10 Generative AI Finance Use Cases in 2023

Autonomous finance refers to the use of advanced technologies such as artificial intelligence (AI) and hyper-automation to automate financial processes, decision-making and services. Financial institutions are increasingly adopting autonomous finance due to its potential to increase efficiency, reduce operational costs and improve customer experience.

The financial services industry can use generative AI technologies to promote autonomous finance. In this article, we explain 10 generative AI finance use cases, providing some real-life examples.

Generative AI Finance Use Cases:

The financial services industry is already on its way to adopting generative AI models for certain financial problems.

For example, Morgan Stanley uses OpenAI-powered chatbots to support financial advisors, using the firm’s internal collection of research and data as a knowledge resource. More interestingly, Bloomberg announced its improved financial generative model, BloombergGPT, which is capable of performing sentiment analysis, news classification and some other financial tasks successfully passing benchmarks.

Figure 1: How BloombergGPT handles two broad categories of NLP tasks: financial and general purpose

Source: Bloomberg

However, generative AI has greater potential to facilitate many financial tasks if adequately adopted. Here are 10 possible use cases.

1- Conversational finance

Generative AI is a class of artificial intelligence models that can generate new data by learning patterns from existing data and generate human-like text based on input provided. Conversational AI specifically focuses on simulating human-like conversations through AI-powered chatbots or virtual assistants using natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG).

In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses as they learn to understand and generate human-like language patterns. As a result, generative AI can significantly improve the performance and user experience of financial conversational AI systems by providing more accurate, engaging and nuanced interactions with users.

Talking finance provides customers with:

• Improved customer support
• personal financial advice
• Payment notices

For more information on conversational finance, you can check out our article on conversational AI use cases in financial services.

Also, for a wide range of conversational AI uses for customer service operations, check out our conversational AI in customer service article.

2- 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 effective analysis and decision making.

3- Financial analysis and forecasting

By learning from historical financial data, generative AI models can capture complex patterns and relationships in data, enabling them to make predictive analyzes of future trends, asset prices and economic indicators.

Generative AI models, when properly configured, can generate various scenarios by simulating market conditions, macroeconomic factors and other variables, providing valuable insights into potential risks and opportunities.

4- Financial question answer

Using its understanding of human language patterns and its ability to generate coherent, contextually relevant answers, generative AI can provide accurate and detailed answers to financial questions posed by users.

These models can be trained on big financial knowledge data to answer a wide range of financial queries with relevant information, including topics such as:

• Principles of accounting
• financial ratios
• stock analysis
• Regulatory compliance

For example, BloombergGPT can answer some finance-related questions more accurately than other generative models.

Figure 2: Ability of BloombergGPT, GPT-NeoX, and FLAN-T5-XXL to recall company CEO names

Source: “BloombergGPT. A large model of languages ​​for finance”

5- Creation of financial reports

Generative AI can automatically generate well-structured, coherent and informative financial statements based on available data. These reports may include:

• balances
• Income Statements
• Cash flow statements

This automation not only simplifies the reporting process and reduces manual effort, but also ensures consistency, accuracy and timely delivery of reports.

Furthermore, generative AI models can be used to create personalized financial reports or visualizations that are tailored to specific user needs, making them more valuable to businesses and financial professionals.

6- Fraud detection

Generative AI can be used to detect fraud in finance by creating synthetic examples of fraudulent transactions or activities. These generated examples can help train and enhance machine learning algorithms to recognize and distinguish between legitimate and fraudulent patterns in financial data.

Enhanced understanding of fraud patterns allows these models to more accurately and efficiently identify suspicious activity, leading to faster fraud detection and prevention. By implementing generative intelligence in fraud detection systems, financial institutions can:

• Improve the overall security and integrity of their operations
• Minimize losses due to fraud
• maintain consumer confidence

7- Creating applicant-friendly rejection explanations

AI plays a significant role in the banking industry, particularly in credit decision-making processes. It helps banks and financial institutions assess the creditworthiness of customers, determine appropriate credit limits and price credit based on risk. However, both decision makers and loan applicants need clear explanations of AI-based decisions, such as reasons for application rejections, to promote trust and improve customer awareness for future applications.

A conditional generative adversarial network (GAN), a variant of generative AI, was used to generate user-friendly rejection explanations. By hierarchically organizing the reasons for rejection from simple to complex, two-level conditioning is applied to create more comprehensible explanations for applicants (Figure 3).

Figure 3: AI-generated loan denial explanations

Source: “Creating User-Friendly Explanations for Loan Denials Using Generative Adversarial Networks”

8- Portfolio management and risk management

Another financial application of generative AI could be portfolio optimization. By analyzing historical financial data and generating different 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 earnings
• investment horizons

These models can simulate various market conditions, economic environments and events to better understand potential impacts on portfolio performance. This allows financial professionals to develop and refine their investment strategies, optimize risk-adjusted returns and make more informed decisions about managing their portfolios. This ultimately leads to improved financial results for their clients or institutions.

9- Creation of synthetic data

Because customer information is proprietary to finance teams, it presents certain challenges in terms of its use and regulation. Generative AI can be used by financial institutions to produce synthetic data to comply with privacy regulations such as GDPR and CCPA. By learning patterns and relationships from real financial data, generative AI models can create synthetic data sets that closely resemble the original data while maintaining data privacy.

These synthetic databases 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

To learn more about synthetic data, you can check out our articles comparing synthetic data and real data or comparing synthetic data and data masking methods for data privacy.

10- Sentiment analysis

Sentiment analysis, an approach within NLP, classifies texts, images or videos according to their emotional tone as negative, positive or neutral. By gaining insights into customer emotions and opinions, companies can develop strategies to enhance their services or products based on these results.

Financial institutions also use sentiment analysis to measure their brand reputation and customer satisfaction through social media posts, news articles 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 well-tuned generative model for finance, it has outperformed other models by successfully achieving sentiment analysis.

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