can be used for various purposes by financial institutions without exposing sensitive customer information, such as:
- Training machine learning models
- Conducting stress tests
- Validating models
For more on synthetic data, you can check our articles comparing synthetic data and real data, or comparing synthetic data and data masking methods for data privacy.
Common applications
13- Financial question answering
By leveraging its understanding of human language patterns and its ability to generate coherent, contextually relevant responses, generative AI can provide accurate and detailed answers to financial questions posed by users.
These models can be trained on large datasets of financial knowledge to respond to a wide range of financial queries with appropriate information, including topics like:
- Accounting principles
- Financial ratios
- Stock analysis
- Regulatory compliance
For example, BloombergGPT can accurately respond to some finance related questions compared to other generative models.
Figure 2. The ability of BloombergGPT, GPT-NeoX, and FLAN-T5-XXL to recall the names of CEOs of companies
Source: BloombergGPT: A Large Language Model for Finance5
Learn how to use chatGPT for your business.
14- Sentiment analysis
Sentiment analysis, an approach within NLP, categorizes texts, images, or videos according to their emotional tone as negative, positive, or neutral. By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings.
Financial institutions can benefit from sentiment analysis to measure their brand reputation and customer satisfaction through social media posts, news articles, contact centre interactions or other sources.
Check out our article on stock market sentiment analysis to learn more.
For example, BloombergGPT was also evaluated in the sentiment analysis task. As a fine-tuned generative model for finance, it outperformed other models by succeeding in sentiment analysis.
How to implement these generative AI finance use cases?
Business can either rely on off-the-shelf large language models or fine-tune LLMs for their use cases. For instance, internal audit functions can be greatly enhanced by generative AI through automated analysis and reporting.
For example, Bloomberg announced its finance fine-tuned generative model BloombergGPT, which is capable of making sentiment analysis, news classification and some other financial tasks, successfully passing the benchmarks.
Figure 1. How BloombergGPT performs across two broad categories of NLP tasks: finance-specific and general-purpose
Source: Bloomberg6
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External links
- 1. “Generating User-Friendly Explanations for Loan Denials Using Generative Adversarial Networks.” Fujitsu Global. Accessed 2 January 2024.
- 2. Michael Abbott (May 8, 2023).”What Generative AI Means For Banking“. Forbes. Retrieved June 18,2023
- 3. Nakai, Junta; Amend, Antoine; Knudsen, Eliot; Cuisia, Anna (June 7, 2023).”Generative AI is Everything Everywhere, All at Once“. Databricks. Retrieved June 18, 2023
- 4. Nakai, Junta; Amend, Antoine; Knudsen, Eliot; Cuisia, Anna (June 7, 2023).”Generative AI is Everything Everywhere, All at Once“. Databricks. Retrieved June 18, 2023
- 5. “BloombergGPT: A Large Language Model for Finance“, arXiv. Accessed 2 January 2024.
- 6. “Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance | Press.” Bloomberg.com. Accessed 2 January 2024.
