Multimodal Banking Dataset: Understanding Client Needs through Event Sequences

## Abstract

Abstract Financial organizations collect a huge amount of data about clients that typically has a temporal (sequential) structure and is collected from various sources (modalities). Due to privacy issues, there are no large-scale open-source multimodal datasets of event sequences, which significantly limits the research in this area.
In this paper, we present the industrial-scale publicly available multimodal banking dataset, MBD, that contains more than 1.5M corporate clients with several modalities: 950M bank transactions, 1B geo position events, 5M embeddings of dialogues with technical support and monthly aggregated purchases of four bank’s products. All entries are properly anonymized from real proprietary bank data. Using this dataset, we introduce a novel benchmark with two business tasks: campaigning (purchase prediction in the next month) and matching of clients. We provide numerical results that demonstrate the superiority of our multi-modal baselines over single-modal techniques for each task. As a result, the proposed dataset can open new perspectives and facilitate the future development of practically important large-scale multimodal algorithms for event sequences. HuggingFace Link: https://huggingface.co/datasets/ai-lab/MBD Github Link: https://github.com/Dzhambo/MBD

## 1 Introduction

The ever-growing digital footprint of consumers has led to an explosion of data collected by businesses across all industries. Financial institutions, in particular, gather a vast amount of information about their clients over time, encompassing various aspects of their financial activities. This data, spanning extended periods, is typically annotated with temporal information, forming what is known as event sequences [, , , ]. Description of an event usually contains several heterogeneous fields, numerical and categorical, and processing of event sequence is a separate problem in machine learning different from techniques applied to time series or texts.

An important property of event sequences is that these data are often gathered from multiple sources or channels, rendering multimodal. The comprehensive analysis of such data in a joint way is critical for informed decision-making processes within enterprises. The scarcity of publicly available datasets in this domain hampers research and development efforts. Indeed, the significant progress in multimodal data for natural language processing, computer vision, audio, etc., is due to the appearance of large-scale datasets, such as LAION, DataComp, etc. []. Unfortunately, though several datasets of event sequences are used in research, e.g., credit card transactions [] or MIMIC [], they are either small or contain only one modality. Thus, a gap exists in methodologies designed to tackle the complexity of multimodal event sequence data.

To bridge this gap, this paper introduces the Multimodal Banking Dataset (MBD), an unprecedented open-source resource encompassing extensive multichannel event sequence data of banking corporate clients. This dataset is the largest of its kind, featuring detailed records of approximately 1.5 million clients across four distinct modalities: money transfers (about 950 million events), geo position data (around 1 billion events), technical support dialog embeddings (approximately 5 million entries), and monthly aggregated bank product purchases categorized into four types. Each modality encompasses roughly one or two year of historical, time-annotated data, making it a rich resource for analyzing the dynamics of client behavior over time.

The MBD dataset enables the exploration of several critical business problems in a multimodal context. Specifically, it facilitates research in such tasks as purchase prediction for the following month (campaigning), and matching of different modalities for the same clients. These problems can all be addressed by leveraging the temporal and spatial attributes inherent in the dataset. Moreover, we provide benchmarks for these tasks using the MBD. Importantly, all the data are anonymized to protect client privacy, and our research confirms that the anonymization process preserves the consistency of model performance between original and anonymized data. This paper underscores the potential of MBD as a foundational tool for advancing research in multimodal sequential data analysis, fostering innovation, and enhancing decision-making processes in the financial sector.

## 2 Dataset

Modern innovative banking institutions actively develop AI technologies for customizing their human-oriented technologies and making everyday decisions. A superior level of technologies will lead to new cases of customers’ experience, which should form a competitive advantage of services provided, including the speed, accuracy, and price of customer services, including personal credit conditions, individual finance strategy, etc.

One of the main benefits of using AI is the ability to analyze large amounts of customer data. This helps banks better understand their customers’ needs and offer them the most suitable products and services. Additionally, AI can protect customers from fraud and prevent financial losses. More accurate forecasting of financial risks associated with lending or investing allows us to provide more favorable conditions to more reliable clients.

This strategy is more effective if there is a lot of data, so banks strive to accumulate as much information as possible.

Being a team of a bank that stores petabytes of data about bank processes and clients, we understand the urgent needs of fintech data scientists in large sets of publicly available temporal data from various sources (bank transactions, client locations, purchased products, etc.) to drive innovation and scientific discovery. Unfortunately, the number of appropriate datasets is limited because providing such data comes with certain risks. Typically, banks are wary of sharing their data due to potential leaks of confidential information or violations of data protection regulations. In addition, the data is considered to have commercial value, and companies do not want to disclose it. To mitigate these risks, it is required to take all necessary steps to ensure data security and remove any identifying information. This allows information to be shared without violating client confidentiality, but this procedure requires significant effort from engineers, managers, and lawyers. Though there exist a small number of properly anonymized banking datasets, such as credit card transactions [] or AlphaBattle [], to the best of our knowledge, there are no publicly available large multimodal temporal datasets for banks.

Thus, in this paper, we introduced the first large-scale multimodal banking dataset to support future research on multimodal techniques for event sequences. In particular, we selected several practically important tasks, such as campaigning, i.e., prediction if a client would purchase some of four rather popular products in the next month. Each client is described by sequences of typical bank data: transactions, geo positions where the customer used the bank application, and dialogues with technical support. These data sources ideally highlight the main difficulties in developing multimodal models, namely, asynchronous events in different modalities, various intensities of events, rare/irregular events, and even the absence of some modalities for many clients. Based on our dataset, the researchers will be able to fully take into account cross-modal connections of sequences from multiple sources at the level of individual events.

The dataset was collected using the following procedure. At first, we selected a complete sample of clients for two year (2021, 2022) to be able to cover all seasons. Among all customers who had the opportunity to purchase at least one of four products during 2022, we randomly selected 2,186,230 clients, among which 1M customers are labeled by monthly aggregated purchases of each of four products in each month. For these clients, we collected 947,899,612 bank transactions, 1,117,213,760 geo position events, and 5,080,781 dialogues with technical support. Our data raise typical practical challenges for training multimodal models. For example, many clients do not have all three modalities simultaneously because they can never make a transaction, call tech support, or leave their geo trace while running the bank application.
Next, all the data were properly anonymized to guarantee the confidentiality and privacy of customer information. As a result, it becomes impossible to recover back to real clients from our anonymous data. We will show in the experimental study that such anonymization still let us extract valuable information about clients.

To demonstrate the complete transformation of data from various sources, Fig. 1 briefly shows example data for each modality, and the temporal structure of the data for a campaigning model is presented. Let us introduce further details for each modality in the following subsections.

Figure: Figure 1: Pipeline for processing original data sources for solving campaigning task. Examples of raw data for each modality are presented on the left. Only anonymized data is published in the dataset. The center shows the temporal anonymized structure of the data. The multimodal multi-label classification model for predicting purchases is shown on the right.
Refer to caption: x1.png

### 2.1 Modalities

1. Bank transactional data are financial operations (events) carried out between different clients. The sequence of transactions can uniquely characterize the client [], so this modality plays one of the most important roles in financial planning and recommendations.
Thus, the main component of our MBD dataset is each client’s transactional history, represented by an event with a timestamp and various attributes of the anonymized counterparty. Clients have 638 transactions on average. Fig. 2 in Appendix A contains the detailed distribution of clients for different lengths of the transaction history. The list of attributes is shown in Table 4.

2. Dialogues.
The dialogue data are transcriptions of customer calls to technical support and negotiations between the client and its manager. It is an extremely important source of information about client needs and problems []. The audio utterance is fed into a commercial Speech-to-Text algorithm. Personal information, e.g., the client’s name, is detected in the text and masked. To further anonymize the dialogue, we feed its text into a pre-trained NLP model(^1^11 https://huggingface.co/ai-forever/ruBert-base) and save the resulting embeddings of size 768 in dialogue modality. An example of dialogue data is presented in Fig. 3 in Appendix A. Only 46% of customers contact support and have records of conversations, 98% of them have no more than 10 dialogues per year. Fig. 4 contains the detailed distribution of clients for various numbers of dialogues.

