Top 25 Generative AI Finance Use Cases in 2026
I spent a decade consulting for financial services firms. Every AI implementation I saw followed the same pattern: pilot projects that looked impressive in presentations but stalled in production.
That’s changing. Banks are now deploying generative AI at scale, and the results are measurable. Here’s what’s actually working, based on implementations you can verify.
- For financial services firms
- For finance units in non-financial firms
- For banking, check out generative AI use cases.
Finance functions in non-financial firms
1-Automation of accounting functions
Specialized transformer models help finance units automate functions such as auditing and 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.
Financial services firms
2-Conversational finance
Generative AI models can produce more natural, contextually relevant responses because 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 and chatbots by providing more accurate, engaging, and nuanced interactions with users.
Conversational finance provides customers with:
- Improved customer support
- Personalized financial advice
- Payment notifications
- Document generation, such as investment summaries or loan applications.
For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by leveraging the company’s internal research and data as a knowledge resource.
For more on conversational finance, you can check our article on the use cases of conversational AI in the financial services industry. To explore the many ways conversational AI can enhance customer service operations, look at our dedicated article on conversational AI for customer service.
3-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 type of generative AI, was utilized 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).
Real-life example of script generation
In a case study, the investor relations team anticipates a strong market reaction to the company’s quarterly financial results and needs to prepare a comprehensive script and potential investor questions for the earnings call.2
An analyst imports financial data from the current and previous quarters into a spreadsheet and uses a generative AI tool. The AI is given context from past earnings calls and specific insights to generate relevant commentary.
The AI tool generates a script for the earnings call, including likely investor questions and responses. The analyst formats this content into a Word document, highlights key investor questions, and prepares it for managerial review and the CFO’s preparation.
Back office
4-Code Modernization for Legacy Systems
Banks still run software written in COBOL from the 1970s and 80s. Finding developers who know COBOL is nearly impossible, but this software handles critical transactions and can’t just be turned off.
Generative AI models can:
- Read legacy code in COBOL, Fortran, or other old languages
- Convert it to modern languages like Python or Java
- Maintain the same business logic while improving performance
- Generate documentation explaining what the code actually does
Goldman Sachs confirmed that generative AI is now central to its application development and enhancement efforts. One bank’s developers validate AI-generated code, catching errors before deployment, but the AI does the heavy lifting.
Technology costs make up ~10% of a typical bank’s expenses. Speeding up development and reducing maintenance costs directly improves profitability.3
5-Application modernization
Banks aim to avoid relying on outdated software and are continually investing in modernization efforts. Enterprise GenAI models can convert code from old software languages to modern ones, and developers can validate the new software, saving significant time.
Employees at Goldman Sachs confirm that generative AI is a strong aspect of application development and enhancement.4
6-Automated Document Generation
Banks produce thousands of documents daily: investment summaries, loan applications, client reports, and regulatory submissions. These documents pull from templates, but customizing them takes time.
Generative AI now handles this:
- Generate professional documents from simple prompts
- Pull relevant data from multiple systems
- Apply appropriate formatting based on document type and recipient
- Ensure consistency with regulatory requirements
7-Financial Forecasting and Analysis
Generative AI improves forecasting by learning from historical financial data to capture complex patterns and relationships. When properly fine-tuned for specific banks and economic contexts, these models make predictions about:
- Asset price movements
- Interest rate trajectories
- Credit default probabilities
- Market volatility
- Economic indicator trends
The key phrase: “properly fine-tuned.” Off-the-shelf models hallucinate and make confident predictions based on patterns that don’t exist. Banks that succeed with AI forecasting invest heavily in training models on their specific data and validating outputs against expert judgment.
8- Market predictions
By analyzing large volumes of data, generative AI can improve the accuracy of financial forecasts, including stock prices, interest rates, and economic indicators.
Real-life example
An Asian financial institution is running a PoC to provide prompt-to-report functionality to 2,000 analysts and users.5
9-Financial report generation
Automated reporting
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 streamlines the reporting process, reducing manual effort and ensuring consistency, accuracy, and timely report delivery.
