Comparing Credit Risk Estimates in the Gen-AI Era

Generative AI technologies have demonstrated significant potential across diverse applications. This study provides a comparative analysis of credit score modeling techniques, contrasting traditional approaches with those leveraging generative AI. Our findings reveal that current generative AI models fall short of matching the performance of traditional methods, regardless of the integration strategy employed. These results highlight the limitations in the current capabilities of generative AI for credit risk scoring, emphasizing the need for further research and development before the possibility of applying generative AI for this specific task, or equivalent ones.

LLMs' Leaning in European Elections

Many studies suggest that LLMs have left wing leans. The article extends the US presidential election analysis made in previous works, where multiple LLMs were asked to vote between Joe Biden and Donald Trump in a virtual election, and the results showed a clear lean of LLMs toward Joe Biden. This article considers natural follow-up questions that could arise from that experiment, such as: what is the extent of this phenomenon? Is it generalizable to multiple virtual elections in other countries? The article considers virtual elections in ten european countries: Germany, France, Italy, Spain, Poland, Romania, Netherlands, Belgium, Czech Republic, and Sweden, and with four different LLMs: gpt4o, claude 3.5 sonnet, mistral-large, and gemini-2.0-flash.