Financial Inclusion and Large Language Models

Abstract

Large language models have gained popularity, and it is important to understand their applications in the financial inclusion domain. This study identifies the benefits and risks of using large language models (LLMs) in the financial inclusion domain. We show that LLMs can be used to (i) summarize the key themes in financial inclusion communications, (ii) gain insights from the tone of financial inclusion communications, (iii) bring discipline to financial inclusion communications, (iv) improve financial inclusion decision making, and (v) enhance context-sensitive text analysis and evaluation. However, the use of large language models in the financial inclusion domain poses risks relating to biased interpretations of LLM-generated responses, data privacy risk, misinformation and falsehood risks. We emphasize that LLMs can be used safely in the financial inclusion domain to summarise financial inclusion speeches and communication, but they should not be used in situations where finding the truth is important to make decisions that promote financial inclusion.

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