Summary of Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences, by Piotr Skalski et al.
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
by Piotr Skalski, David Sutton, Stuart Burrell, Iker Perez, Jason Wong
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel generative pretraining method for multivariate time series of financial transactions, leveraging large self-supervised generative models from natural language processing and computer vision. This approach contextualizes embeddings of financial transactions, surpassing state-of-the-art self-supervised methods on downstream tasks in benchmark datasets. The authors apply the method to a corpus of 5.1 billion transactions from 180 issuing banks, demonstrating significant improvements in card fraud detection rate at high precision thresholds and transferability to out-of-domain distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to analyze financial data using machine learning. They use big models that don’t need labeled data to learn patterns in large amounts of transaction information. The method is tested on public datasets and performs better than other methods. It’s also used for card fraud detection and shows promise. |
Keywords
* Artificial intelligence * Machine learning * Natural language processing * Precision * Pretraining * Self supervised * Time series * Transferability