3. Geostream data contains a sequence of geo-coordinates of a client. To anonymize this modality, the coordinates are encoded using geohashes(^2^22https://pypi.org/project/pygeohash/), a geocoding system that converts a geographic location into a short string. Each unique geohash corresponds to a region on the Earth’s surface. It is possible to adjust the accuracy and size by removing characters from the end of the code. In our dataset, the coordinates were encoded with a precision of 4, 5, and 6 characters, representing cells of different sizes on the map. In our dataset, there are 43,999 distinct values of geohash_4, 347,698 numbers of geohash_5, and 2,264,404 most precise locations (geohash_6).

An example of geo data is presented in Fig. 5 in Appendix A. The clients have an average of 910 geo-points in the time period considered. Detailed information about the distribution of their length over the clients is available in Fig. 6.

4. Products purchases. Our dataset is a valuable resource for analyzing the bank customers’ needs. In particular, it contains information about purchases of four different banking products for each month of 2022. To predict a purchase in a certain month, it is necessary to take events (transactions, geo, dialogues) strictly before the beginning of this month. Therefore, the date range for the purchases dataset is shifted by 1 month, i.e., information is available from February 1, 2022, to January 31, 2023. The campaigning task is a multi-label classification problem, i.e., we store a binary label for each product that indicates whether it was purchased by a customer in a certain month. Example of data about purchases is shown in Fig. 8 (Appendix A).
The peculiarity of this dataset is its imbalance, which is specific to this type of business task: 81% of clients have no purchases, 15% have one, and the remaining 4% have two or more purchases. A historical overview over 12 months allows us to model the customer behavior dynamic and predict the date of purchase more accurately.

### 2.2 Data anonymization

Our dataset contains no personal or confidential information whatsoever. Nevertheless, the event sequences are detailed enough that it could be possible to compare individuals from the publicly accessible portion of the dataset with the original proprietary data. To mitigate this risk, noise has been introduced to the data, ensuring that such comparisons and identification are impossible. The noise patterns were selected by our bank’s internal security department. These patterns are applied locally, preserving the overall structure of the data. The specific parameters of the noise are not disclosed to prevent potential attacks on the dataset.

All ID fields were hashed with a random salt. All categorical field values was mapped to enumerated indexes. Random noise was added to numerical fields. The noise was added to date fields with preserving hour of original date, which may be the cause the shuffle of local sequence. The dialogue embedding space was divided into regions, which were then shuffled.

## 3 Benchmark

In this paper, we formulate two practically important tasks using our new dataset: campaigning and client matching. This section contains details about baseline methods and testing protocols.

### 3.1 Methods

To establish performance baselines, we implement several widely-adopted architectures. Our approach prioritizes unsupervised and semi-supervised methods [], enabling the training of a general-purpose encoder on unlabeled sequential data. Additionally, we incorporate supervised methods that allow for the immediate training of the encoder in a fully supervised manner.

The following techniques are implemented in our benchmark to extract features for Transactions and Geostream modalities:

- 1.
Aggregation Baseline that contains hand-crafted aggregation statistics []: 3,000 and 4 features in total for sequences of transactions and geodata, respectively.
- 2.
CoLES (Contrastive Learning for Event Sequences), a self-supervised contrastive model [] specially developed to obtain representations of such event sequences as bank transactions. The sequence encoder is a GRU (Gated Recurrent Unit) with a hidden size 256.
- 3.
Two Tabular Transformers from IBM []. The first model, TabBERT, adapts BERT to event sequences such as bank transactions. The second model, TabGPT, was initially proposed to generate synthetic tabular sequences. Both models extract 256-dimensional embeddings of an input event sequence. After, we pool output embeddings of the client in result embedding of size 1024, calculating min, max, mean, and std.

To obtain representation of a sequence of dialogues, we implemented several straightforward techniques to aggregate the sequence of embeddings of each dialog of a client: 1) mean pooling of all embeddings; and 2) use only the most recent embedding for the date of interest.

To demonstrate the potential of multimodal processing of event sequences, we implemented traditional fusion techniques, namely, 1) blending, i.e., a weighted sum of scores (estimates of class posterior probabilities) from single-modal classifier, and 2) late fusion, i.e., learning a classifier for concatenated embeddings from all modalities []

For supervised methods, we use the following architectures: for unimodal data, we utilize an GRU encoder, while for multimodal data, we employ different GRU encoders for each modality and embeddings from all encoders are concatenated. The resulting neural network is trained using binary cross-entropy (BCE) loss in multilabel setting. For the GRU encoders we used hidden size 32.

### 3.2 Tasks

#### 3.2.1 Campaigning task

In this task, it is required to predict the customer’s propensity to purchase four different products in the next month (Fig. 1) given sequences of transactions, geo locations, and dialogues from the beginning of this month. Solutions to this problem are used to plan marketing campaigns and prepare sales communications through various communication channels with the client.

We implement an out-of-fold validation protocol to conduct our experiments. The entire client dataset is divided into five folds: four folds are used for training, while the remaining fold is designated for testing. Both the training and testing sets are made publicly available alongside our dataset to enable future researchers to compare performance metrics.

The baseline methods outlined earlier are applied as follows. First, we train our models using the training set. Considering the temporal structure of our target, we compute the embedding of each client’s history up to one month, focusing on the presence of the target product (Fig. 1). We then evaluate the model using the multi-label classification metric ROC AUC across the 12 months of 2022 and for four binary product labels.

#### 3.2.2 Multimodal Matching

Multimodal matching [] involves aligning and comparing different modalities to identify meaningful relationships or connections between them. Frequently, data from multiple sources for the same client are matched using predefined rules or heuristics, which may not always yield optimal results. To enhance the accuracy of this process, specialized identification algorithms are required to more precisely compare different modalities.

For the matching task, we employed a framework analogous to CLIP []. We utilized GRU encoders to embed pairs of samples from two input modalities, labeling them as either positive matches (i.e., data from the same client) or negative matches (i.e., data from different clients). The model was trained using the InfoNCE loss function [], which maximizes similarity for positive pairs while minimizing it for negative pairs. To assess the model’s performance, we used Recall@1, Recall@50 and Recall@100 metrics.

## 4 Experiments

Our models, experiments, and training procedures were implemented in Python, leveraging PyTorch and PyTorch Lightning for deep learning tasks, and PySpark for distributed data processing. We trained the neural networks using NVIDIA V100 GPU, while the boosting models were trained on computational clusters equipped with 600 cores. The reported experiments, including extensive hyperparameters optimization, required approximately 500 hours of computation time.

### 4.1 Model and training hyperparameters

We employed the unsupervised baseline methods (CoLES, TabGPT, TabBERT, Aggregation) with default hyperparameters from the pytorch-lifestream framework. The PyTorch implementation of the Adam optimizer was utilized, with an initial learning rate of 0.001, coupled with the StepLR scheduler. Models were saved based on the lowest validation loss or the highest validation unsupervised metrics [], evaluated after each training epoch, with training of 15 epochs.

We employed 24-dimensional embeddings for categorical features and clipped the number of categories for features with many unique values. We applied either an identical mapping or a logarithmic transformation for numerical features. We used the gradient boosting algorithm available in PySpark ML for downstream tasks.

### 4.2 Campaigning task

In this Subsection, we provide experimental results for the campaigning task in unimodal and multimodal baselines.

One of the main objectives of our paper is to provide a real data benchmark to facilitate the development of multimodal algorithms. To achieve this, we must demonstrate that an algorithm outperforming another on our public benchmark will similarly outperform it on real data.

