Industries

Transform financial decisions with TabPFN

From treasury forecasting to credit modelling, 
TabPFN handles any new prediction task in seconds.

Finance can't wait — markets move faster than ML pipelines

TabPFN delivers state-of-the-art predictions instantly to support decisions fit for financial markets. Get insitutional grade results that boost margins and comply with regulatory requirements.

SOTA for time-series forecasting

Combine several data sources with your time-series and get SOTA market & product forecasting results with TabPFN

Regulatory-compliant and explainable

Meet compliance requirements with transparent, auditable predictions that regulators trust

One model for all use-cases

Replace dozens of specialized models with one foundation model that handles any financial prediction task

In-Context Learning

Get research-grade results immediately — no data prep, no model selection, no long training runs & no retraining needed

Open source models used at

High impact use cases

Credit Risk: Consumer Lending

This dataset contains 9,578 loan records with borrower credit profiles, FICO scores, debt-to-income ratios, loan purpose, and payment history. The goal: predict whether borrowers will repay their loans in full or default.

TabPFN achieves 99% ROC-AUC without any tuning, significantly outperforming CatBoost (93%), Random Forest (88%), LightGBM (84%), and XGBoost (71%).

Underwriting teams approve loans with higher confidence and increase approval rates within the same risk thresholds. Data scientists deploy models without retraining for several loan products and market segments.

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Cross-Sell Optimization: Banking

This dataset contains 5,000 bank customers with demographics, account relationships, and loan product holdings. The goal: predict which customers will accept personal loan offers in a cross-sell campaign.

TabPFN achieves 97% ROC-AUC without tuning, significantly outperforming CatBoost (91%), LightGBM (90%), XGBoost (88%), and Random Forest (86%).

Marketing teams increase conversion rates by focusing on high-propensity customers and reduce wasted outreach on unlikely prospects. Models adapt instantly to new customer cohorts or product variations without rebuilding pipelines.

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