Artificial intelligence, particularly large language models is increasingly being used to assist with financial planning and decision making. One of the key areas where these models can be useful is in budgeting where they can help individuals and organizations create and manage their budgets more effectively.
Another area where large language models can be useful is in research summaries where they can help analyze and summarize large amounts of financial data, providing insights and trends that may not be immediately apparent. This can be particularly useful for financial analysts and investors who need to make informed decisions based on accurate and up-to-date information.
Scenario planning
Large language models can also be used for scenario planning where they can help individuals and organizations anticipate and prepare for different financial scenarios. This can include stress testing and sensitivity analysis which can help identify potential risks and opportunities.
However, it’s also important to be aware of the limitations of large language models in finance. For example, they may not always be able to understand the nuances of human decision making, and may not be able to account for all the variables that can affect financial outcomes.
Eliciting assumptions and sources
To get the most out of large language models in finance, it’s essential to be able to elicit assumptions and sources from the models. This can involve using specific prompts and queries to get the models to provide more detailed and accurate information.
For example, users can ask the models to provide references and citations for the information they provide, which can help to verify the accuracy of the information and identify any potential biases or limitations.
Bias checklist and human-in-the-loop safeguards
It’s also important to have a bias checklist in place to ensure that the models are not perpetuating any biases or stereotypes. This can involve regularly reviewing the models’ outputs and checking for any inconsistencies or anomalies.
In addition, having human-in-the-loop safeguards in place can help to ensure that the models are being used in a responsible and transparent way. This can involve having human reviewers and editors who can check the models’ outputs and provide feedback and corrections as needed.
