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#financialrisk #prediction is vital but hindered by outdated algorithms and the absence of comprehensive benchmarks. Addressing this, FinPT uses large pretrained models and Profile Tuning for #risk prediction, while FinBench provides datasets on #default, #fraud, and #churn. FinPT inserts tabular data into templates, generates customer profiles using #languagemodels, and fine-tunes models for predictions, demonstrated effectively through experiments on FinBench, enhancing understanding of language models in financial risk.

"We assess the ability of #GPT … to serve as a financial robo-advisor for the masses, by combining a financial literacy test and an advice-utilization task (the Judge-Advisor System). #davinci and #chatgpt (variants of GPT) score 58% and 67% on the #financialliteracy literacy test, respectively, compared to a baseline of 31%. However, people overestimated GPT's performance (79.3%), and in a savings dilemma, they relied heavily on advice from GPT (WOA = 0.65). Lower subjective financial knowledge increased advice-taking. We discuss the risk of overreliance on current large #languagemodels models and how their utility to laypeople may change."