10- Scenario-based reporting
AI can simulate different regulatory scenarios and generate reports to help financial institutions ensure compliance with all necessary requirements under various conditions.
Learn AI text generation use cases and real-life examples.
11-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 enables these models to identify suspicious activity more accurately and effectively, leading to faster fraud detection and prevention. 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.
Real-life example
Mastercard needed a faster, more accurate way to detect fraudulent transactions as fraudsters exploited stolen payment card data. Using generative AI, Mastercard scanned transaction data across millions of merchants, predicting and detecting compromised cards, helping banks block them faster and prevent fraud.
Results:
- Doubled detection rate of compromised cards.
- Reduced false positives in fraud detection by up to 200%.
- Increased merchant fraud detection speed by 300%.
12-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. 6
13-Portfolio management
Dynamic portfolio 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.
14-Customized indices
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.
15-Risk management
Stress testing
Generative AI can simulate extreme market conditions that have not occurred in the historical data, allowing financial institutions to better prepare for rare but high-impact events.
16-Credit risk modeling
AI models can generate synthetic borrower profiles to test the robustness of credit risk models, improving the accuracy of credit scoring and default predictions.
17-Anomaly detection
Generative AI models can be trained to understand the normal patterns of transactions and generate data points that represent outliers or anomalies. This helps in identifying potentially fraudulent activities or unusual transaction patterns that might indicate money laundering.
18-Synthetic data for training
Since real fraudulent transactions are rare, generative AI can create synthetic examples of fraudulent activity, helping to train better detection algorithms.
19-Synthetic data generation
Customer financial data is proprietary and regulated under GDPR, CCPA, and other privacy laws. This creates problems:
- Can’t share data with third-party vendors for model training
- Can’t use production data in development/testing environments
- Can’t conduct research without risking privacy breaches
Synthetic data enables:
- Training machine learning models without exposing customer information
- Stress testing systems with realistic data volumes
- Validating models across diverse customer segments
- Sharing data with partners for integration testing
The synthetic customers have realistic credit scores, transaction patterns, income levels, and financial behaviors but they’re not real people, so no privacy violations occur.
Since customer information is proprietary data for finance teams, it poses challenges for its use and regulation. Generative AI can be used by financial institutions to generate synthetic data that complies with privacy regulations such as GDPR and CCPA.
Real-life example on synthetic data generation
Morgan Stanley faced the challenge of optimizing wealth management operations and enhancing advisor-client interactions through advanced AI tools while maintaining data security and minimizing errors.
They partnered with OpenAI to implement a generative AI platform for synthesizing research data. They piloted the tool with 900 advisors and planned a broader rollout.
The AI tool enhanced advisors’ ability to efficiently process large volumes of data. Morgan Stanley is scaling the platform while addressing risks such as AI errors and data security issues.7
20-Algorithmic trading & investment strategies
21-Scenario analysis
These models can simulate various market scenarios, helping traders and portfolio managers understand potential risks and returns under different conditions.
According to Dimension Market Research, the size of the global market for generative AI in trading is projected to be USD 208.3 million by 2024 and USD 1,705.1 million by 2033. In 2024, the market is expected to grow at a compound annual growth rate (CAGR) of 26.3%.8
22-Product development
Customized investment portfolios
Generative AI can analyze individual investor profiles, preferences, and financial goals to generate personalized investment portfolios. This is particularly useful for robo-advisors and wealth management platforms.
Tailored insurance products
AI can create personalized insurance products based on individual risk profiles, generating unique terms and pricing structures for different customers.
23-Underwriting & pricing
Dynamic pricing models
Generative AI can help insurers and lenders develop dynamic pricing models that adjust in real-time based on new data, market conditions, and individual customer behavior.
Risk assessment
AI can generate different risk scenarios, helping underwriters assess potential outcomes and set appropriate premiums or interest rates.
Common applications
24-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.
Learn how to use chatGPT for your business.
25-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.
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.
Check out our article on stock market sentiment analysis to learn more.