Table 1 shows that the transaction modality plays the most crucial role in achieving accurate classification. In contrast, dialogues and geostream, when used in a unimodal setting, performed only slightly better than a random estimator. However, as shown in Table 2, the predictive performance improved significantly in the multimodal setting through the integration of additional modalities. The overall trend indicates a consistent improvement in validation metrics as more modalities are incorporated. Specifically, the multimodal late fusion approach enhanced predictive accuracy by 1–2% when adding a single data source to the transaction stream, and by up to 3% when utilizing all available data sources.

Models trained on public datasets generally exhibit higher performance scores, which is an anticipated outcome of the data anonymization process. Nevertheless, the relative ranking of different methods remains consistent across both MBD and private datasets. Fig. 7 in Appendix B demonstrates a strong correlation in performance metrics between the public and private datasets, with a Kendall-tau correlation coefficient of 0.94.

Comprehensive results for each modality and all possible fusion combinations of multiple modalities are provided in Appendix B. We denote our modalities: geostream, transactions, and dialogues are represented as Geo, Trx, and Dialogs, respectively. We specify the method with the modality to indicate that a particular method was applied. For example, if embeddings from the CoLES model are used on transactions, we denote this as TrxCoLES.

**Table 1: Mean ROC-AUC of campaign results for the original private dataset and MBD using unimodal methods.**
| Modalities | Methods | MBD | Private dataset |
| --- | --- | --- | --- |
| Dialog | Mean | 0.595 $\pm$ 0.002 | 0.604 $\pm$ 0.001 |
|  | Last | 0.586 $\pm$ 0.001 | 0.590 $\pm$ 0.001 |
|  | Supervised | 0.540 $\pm$ 0.012 | 0.538 $\pm$ 0.010 |
| Geo | Aggregation | 0.555 $\pm$ 0.001 | 0.554 $\pm$ 0.001 |
|  | TabGPT | 0.621 $\pm$ 0.003 | 0.622 $\pm$ 0.001 |
|  | TabBERT | 0.603 $\pm$ 0.002 | 0.596 $\pm$ 0.002 |
|  | CoLES | 0.598 $\pm$ 0.004 | 0.601 $\pm$ 0.004 |
|  | Supervised | 0.566 $\pm$ 0.002 | 0.563 $\pm$ 0.002 |
| Trx | Aggregation | 0.783 $\pm$ 0.002 | 0.791 $\pm$ 0.017 |
|  | TabGPT | 0.802 $\pm$ 0.002 | 0.796 $\pm$ 0.000 |
|  | TabBERT | 0.762 $\pm$ 0.004 | 0.754 $\pm$ 0.011 |
|  | CoLES | 0.773 $\pm$ 0.002 | 0.772 $\pm$ 0.002 |
|  | Supervised | 0.819 $\pm$ 0.002 | 0.813 $\pm$ 0.002 |

**Table 2: Mean ROC-AUC of campaining results for original private dataset and MBD in late fusion setting.**
| Methods | Modalities | MBD | Private dataset |
| --- | --- | --- | --- |
| CoLES | Trx | 0.773 $\pm$ 0.002 | 0.772 $\pm$ 0.002 |
|  | Trx + Geo | 0.775 $\pm$ 0.002 | 0.772 $\pm$ 0.002 |
|  | Trx + Dialog | 0.781 $\pm$ 0.001 | 0.776 $\pm$ 0.002 |
|  | Trx + Dialog + Geo | 0.783 $\pm$ 0.001 | 0.777 $\pm$ 0.002 |
| TabGPT | Trx | 0.802 $\pm$ 0.001 | 0.796 $\pm$ 0.000 |
|  | Trx + Geo | 0.800 $\pm$ 0.001 | 0.796 $\pm$ 0.001 |
|  | Trx + Dialog | 0.810 $\pm$ 0.002 | 0.805 $\pm$ 0.001 |
|  | Trx + Dialog + Geo | 0.808 $\pm$ 0.001 | 0.802 $\pm$ 0.001 |
| Supervised | Trx | 0.819 $\pm$ 0.002 | 0.813 $\pm$ 0.002 |
|  | Trx + Geo | 0.819 $\pm$ 0.001 | 0.815 $\pm$ 0.001 |
|  | Trx + Dialog | 0.821 $\pm$ 0.0006 | 0.817 $\pm$ 0.0006 |
|  | Trx + Dialog + Geo | 0.824 $\pm$ 0.001 | 0.821 $\pm$ 0.001 |
| TabBERT | Trx | 0.762 $\pm$ 0.004 | 0.754 $\pm$ 0.011 |
|  | Trx + Geo | 0.764 $\pm$ 0.004 | 0.756 $\pm$ 0.011 |
|  | Trx + Dialog | 0.773 $\pm$ 0.003 | 0.765 $\pm$ 0.009 |
|  | Trx + Dialog + Geo | 0.775 $\pm$ 0.003 | 0.766 $\pm$ 0.009 |

### 4.3 Multimodal matching task

In this subsection, we present the results of our proposed multimodal matching benchmark, summarized in Table 3, which includes both transactions and dialogues. Detailed results for other modalities can be found in Appendix B, in Table 9. In this evaluation, we report recall@1, recall@50, and recall@100 for each modality, measured in both directions. For example, in the case of the transaction and geostream pair, we compute both Trx2Geo and Geo2Trx, allowing for an evaluation of alignment from both perspectives.

Our analysis reveals considerable variation in performance across different modality pairs. Specifically, dialogue data consistently exhibits weaker matching performance compared to other modalities, such as transactions and geostream, which demonstrate significantly stronger alignment. This disparity suggests potential limitations within the dialogue modality, indicating that it may offer less complementary or aligned information. Alternatively, the unique structure of dialogue data may necessitate more sophisticated or specialized techniques for effective integration with other modalities.

These findings provide important insights for the advancement of multimodal matching methods, underscoring the need for further refinement.

**Table 3: Multimodal matching results: Transactions and Dialogues**
|  | Recall@1 | Recall@50 | Recall@100 |
| --- | --- | --- | --- |
| Trx2Geo | 0.006 $\pm$ 0.0003 | 0.1967 $\pm$ 0.002 | 0.303 $\pm$ 0.004 |
| Geo2Trx | 0.004 $\pm$ 0.0003 | 0.1624 $\pm$ 0.002 | 0.262 $\pm$ 0.004 |

## 5 Related Work

### 5.1 Financial data

The multitude of services and processes in banks generates a variety of data that can be considered as modalities. Early works such as [, ] use feature processing techniques that remove multimodal complexity and present data in tabular form. The Amex dataset[] improves information content and complexity. Here, a wide range of different financial aggregates are presented as a sequence of historical slices.
The development of deep learning methods has led to the ability to work with complexly structured data, such as sequences of events. Quite a few datasets [, , ], mostly monomodal, presented mainly at ML competitions. To work with such data, both supervised[, , ] and unsupervised methods[, , ] are used.
Multimodal financial sequential data proposed in DataFusion2022 []. There are two sequential modalities, transaction and web clickstream, and two downstream tasks: matching and education level prediction. However, this is an extremely small dataset of 17K clients, and no accurate baseline model is available.

### 5.2 Other event sequence domains

Temporal point process[, ] model streams of discrete events in continuous time by constructing a neurally multivariate point process. The authors use a large collection of datasets from different types of modalities: Media (Retweets[], MemeTrack[], Amazon[], IPTV[]), Medical (MIMIC-II[]), Social(Stack Overflow[], Linkedin[]) and Financial (Transaction[]) data. All datasets are independent, and each one is single-modal.
EventStreamGPT [] uses multimodal medical record datasets, MIMIC-IV[]. The authors propose a GPT-like approach for continuous-time event sequences. The structure of this dataset is close to the MDB. But it must be emphasized that financial data, unlike medical data, contains longer chains of events, more regular patterns, and individual transactions are less informative. These features may influence the quality of the methods used.

