6 papers
Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
Harry Proshian, Nikita Severin, Sergey Nikolenko +5
Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recomm…
LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients
Egor Fadeev, Dzhambulat Mollaev, Aleksei Shestov +6
Learning clients embeddings from sequences of their historic communications is central to financial applications. While large language models (LLMs) offer general world knowledge,…
LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models
Aleksei Shestov, Omar Zoloev, Maksim Makarenko +4
This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. Event sequences are transformed…
Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin +2
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their applica…
Multimodal Banking Dataset: Understanding Client Needs through Event Sequences
Dzhambulat Mollaev, Alexander Kostin, Maria Postnova +4
Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent pract…
Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov +7
In today's world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categor…