Best Practices From Banks That Got It Right
1. Start with clear business objectives
Don’t implement AI because “everyone’s doing it.” Morgan Stanley wanted to make their research accessible to advisors. That specific goal drove their implementation decisions.
2. Ensure data governance first
AI quality depends on data quality. Banks that succeed invest in data platforms, lineage tracking, and quality controls before deploying AI.
3. Build AI literacy across the organization
Citigroup trained 175,000 employees on AI prompting and capabilities. When employees understand what AI can do, they find use cases the C-suite never imagined.
4. Implement robust evaluation frameworks
Morgan Stanley’s eval framework tests every AI use case before deployment. They measure accuracy, check for biases, validate outputs against expert judgment, and adjust prompts based on results.
5. Maintain human oversight
Even with 98% adoption at Morgan Stanley, humans validate AI outputs before they reach clients. The AI assists; humans decide.
6. Plan for organizational change
AI changes workflows, roles, and skills requirements. Banks that succeed prepare teams for this transition through training, communication, and change management.
7. Focus on high-impact areas first
Bank of America’s chief technology officer explicitly avoids AI tools that save “a couple of minutes on simplistic tasks.” They target processes involving 40+ steps and thousands of employees where AI delivers measurable ROI.
Generative AI challenges in finance industry & tips to overcome them
Here are some reasons why some financial professionals hesitate adopting generative AI tools in finance:
- Data accuracy : “Although AI greatly enhances the processing and generation of data, it may be prone to significant data quality issues.” as the European Central Bank states. There is a chance that the biased and inaccurate data used to train foundation models will produce output with more errors. When feeding foundation models, data quality and accuracy are crucial factors.11
- Bias in Models: AI models can inherit biases from the data they are trained on, leading to unfair or skewed decisions, particularly in areas like credit scoring or investment recommendations.
- To detect such AI bias, businesses can adopt a responsible AI platform.
- Limited generalization: Businesses can either rely on off-the-shelf large language models or fine-tune LLMs for their use cases. Off-the-shelf models may not perform well in specific, highly specialized financial contexts without proper fine-tuning, which could result in inaccurate or irrelevant outputs.
- Adopt LLMOps tools to build, test, monitor and fine-tune your LLMs better.
- Hallucinations: Generative AI can produce inaccurate or fabricated information, which is risky in finance where decisions rely on precise data, leading to poor investment advice or regulatory breaches.
- Apply LLM security tools and extractive AI to overcome this issue and ensure model accuracy.
- Regulations: The financial sector is highly regulated, and AI must comply with strict standards on transparency, accountability, and data use, posing challenges for ensuring compliance with evolving legal frameworks.
- Deploy AI governance tools and build an AI inventory to ensure AI compliance.
- Data Security: Financial data is sensitive, and ensuring that AI systems handle it securely, preventing breaches or misuse, is crucial to maintain client trust and avoid regulatory penalties.12
Explore 10 major LLM risks and their impact.
Spending on generative AI & market expectations
Financial simulations and forecasts produced with the aid of enterprise generative AI are beneficial for trading, portfolio management, and financial markets. Despite its many advantages, including time savings, large data sets, and computational power, it can malfunction and expose sensitive data, posing security risks. These challenges can specifically affect finance processes and the overall finance function.
- By 2030, the banking industry is expected to spend 84.99 billion US dollars on generative artificial intelligence (AI), growing at a remarkable compound annual growth rate of 55.55 percent.13
- It is anticipated that J.P. Morgan will invest $17 billion in generative AI this year, up 10% from $15.5 billion in 2023. Professionals with experience in AI and machine learning are working on a taskforce to find applications in various business verticals.15
- According to the McKinsey Global Institute (MGI), the use of Gen AI in the banking industry could result in an annual value addition of $200 billion to $340 billion, or 2.8 to 4.7 percent of the total industry revenues. This value addition would primarily come from increased productivity.16
For additional insights into automation in the financial sector, explore our article on Intelligent Automation in Banking & Financial Services.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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