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This paper proposes a method for pretraining encoders on financial transaction event sequences. It chooses prediction targets by testing how useful the learned representations are downstream, then pretrains on the selected targets with a joint likelihood over a shared discrete latent variable. The evidence is reported on a real-world transaction-risk dataset and several public financial benchmarks, with ablations for the target-selection and joint-modeling components. Readers should weigh the gains against the baselines chosen and the cost of the selection trials, which the abstract does not quantify.
TAPES: Task-Aligned Joint Predictive Pretraining for Transaction Event Sequences
arXiv:2611.04747v1 Announce Type: new Abstract: Transaction event sequences support diverse prediction tasks in online commerce and digital financial services. The same transaction records can yield prediction targets with different semantics, including values, changes, and relations to past events. These targets impose different constraints on history representations, but their predictability alone does not establish the downstream utility of a target set. Moreover, target-wise prediction losses do not explicitly model conditional co-occurrence among targets. To address these challenges, we propose TAPES, a task-aligned joint predictive pretraining framework for transaction sequences. TAPES constructs a finite collection of candidate target sets from admissible field-operator combinations. It selects a target set according to the downstream utility of representations learned through short joint pretraining trials under matched budgets. It then reinitializes the encoder and pretrains it on the selected targets through a joint likelihood parameterized by a shared discrete latent variable, before downstream supervised adaptation. Experiments in a real-world transaction-risk setting and on multiple public financial benchmarks spanning diverse downstream tasks demonstrate that TAPES outperforms representative baselines. Ablation studies further validate the contributions of predictive target selection and joint modeling.
Abstract:Transaction event sequences support diverse prediction tasks in online commerce and digital financial services. The same transaction records can yield prediction targets with different semantics, including values, changes, and relations to past events. These targets impose different constraints on history representations, but their predictability alone does not establish the downstream utility of a target set. Moreover, target-wise prediction losses do not explicitly model conditional co-occurrence among targets. To address these challenges, we propose TAPES, a task-aligned joint predictive pretraining framework for transaction sequences. TAPES constructs a finite collection of candidate target sets from admissible field-operator combinations. It selects a target set according to the downstream utility of representations learned through short joint pretraining trials under matched budgets. It then reinitializes the encoder and pretrains it on the selected targets through a joint likelihood parameterized by a shared discrete latent variable, before downstream supervised adaptation. Experiments in a real-world transaction-risk setting and on multiple public financial benchmarks spanning diverse downstream tasks demonstrate that TAPES outperforms representative baselines. Ablation studies further validate the contributions of predictive target selection and joint modeling.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2611.04747 [cs.LG]
(or arXiv:2611.04747v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.04747 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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