### 5.3 Geostream and dialogues

Geodata is used for various tasks. One of the uses of geo is a visualization of analytics on a map []. However, geo is used here not as a separate modality but as additional tags to the mainstream of tweets. Mobile marketers[] use geo-targeting for pricing and send personalized recommendations. In [] geo hashes are used for user mobility detection and prediction.

We encode our dialogue entries via a pretrained NLP Model. Previously, such models have already been used[] for text anonymization tasks. Embeddings preserve the meaning of the text, which was shown in[]. Pre-trained text embeddings can capture text sentiment and improve text-to-speech models[].

## 6 Limitations

The study used data from only one company, which may limit the generalizability of the results to other organizations. In addition, the data was subject to de-identification, which limits the possibility of using models trained on this dataset outside of it. Also, the data analysis results can not be generalized. In other words, based on this dataset, it is impossible to draw conclusions regarding specific regions and market characteristics or perform deep text analytics. However, within the dataset, the data is consistent, which allows us to draw correct conclusions about the performance of multimodal or unimodal methods for working with sequences.
It is also worth noting that the study was conducted on a sample of clients of a certain segment who had the opportunity to purchase certain products and does not cover all possible groups of consumers.

## 7 Ethics and Societal Implications

Our dataset is designed to facilitate the development of algorithms for analyzing complex event data, with the goal of enhancing the accuracy and efficiency of predictive models. Our event data does not contain characteristics such as gender, age, or race, which can be used to discriminate against certain groups of people. This makes the models trained on our dataset more ethical.
However, it is important to note that companies must comply with legal grounds and obtain customer consent before collecting and analyzing additional data. It is also worth mentioning the need to ensure the security of data storage and transmission to prevent leakage of confidential information. Failure to comply with these conditions may result in privacy violations.

## 8 Conclusion and future work

In this paper, we introduced the first large-scale publicly available multimodal banking dataset, MBD(^3^33[https://huggingface.co/datasets/ai-lab/MBD](https://huggingface.co/datasets/ai-lab/MBD)), which contains anonymized sequential data (bank transactions, geo position, and technical support dialogues) for 1M bank clients. It can be used to create industrial multimodal models for event sequences to predict client needs. Using this dataset, we present the novel benchmark based on MBD with two practically important tasks: campaigning (purchase prediction for the next month) and matching different modalities of clients. Our experimental results demonstrate that even a simple fusion of multiple data sources improves the overall quality compared to single-modal baselines (Table 2). Also, we show that anonymization does not significantly impact the algorithms’ performance, so our dataset is appropriate for choosing the best model that can be implemented in production scenarios.
As a result, our work opens new horizons for developing practically relevant large-scale multimodal algorithms for event sequences.

In the future, expanding the range of tasks with our dataset is possible. For example, it is possible to estimate the multimodal techniques of the next-event prediction.
Moreover, or baseline methods are based on late fusion, so it is very important to study more sophisticated models that consider possible mutual influence between modalities.

## Appendix A Dataset Statistics

**Table 4: Transaction features**
| Features | Number of categories | Description |
| --- | --- | --- |
| amount | - | transaction amount |
| event time | - | transaction time |
| currency | 15 | transfer currency |
| event type | 56 | transaction type |
| event subtype | 62 | transaction subtype |
| src type11 | 101 | sender field type 1 |
| src type12 | 536 | sender field subtype 1 |
| dst type11 | 123 | receiver field type 1 |
| dst type12 | 649 | receiver field subtype 1 |
| src type21 | 40131 | sender field type 2 |
| src type22 | 87 | sender field subtype 2 |
| src type31 | 2389 | sender field type 3 |
| src type32 | 88 | sender field subtype 3 |

Figure: Figure 2: The histogram of the number clients with a certain length of transaction history
Refer to caption: x2.png

Figure: Figure 3: Sample data of dialogues modality.
Refer to caption: extracted/5880953/pictures/sample_data/Dial.png

Figure: Figure 4: The histogram of the number of clients with a certain number of dialogues
Refer to caption: x3.png

Figure: Figure 5: Sample data of geostream modality.
Refer to caption: extracted/5880953/pictures/sample_data/Geo.png

Figure: Figure 6: The histogram of the number of clients with a certain length of geostream
Refer to caption: x4.png

