A Bank Trained Its Own Foundation Model to Catch Fraud — Freddy Lecue, Wells Fargo
Freddy Lecue explains how Wells Fargo pre-trained its own transformer from scratch on billions of customer transactions instead of words.
The model learns to predict the next transaction, and that one backbone can then be fine-tuned for fraud detection, credit line decisions, anti-money laundering and marketing. He covers the hardest part, turning tabular data like amounts, timing, merchants and zip codes into tokens, plus the model's size and training setup. On fraud, it caught more fraudulent transactions with fewer false alarms than the production XGBoost model, while staying fast enough for real-time card approvals. Chapters: - 00:00 Why a foundation model for transactions - 02:14 Why not just XGBoost? - 02:47 Billions of transactions - 03:22 Language models vs. transaction models - 04:45 Pre-training from scratch - 05:58 Who, what, how, when and where - 07:59 Turning tabular data into tokens - 09:42 Architecture and scaling - 12:10 Training setup - 13:44 Fraud detection results - 16:47 Recap Freddy Lecue, Wells Fargo: https://www.wellsfargo.com Join us in SF for the second annual AIE CODE summit: https://aie.codes our last flagship summit of 2026. Applications live now!
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