# Multimodal Banking Dataset: Understanding Client Needs through Event Sequences

arXiv: https://arxiv.org/abs/2409.17587 · HTML: https://mlsanity.com/paper/2409.17587/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 61 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Tabular Transformers for Modeling Multivariate Time Series](https://mlsanity.com/paper/2011.01843/) (2020)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events](https://mlsanity.com/paper/2306.11547/) (2023)
- [CoLES: Contrastive Learning for Event Sequences with Self-Supervision](https://mlsanity.com/paper/2002.08232/) (2020)
- [LightAutoML: AutoML Solution for a Large Financial Services Ecosystem](https://mlsanity.com/paper/2109.01528/) (2021)
- [E.T.-RNN: Applying Deep Learning to Credit Loan Applications](https://mlsanity.com/paper/1911.02496/) (2019)
- [Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution](https://mlsanity.com/paper/2408.10548/) (2024)
- [A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering](https://mlsanity.com/paper/1701.09177/) (2017)
- [FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data](https://mlsanity.com/paper/2310.13818/) (2023)
- [Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences](https://mlsanity.com/paper/2401.01641/) (2024)
- [Survey on Visual Analysis of Event Sequence Data](https://mlsanity.com/paper/2006.14291/) (2020)
- [SeqNAS: Neural Architecture Search for Event Sequence Classification](https://mlsanity.com/paper/2401.03246/) (2024)
- [Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers](https://mlsanity.com/paper/2012.08777/) (2020)
- [Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge](https://mlsanity.com/paper/2404.02047/) (2024)
- [Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world](https://mlsanity.com/paper/2003.04173/) (2020)
- [Learning Latent Representations of Bank Customers With The Variational Autoencoder](https://mlsanity.com/paper/1903.06580/) (2019)
- [A Survey of Approaches for Event Sequence Analysis and Visualization using the ESeVis Framework](https://mlsanity.com/paper/2202.07941/) (2022)
- [The Long Tail of Context: Does it Exist and Matter?](https://mlsanity.com/paper/2210.01023/) (2022)

## Built on by (most cited first, 9 of 9)

- [EBES: Easy Benchmarking for Event Sequences](https://mlsanity.com/paper/2410.03399/) (2024)
- [LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients](https://mlsanity.com/paper/2508.10021/) (2025)
- [Open Banking Foundational Model: Learning Language Representations from Few Financial Transactions](https://mlsanity.com/paper/2511.12154/) (2025)
- [Enhancing Temporal Awareness in LLMs for Temporal Point Processes](https://mlsanity.com/paper/2601.00845/) (2025)
- [Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences](https://mlsanity.com/paper/2603.15713/) (2026)
- [How to model Human Actions distribution with Event Sequence Data](https://mlsanity.com/paper/2510.05856/) (2025)
- [MortarBench: Evaluating Mortgage Loan Origination Agents](https://mlsanity.com/paper/2606.19416/) (2026)
- [A Foundation Model for Multimodal Event Sequences in Financial Applications](https://mlsanity.com/paper/2607.09955/) (2026)
- [FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences](https://mlsanity.com/paper/2609.11993/) (2026)

## Abstract (arXiv)

Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent practical need, developing deep learning techniques suitable to handle such data is limited by the absence of large open-source multi-source real-world datasets of event sequences. To fill this gap, which is mainly caused by security reasons, we present the first industrial-scale publicly available multimodal banking dataset, MBD, that contains information on more than 2M corporate clients of a large bank. Clients are represented by several data sources: 950M bank transactions, 1B geo position events, 5M embeddings of dialogues with technical support, and monthly aggregated purchases of four bank products. All entries are properly anonymized from real proprietary bank data, and the experiments confirm that our anonymization still saves all significant information for introduced downstream tasks. Moreover, we introduce a novel multimodal benchmark suggesting several important practical tasks, such as future purchase prediction and modality matching. The benchmark incorporates our MBD and two public financial datasets. We provide numerical results for the state-of-the-art event sequence modeling techniques including large language models and demonstrate the superiority of fusion baselines over single-modal techniques for each task. Thus, MBD provides a valuable resource for future research in financial applications of multimodal event sequence analysis. HuggingFace Link: https://huggingface.co/datasets/ai-lab/MBD Github Link: https://github.com/Dzhambo/MBD