## Appendix B Detailed experimental results

**Table 5: Blending results on private dataset**
| methods | mean | target_1 | target_2 | target_3 | target_4 |
| --- | --- | --- | --- | --- | --- |
| DialogLast | 0.590 $\pm$ 0.001 | 0.633 $\pm$ 0.001 | 0.604 $\pm$ 0.005 | 0.549 $\pm$ 0.002 | 0.576 $\pm$ 0.002 |
| DialogLast+GeoAggregation | 0.603 $\pm$ 0.002 | 0.632 $\pm$ 0.001 | 0.629 $\pm$ 0.007 | 0.558 $\pm$ 0.001 | 0.592 $\pm$ 0.002 |
| DialogLast+GeoCoLES | 0.632 $\pm$ 0.002 | 0.641 $\pm$ 0.001 | 0.688 $\pm$ 0.008 | 0.580 $\pm$ 0.002 | 0.619 $\pm$ 0.003 |
| DialogLast+GeoTabGPT | 0.648 $\pm$ 0.002 | 0.654 $\pm$ 0.001 | 0.715 $\pm$ 0.008 | 0.591 $\pm$ 0.003 | 0.632 $\pm$ 0.003 |
| DialogLast+GeoTabBERT | 0.628 $\pm$ 0.001 | 0.641 $\pm$ 0.002 | 0.683 $\pm$ 0.006 | 0.579 $\pm$ 0.002 | 0.610 $\pm$ 0.006 |
| DialogLast+TrxAggregation | 0.731 $\pm$ 0.001 | 0.701 $\pm$ 0.001 | 0.816 $\pm$ 0.003 | 0.673 $\pm$ 0.001 | 0.735 $\pm$ 0.002 |
| DialogLast+TrxAggregation+GeoAggregation | 0.729 $\pm$ 0.001 | 0.694 $\pm$ 0.001 | 0.812 $\pm$ 0.003 | 0.670 $\pm$ 0.001 | 0.738 $\pm$ 0.003 |
| DialogLast+TrxCoLES | 0.715 $\pm$ 0.002 | 0.692 $\pm$ 0.002 | 0.800 $\pm$ 0.005 | 0.645 $\pm$ 0.002 | 0.723 $\pm$ 0.005 |
| DialogLast+TrxCoLES+GeoCoLES | 0.720 $\pm$ 0.002 | 0.689 $\pm$ 0.003 | 0.815 $\pm$ 0.003 | 0.644 $\pm$ 0.002 | 0.734 $\pm$ 0.005 |
| DialogLast+TrxTabGPT | 0.739 $\pm$ 0.001 | 0.683 $\pm$ 0.001 | 0.820 $\pm$ 0.005 | 0.685 $\pm$ 0.001 | 0.767 $\pm$ 0.005 |
| DialogLast+TrxTabGPT+GeoTabGPT | 0.749 $\pm$ 0.002 | 0.690 $\pm$ 0.001 | 0.838 $\pm$ 0.005 | 0.687 $\pm$ 0.001 | 0.780 $\pm$ 0.005 |
| DialogLast+TrxTabBERT | 0.710 $\pm$ 0.006 | 0.686 $\pm$ 0.004 | 0.806 $\pm$ 0.004 | 0.628 $\pm$ 0.006 | 0.720 $\pm$ 0.012 |
| DialogLast+TrxTabBERT+GeoTabBERT | 0.716 $\pm$ 0.005 | 0.683 $\pm$ 0.003 | 0.821 $\pm$ 0.003 | 0.627 $\pm$ 0.006 | 0.731 $\pm$ 0.009 |
| DialogMean | 0.604 $\pm$ 0.001 | 0.636 $\pm$ 0.002 | 0.629 $\pm$ 0.001 | 0.564 $\pm$ 0.001 | 0.587 $\pm$ 0.001 |
| DialogMean+GeoAggregation | 0.613 $\pm$ 0.001 | 0.635 $\pm$ 0.002 | 0.648 $\pm$ 0.001 | 0.568 $\pm$ 0.001 | 0.601 $\pm$ 0.002 |
| DialogMean+GeoCoLES | 0.638 $\pm$ 0.002 | 0.643 $\pm$ 0.002 | 0.699 $\pm$ 0.005 | 0.585 $\pm$ 0.001 | 0.626 $\pm$ 0.001 |
| DialogMean+GeoTabGPT | 0.653 $\pm$ 0.001 | 0.656 $\pm$ 0.001 | 0.725 $\pm$ 0.005 | 0.595 $\pm$ 0.002 | 0.638 $\pm$ 0.003 |
| DialogMean+GeoTabBERT | 0.635 $\pm$ 0.002 | 0.643 $\pm$ 0.002 | 0.696 $\pm$ 0.004 | 0.584 $\pm$ 0.001 | 0.617 $\pm$ 0.006 |
| DialogMean+TrxAggregation | 0.732 $\pm$ 0.001 | 0.700 $\pm$ 0.001 | 0.816 $\pm$ 0.003 | 0.673 $\pm$ 0.001 | 0.739 $\pm$ 0.001 |
| DialogMean+TrxAggregation+GeoAggregation | 0.729 $\pm$ 0.001 | 0.694 $\pm$ 0.001 | 0.812 $\pm$ 0.004 | 0.670 $\pm$ 0.001 | 0.741 $\pm$ 0.002 |
| DialogMean+TrxCoLES | 0.715 $\pm$ 0.002 | 0.692 $\pm$ 0.002 | 0.800 $\pm$ 0.004 | 0.645 $\pm$ 0.002 | 0.725 $\pm$ 0.004 |
| DialogMean+TrxCoLES+GeoCoLES | 0.721 $\pm$ 0.002 | 0.689 $\pm$ 0.003 | 0.813 $\pm$ 0.003 | 0.644 $\pm$ 0.002 | 0.737 $\pm$ 0.004 |
| DialogMean+TrxTabGPT | 0.739 $\pm$ 0.001 | 0.682 $\pm$ 0.001 | 0.819 $\pm$ 0.005 | 0.684 $\pm$ 0.001 | 0.769 $\pm$ 0.004 |
| DialogMean+TrxTabGPT+GeoTabGPT | 0.748 $\pm$ 0.001 | 0.689 $\pm$ 0.001 | 0.837 $\pm$ 0.004 | 0.686 $\pm$ 0.001 | 0.782 $\pm$ 0.004 |
| DialogMean+TrxTabBERT | 0.711 $\pm$ 0.006 | 0.685 $\pm$ 0.004 | 0.806 $\pm$ 0.005 | 0.629 $\pm$ 0.005 | 0.726 $\pm$ 0.011 |
| DialogMean+TrxTabBERT+GeoTabBERT | 0.717 $\pm$ 0.005 | 0.682 $\pm$ 0.003 | 0.821 $\pm$ 0.004 | 0.628 $\pm$ 0.006 | 0.735 $\pm$ 0.009 |
| GeoAggregation | 0.554 $\pm$ 0.001 | 0.540 $\pm$ 0.001 | 0.584 $\pm$ 0.002 | 0.534 $\pm$ 0.001 | 0.559 $\pm$ 0.001 |
| GeoCoLES | 0.601 $\pm$ 0.004 | 0.565 $\pm$ 0.004 | 0.668 $\pm$ 0.011 | 0.571 $\pm$ 0.003 | 0.600 $\pm$ 0.003 |
| GeoTabGPT | 0.622 $\pm$ 0.001 | 0.589 $\pm$ 0.001 | 0.700 $\pm$ 0.008 | 0.586 $\pm$ 0.003 | 0.615 $\pm$ 0.004 |
| GeoTabBERT | 0.596 $\pm$ 0.002 | 0.566 $\pm$ 0.003 | 0.663 $\pm$ 0.010 | 0.570 $\pm$ 0.003 | 0.585 $\pm$ 0.007 |
| TrxAggregation | 0.783 $\pm$ 0.001 | 0.743 $\pm$ 0.001 | 0.825 $\pm$ 0.002 | 0.764 $\pm$ 0.001 | 0.801 $\pm$ 0.002 |
| TrxAggregation+GeoAggregation | 0.774 $\pm$ 0.001 | 0.733 $\pm$ 0.001 | 0.817 $\pm$ 0.003 | 0.756 $\pm$ 0.001 | 0.789 $\pm$ 0.003 |
| TrxCoLES | 0.772 $\pm$ 0.002 | 0.734 $\pm$ 0.003 | 0.813 $\pm$ 0.004 | 0.747 $\pm$ 0.002 | 0.793 $\pm$ 0.003 |
| TrxCoLES+GeoCoLES | 0.772 $\pm$ 0.002 | 0.729 $\pm$ 0.003 | 0.825 $\pm$ 0.006 | 0.740 $\pm$ 0.002 | 0.795 $\pm$ 0.003 |
| TrxTabGPT | 0.796 $\pm$ 0.000 | 0.746 $\pm$ 0.001 | 0.837 $\pm$ 0.004 | 0.778 $\pm$ 0.001 | 0.825 $\pm$ 0.004 |
| TrxTabGPT+GeoTabGPT | 0.798 $\pm$ 0.001 | 0.743 $\pm$ 0.001 | 0.850 $\pm$ 0.003 | 0.772 $\pm$ 0.001 | 0.827 $\pm$ 0.004 |
| TrxTabBERT | 0.754 $\pm$ 0.011 | 0.707 $\pm$ 0.019 | 0.815 $\pm$ 0.006 | 0.717 $\pm$ 0.012 | 0.778 $\pm$ 0.012 |
| TrxTabBERT+GeoTabBERT | 0.758 $\pm$ 0.010 | 0.707 $\pm$ 0.016 | 0.831 $\pm$ 0.006 | 0.713 $\pm$ 0.011 | 0.781 $\pm$ 0.010 |

