FinBench

Ten Kaggle-sourced tabular datasets, turned into natural-language customer profiles, that test whether a model flags credit default, fraud or customer-churn risk.

Also known as: FinBench (FinPT)

unassessed

What it measures

FinBench tests whether a model can predict financial risk — credit-card default, loan default, credit-card fraud, or customer churn — from a customer's record. The record starts as a row of tabular data (age, income, credit score, loan type, and so on); FinPT, the method the benchmark was built to evaluate, first has a language model turn that row into a natural-language "customer profile," then fine-tunes a foundation model on the profile text to predict a binary label (risky or not). It is a single-turn, text-only, English-language classification task over ten separate datasets, each drawn from a different Kaggle source and grouped into three risk categories.

Task format

Binary classification: given a natural-language customer profile (or the underlying tabular row), predict whether the customer is financially risky (1) or not (0), separately for each of ten datasets across default, fraud and churn.

Models reporting this benchmark

Data

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