**Table 6: Late Fusion results on private dataset**
| methods | mean | target_1 | target_2 | target_3 | target_4 |
| --- | --- | --- | --- | --- | --- |
| DialogLast | 0.590 $\pm$ 0.001 | 0.633 $\pm$ 0.001 | 0.604 $\pm$ 0.005 | 0.549 $\pm$ 0.002 | 0.576 $\pm$ 0.002 |
| DialogLast+GeoAggregation | 0.636 $\pm$ 0.001 | 0.603 $\pm$ 0.001 | 0.649 $\pm$ 0.003 | 0.645 $\pm$ 0.001 | 0.648 $\pm$ 0.002 |
| DialogLast+GeoCoLES | 0.647 $\pm$ 0.002 | 0.615 $\pm$ 0.002 | 0.660 $\pm$ 0.006 | 0.650 $\pm$ 0.002 | 0.663 $\pm$ 0.003 |
| DialogLast+GeoTabGPT | 0.654 $\pm$ 0.002 | 0.631 $\pm$ 0.001 | 0.673 $\pm$ 0.005 | 0.652 $\pm$ 0.002 | 0.662 $\pm$ 0.002 |
| DialogLast+GeoTabBERT | 0.642 $\pm$ 0.003 | 0.613 $\pm$ 0.002 | 0.655 $\pm$ 0.009 | 0.646 $\pm$ 0.001 | 0.655 $\pm$ 0.004 |
| DialogLast+TrxAggregation | 0.788 $\pm$ 0.001 | 0.749 $\pm$ 0.001 | 0.826 $\pm$ 0.003 | 0.772 $\pm$ 0.001 | 0.805 $\pm$ 0.004 |
| DialogLast+TrxAggregation+GeoAggregation | 0.787 $\pm$ 0.001 | 0.746 $\pm$ 0.001 | 0.826 $\pm$ 0.004 | 0.773 $\pm$ 0.001 | 0.804 $\pm$ 0.002 |
| DialogLast+TrxCoLES | 0.776 $\pm$ 0.002 | 0.739 $\pm$ 0.003 | 0.814 $\pm$ 0.004 | 0.753 $\pm$ 0.002 | 0.797 $\pm$ 0.003 |
| DialogLast+TrxCoLES+GeoCoLES | 0.777 $\pm$ 0.002 | 0.739 $\pm$ 0.004 | 0.816 $\pm$ 0.004 | 0.755 $\pm$ 0.003 | 0.798 $\pm$ 0.002 |
| DialogLast+TrxTabGPT | 0.805 $\pm$ 0.001 | 0.775 $\pm$ 0.001 | 0.842 $\pm$ 0.002 | 0.778 $\pm$ 0.002 | 0.823 $\pm$ 0.005 |
| DialogLast+TrxTabGPT+GeoTabGPT | 0.802 $\pm$ 0.001 | 0.764 $\pm$ 0.001 | 0.845 $\pm$ 0.002 | 0.777 $\pm$ 0.001 | 0.820 $\pm$ 0.004 |
| DialogLast+TrxTabBERT | 0.764 $\pm$ 0.009 | 0.715 $\pm$ 0.019 | 0.817 $\pm$ 0.007 | 0.734 $\pm$ 0.007 | 0.789 $\pm$ 0.008 |
| DialogLast+TrxTabBERT+GeoTabBERT | 0.765 $\pm$ 0.009 | 0.714 $\pm$ 0.019 | 0.819 $\pm$ 0.006 | 0.738 $\pm$ 0.007 | 0.789 $\pm$ 0.008 |
| DialogMean | 0.604 $\pm$ 0.001 | 0.636 $\pm$ 0.002 | 0.629 $\pm$ 0.001 | 0.564 $\pm$ 0.001 | 0.587 $\pm$ 0.001 |
| DialogMean+GeoAggregation | 0.642 $\pm$ 0.001 | 0.605 $\pm$ 0.002 | 0.658 $\pm$ 0.002 | 0.650 $\pm$ 0.001 | 0.654 $\pm$ 0.001 |
| DialogMean+GeoCoLES | 0.653 $\pm$ 0.001 | 0.618 $\pm$ 0.002 | 0.670 $\pm$ 0.002 | 0.656 $\pm$ 0.001 | 0.669 $\pm$ 0.001 |
| DialogMean+GeoTabGPT | 0.661 $\pm$ 0.001 | 0.633 $\pm$ 0.001 | 0.681 $\pm$ 0.004 | 0.657 $\pm$ 0.002 | 0.671 $\pm$ 0.002 |
| DialogMean+GeoTabBERT | 0.648 $\pm$ 0.003 | 0.613 $\pm$ 0.003 | 0.666 $\pm$ 0.005 | 0.652 $\pm$ 0.002 | 0.662 $\pm$ 0.004 |
| DialogMean+TrxAggregation | 0.788 $\pm$ 0.000 | 0.749 $\pm$ 0.001 | 0.825 $\pm$ 0.003 | 0.773 $\pm$ 0.001 | 0.804 $\pm$ 0.001 |
| DialogMean+TrxAggregation+GeoAggregation | 0.787 $\pm$ 0.001 | 0.746 $\pm$ 0.001 | 0.825 $\pm$ 0.002 | 0.773 $\pm$ 0.001 | 0.804 $\pm$ 0.003 |
| DialogMean+TrxCoLES | 0.776 $\pm$ 0.002 | 0.739 $\pm$ 0.003 | 0.814 $\pm$ 0.004 | 0.753 $\pm$ 0.002 | 0.798 $\pm$ 0.003 |
| DialogMean+TrxCoLES+GeoCoLES | 0.777 $\pm$ 0.002 | 0.739 $\pm$ 0.002 | 0.815 $\pm$ 0.003 | 0.755 $\pm$ 0.003 | 0.799 $\pm$ 0.002 |
| DialogMean+TrxTabGPT | 0.805 $\pm$ 0.001 | 0.775 $\pm$ 0.001 | 0.843 $\pm$ 0.001 | 0.778 $\pm$ 0.002 | 0.824 $\pm$ 0.004 |
| DialogMean+TrxTabGPT+GeoTabGPT | 0.802 $\pm$ 0.001 | 0.764 $\pm$ 0.001 | 0.845 $\pm$ 0.002 | 0.777 $\pm$ 0.001 | 0.821 $\pm$ 0.003 |
| DialogMean+TrxTabBERT | 0.765 $\pm$ 0.009 | 0.715 $\pm$ 0.019 | 0.817 $\pm$ 0.006 | 0.735 $\pm$ 0.006 | 0.792 $\pm$ 0.007 |
| DialogMean+TrxTabBERT+GeoTabBERT | 0.766 $\pm$ 0.009 | 0.714 $\pm$ 0.019 | 0.819 $\pm$ 0.006 | 0.738 $\pm$ 0.006 | 0.792 $\pm$ 0.007 |
| GeoAggregation | 0.554 $\pm$ 0.001 | 0.540 $\pm$ 0.001 | 0.584 $\pm$ 0.002 | 0.534 $\pm$ 0.001 | 0.559 $\pm$ 0.001 |
| GeoCoLES | 0.601 $\pm$ 0.004 | 0.565 $\pm$ 0.004 | 0.668 $\pm$ 0.011 | 0.571 $\pm$ 0.003 | 0.600 $\pm$ 0.003 |
| GeoTabGPT | 0.622 $\pm$ 0.001 | 0.589 $\pm$ 0.001 | 0.700 $\pm$ 0.008 | 0.586 $\pm$ 0.003 | 0.615 $\pm$ 0.004 |
| GeoTabBERT | 0.596 $\pm$ 0.002 | 0.566 $\pm$ 0.003 | 0.663 $\pm$ 0.010 | 0.570 $\pm$ 0.003 | 0.585 $\pm$ 0.007 |
| TrxAggregation | 0.780 $\pm$ 0.005 | 0.743 $\pm$ 0.001 | 0.824 $\pm$ 0.001 | 0.762 $\pm$ 0.001 | 0.791 $\pm$ 0.017 |
| TrxAggregation+GeoAggregation | 0.779 $\pm$ 0.004 | 0.740 $\pm$ 0.001 | 0.828 $\pm$ 0.002 | 0.762 $\pm$ 0.001 | 0.787 $\pm$ 0.013 |
| TrxCoLES | 0.772 $\pm$ 0.002 | 0.734 $\pm$ 0.003 | 0.813 $\pm$ 0.004 | 0.746 $\pm$ 0.001 | 0.793 $\pm$ 0.003 |
| TrxCoLES+GeoCoLES | 0.772 $\pm$ 0.002 | 0.734 $\pm$ 0.004 | 0.814 $\pm$ 0.004 | 0.749 $\pm$ 0.002 | 0.792 $\pm$ 0.002 |
| TrxTabGPT | 0.796 $\pm$ 0.000 | 0.745 $\pm$ 0.001 | 0.837 $\pm$ 0.004 | 0.777 $\pm$ 0.001 | 0.824 $\pm$ 0.005 |
| TrxTabGPT+GeoTabGPT | 0.796 $\pm$ 0.001 | 0.751 $\pm$ 0.002 | 0.843 $\pm$ 0.003 | 0.774 $\pm$ 0.001 | 0.816 $\pm$ 0.003 |
| TrxTabBERT | 0.754 $\pm$ 0.011 | 0.707 $\pm$ 0.019 | 0.815 $\pm$ 0.006 | 0.717 $\pm$ 0.012 | 0.778 $\pm$ 0.012 |
| TrxTabBERT+GeoTabBERT | 0.756 $\pm$ 0.011 | 0.707 $\pm$ 0.019 | 0.816 $\pm$ 0.005 | 0.722 $\pm$ 0.010 | 0.778 $\pm$ 0.012 |

**Table 7: Blending results on public dataset**
| methods | mean | target_1 | target_2 | target_3 | target_4 |
| --- | --- | --- | --- | --- | --- |
| DialogLast | 0.586 $\pm$ 0.001 | 0.602 $\pm$ 0.001 | 0.622 $\pm$ 0.004 | 0.554 $\pm$ 0.001 | 0.567 $\pm$ 0.002 |
| DialogLast+GeoAggregation | 0.600 $\pm$ 0.001 | 0.605 $\pm$ 0.000 | 0.648 $\pm$ 0.004 | 0.560 $\pm$ 0.001 | 0.585 $\pm$ 0.002 |
| DialogLast+GeoCoLES | 0.625 $\pm$ 0.003 | 0.615 $\pm$ 0.001 | 0.700 $\pm$ 0.005 | 0.577 $\pm$ 0.003 | 0.609 $\pm$ 0.003 |
| DialogLast+GeoTabGPT | 0.642 $\pm$ 0.002 | 0.629 $\pm$ 0.001 | 0.725 $\pm$ 0.007 | 0.589 $\pm$ 0.002 | 0.623 $\pm$ 0.002 |
| DialogLast+GeoTabBERT | 0.629 $\pm$ 0.001 | 0.616 $\pm$ 0.001 | 0.709 $\pm$ 0.006 | 0.577 $\pm$ 0.002 | 0.613 $\pm$ 0.001 |
| DialogLast+TrxAggregation | 0.732 $\pm$ 0.002 | 0.685 $\pm$ 0.001 | 0.826 $\pm$ 0.004 | 0.688 $\pm$ 0.001 | 0.731 $\pm$ 0.003 |
| DialogLast+TrxAggregation+GeoAggregation | 0.731 $\pm$ 0.002 | 0.681 $\pm$ 0.001 | 0.825 $\pm$ 0.005 | 0.684 $\pm$ 0.001 | 0.736 $\pm$ 0.003 |
| DialogLast+TrxCoLES | 0.714 $\pm$ 0.001 | 0.675 $\pm$ 0.001 | 0.806 $\pm$ 0.004 | 0.658 $\pm$ 0.002 | 0.715 $\pm$ 0.003 |
| DialogLast+TrxCoLES+GeoCoLES | 0.720 $\pm$ 0.001 | 0.673 $\pm$ 0.002 | 0.822 $\pm$ 0.004 | 0.657 $\pm$ 0.002 | 0.728 $\pm$ 0.002 |
| DialogLast+TrxTabGPT | 0.743 $\pm$ 0.002 | 0.677 $\pm$ 0.001 | 0.834 $\pm$ 0.004 | 0.697 $\pm$ 0.001 | 0.766 $\pm$ 0.003 |
| DialogLast+TrxTabGPT+GeoTabGPT | 0.753 $\pm$ 0.001 | 0.684 $\pm$ 0.001 | 0.848 $\pm$ 0.004 | 0.700 $\pm$ 0.001 | 0.779 $\pm$ 0.003 |
| DialogLast+TrxTabBERT | 0.710 $\pm$ 0.005 | 0.669 $\pm$ 0.003 | 0.814 $\pm$ 0.006 | 0.640 $\pm$ 0.008 | 0.715 $\pm$ 0.007 |
| DialogLast+TrxTabBERT+GeoTabBERT | 0.718 $\pm$ 0.004 | 0.670 $\pm$ 0.003 | 0.828 $\pm$ 0.005 | 0.641 $\pm$ 0.008 | 0.733 $\pm$ 0.006 |
| DialogMean | 0.595 $\pm$ 0.002 | 0.600 $\pm$ 0.001 | 0.633 $\pm$ 0.006 | 0.566 $\pm$ 0.001 | 0.580 $\pm$ 0.002 |
| DialogMean+GeoAggregation | 0.607 $\pm$ 0.001 | 0.604 $\pm$ 0.000 | 0.657 $\pm$ 0.004 | 0.572 $\pm$ 0.000 | 0.596 $\pm$ 0.002 |
| DialogMean+GeoCoLES | 0.630 $\pm$ 0.002 | 0.615 $\pm$ 0.001 | 0.703 $\pm$ 0.006 | 0.586 $\pm$ 0.002 | 0.618 $\pm$ 0.003 |
| DialogMean+GeoTabGPT | 0.645 $\pm$ 0.002 | 0.629 $\pm$ 0.001 | 0.727 $\pm$ 0.010 | 0.596 $\pm$ 0.001 | 0.630 $\pm$ 0.001 |
| DialogMean+GeoTabBERT | 0.634 $\pm$ 0.002 | 0.616 $\pm$ 0.002 | 0.713 $\pm$ 0.006 | 0.586 $\pm$ 0.002 | 0.621 $\pm$ 0.002 |
| DialogMean+TrxAggregation | 0.732 $\pm$ 0.001 | 0.684 $\pm$ 0.001 | 0.824 $\pm$ 0.001 | 0.688 $\pm$ 0.001 | 0.732 $\pm$ 0.002 |
| DialogMean+TrxAggregation+GeoAggregation | 0.731 $\pm$ 0.001 | 0.679 $\pm$ 0.000 | 0.822 $\pm$ 0.002 | 0.684 $\pm$ 0.001 | 0.737 $\pm$ 0.002 |
| DialogMean+TrxCoLES | 0.713 $\pm$ 0.001 | 0.674 $\pm$ 0.001 | 0.804 $\pm$ 0.002 | 0.659 $\pm$ 0.002 | 0.717 $\pm$ 0.003 |
| DialogMean+TrxCoLES+GeoCoLES | 0.719 $\pm$ 0.001 | 0.672 $\pm$ 0.002 | 0.819 $\pm$ 0.003 | 0.658 $\pm$ 0.002 | 0.729 $\pm$ 0.002 |
| DialogMean+TrxTabGPT | 0.742 $\pm$ 0.001 | 0.675 $\pm$ 0.001 | 0.829 $\pm$ 0.004 | 0.697 $\pm$ 0.001 | 0.766 $\pm$ 0.003 |
| DialogMean+TrxTabGPT+GeoTabGPT | 0.751 $\pm$ 0.001 | 0.682 $\pm$ 0.001 | 0.845 $\pm$ 0.004 | 0.700 $\pm$ 0.001 | 0.779 $\pm$ 0.004 |
| DialogMean+TrxTabBERT | 0.709 $\pm$ 0.004 | 0.668 $\pm$ 0.003 | 0.812 $\pm$ 0.008 | 0.642 $\pm$ 0.007 | 0.717 $\pm$ 0.006 |
| DialogMean+TrxTabBERT+GeoTabBERT | 0.718 $\pm$ 0.003 | 0.668 $\pm$ 0.003 | 0.826 $\pm$ 0.006 | 0.643 $\pm$ 0.007 | 0.734 $\pm$ 0.006 |
| GeoAggregation | 0.555 $\pm$ 0.001 | 0.539 $\pm$ 0.000 | 0.590 $\pm$ 0.002 | 0.533 $\pm$ 0.001 | 0.560 $\pm$ 0.001 |
| GeoCoLES | 0.598 $\pm$ 0.004 | 0.568 $\pm$ 0.003 | 0.663 $\pm$ 0.005 | 0.568 $\pm$ 0.007 | 0.593 $\pm$ 0.005 |
| GeoTabGPT | 0.621 $\pm$ 0.003 | 0.589 $\pm$ 0.002 | 0.696 $\pm$ 0.010 | 0.586 $\pm$ 0.002 | 0.614 $\pm$ 0.002 |
| GeoTabBERT | 0.603 $\pm$ 0.002 | 0.573 $\pm$ 0.003 | 0.672 $\pm$ 0.007 | 0.570 $\pm$ 0.004 | 0.598 $\pm$ 0.004 |
| TrxAggregation | 0.788 $\pm$ 0.001 | 0.743 $\pm$ 0.003 | 0.831 $\pm$ 0.002 | 0.777 $\pm$ 0.001 | 0.800 $\pm$ 0.002 |
| TrxAggregation+GeoAggregation | 0.778 $\pm$ 0.002 | 0.733 $\pm$ 0.002 | 0.822 $\pm$ 0.004 | 0.767 $\pm$ 0.001 | 0.790 $\pm$ 0.002 |
| TrxCoLES | 0.774 $\pm$ 0.002 | 0.734 $\pm$ 0.002 | 0.812 $\pm$ 0.004 | 0.759 $\pm$ 0.002 | 0.790 $\pm$ 0.003 |
| TrxCoLES+GeoCoLES | 0.775 $\pm$ 0.001 | 0.730 $\pm$ 0.002 | 0.827 $\pm$ 0.004 | 0.751 $\pm$ 0.003 | 0.792 $\pm$ 0.002 |
| TrxTabGPT | 0.802 $\pm$ 0.001 | 0.751 $\pm$ 0.001 | 0.844 $\pm$ 0.002 | 0.788 $\pm$ 0.002 | 0.826 $\pm$ 0.003 |
| TrxTabGPT+GeoTabGPT | 0.804 $\pm$ 0.001 | 0.748 $\pm$ 0.001 | 0.854 $\pm$ 0.002 | 0.784 $\pm$ 0.001 | 0.829 $\pm$ 0.003 |
| TrxTabBERT | 0.762 $\pm$ 0.004 | 0.717 $\pm$ 0.006 | 0.819 $\pm$ 0.004 | 0.734 $\pm$ 0.006 | 0.778 $\pm$ 0.006 |
| TrxTabBERT+GeoTabBERT | 0.766 $\pm$ 0.004 | 0.717 $\pm$ 0.006 | 0.831 $\pm$ 0.005 | 0.729 $\pm$ 0.007 | 0.786 $\pm$ 0.005 |

**Table 8: Late Fusion results on public dataset**
| methods | mean | target_1 | target_2 | target_3 | target_4 |
| --- | --- | --- | --- | --- | --- |
| DialogLast | 0.586 $\pm$ 0.001 | 0.602 $\pm$ 0.001 | 0.622 $\pm$ 0.004 | 0.554 $\pm$ 0.001 | 0.567 $\pm$ 0.002 |
| DialogLast+GeoAggregation | 0.646 $\pm$ 0.001 | 0.614 $\pm$ 0.001 | 0.659 $\pm$ 0.003 | 0.654 $\pm$ 0.001 | 0.657 $\pm$ 0.001 |
| DialogLast+GeoCoLES | 0.660 $\pm$ 0.001 | 0.633 $\pm$ 0.002 | 0.675 $\pm$ 0.004 | 0.661 $\pm$ 0.001 | 0.671 $\pm$ 0.003 |
| DialogLast+GeoTabGPT | 0.668 $\pm$ 0.001 | 0.645 $\pm$ 0.001 | 0.690 $\pm$ 0.004 | 0.662 $\pm$ 0.001 | 0.674 $\pm$ 0.002 |
| DialogLast+GeoTabBERT | 0.662 $\pm$ 0.001 | 0.633 $\pm$ 0.002 | 0.680 $\pm$ 0.003 | 0.662 $\pm$ 0.001 | 0.675 $\pm$ 0.002 |
| DialogLast+TrxAggregation | 0.792 $\pm$ 0.002 | 0.752 $\pm$ 0.001 | 0.829 $\pm$ 0.001 | 0.780 $\pm$ 0.001 | 0.805 $\pm$ 0.006 |
| DialogLast+TrxAggregation+GeoAggregation | 0.791 $\pm$ 0.001 | 0.750 $\pm$ 0.001 | 0.829 $\pm$ 0.005 | 0.782 $\pm$ 0.001 | 0.803 $\pm$ 0.001 |
| DialogLast+TrxCoLES | 0.782 $\pm$ 0.001 | 0.746 $\pm$ 0.003 | 0.814 $\pm$ 0.003 | 0.765 $\pm$ 0.002 | 0.802 $\pm$ 0.003 |
| DialogLast+TrxCoLES+GeoCoLES | 0.783 $\pm$ 0.001 | 0.745 $\pm$ 0.004 | 0.819 $\pm$ 0.003 | 0.767 $\pm$ 0.001 | 0.803 $\pm$ 0.002 |
| DialogLast+TrxTabGPT | 0.810 $\pm$ 0.001 | 0.779 $\pm$ 0.001 | 0.846 $\pm$ 0.003 | 0.789 $\pm$ 0.002 | 0.827 $\pm$ 0.004 |
| DialogLast+TrxTabGPT+GeoTabGPT | 0.808 $\pm$ 0.001 | 0.770 $\pm$ 0.001 | 0.849 $\pm$ 0.003 | 0.790 $\pm$ 0.001 | 0.824 $\pm$ 0.004 |
| DialogLast+TrxTabBERT | 0.773 $\pm$ 0.002 | 0.730 $\pm$ 0.006 | 0.822 $\pm$ 0.005 | 0.749 $\pm$ 0.004 | 0.792 $\pm$ 0.003 |
| DialogLast+TrxTabBERT+GeoTabBERT | 0.776 $\pm$ 0.003 | 0.729 $\pm$ 0.006 | 0.827 $\pm$ 0.004 | 0.752 $\pm$ 0.004 | 0.794 $\pm$ 0.003 |
| DialogMean | 0.595 $\pm$ 0.002 | 0.600 $\pm$ 0.001 | 0.633 $\pm$ 0.006 | 0.566 $\pm$ 0.001 | 0.580 $\pm$ 0.002 |
| DialogMean+GeoAggregation | 0.649 $\pm$ 0.001 | 0.614 $\pm$ 0.001 | 0.665 $\pm$ 0.002 | 0.656 $\pm$ 0.001 | 0.662 $\pm$ 0.001 |
| DialogMean+GeoCoLES | 0.663 $\pm$ 0.001 | 0.632 $\pm$ 0.002 | 0.680 $\pm$ 0.004 | 0.663 $\pm$ 0.000 | 0.675 $\pm$ 0.002 |
| DialogMean+GeoTabGPT | 0.670 $\pm$ 0.001 | 0.645 $\pm$ 0.000 | 0.694 $\pm$ 0.004 | 0.664 $\pm$ 0.001 | 0.678 $\pm$ 0.001 |
| DialogMean+GeoTabBERT | 0.664 $\pm$ 0.001 | 0.633 $\pm$ 0.001 | 0.682 $\pm$ 0.002 | 0.664 $\pm$ 0.001 | 0.678 $\pm$ 0.001 |
| DialogMean+TrxAggregation | 0.792 $\pm$ 0.002 | 0.752 $\pm$ 0.002 | 0.828 $\pm$ 0.002 | 0.781 $\pm$ 0.001 | 0.807 $\pm$ 0.006 |
| DialogMean+TrxAggregation+GeoAggregation | 0.792 $\pm$ 0.002 | 0.750 $\pm$ 0.002 | 0.829 $\pm$ 0.002 | 0.782 $\pm$ 0.001 | 0.807 $\pm$ 0.005 |
| DialogMean+TrxCoLES | 0.781 $\pm$ 0.001 | 0.745 $\pm$ 0.003 | 0.814 $\pm$ 0.002 | 0.765 $\pm$ 0.002 | 0.802 $\pm$ 0.003 |
| DialogMean+TrxCoLES+GeoCoLES | 0.783 $\pm$ 0.001 | 0.744 $\pm$ 0.004 | 0.819 $\pm$ 0.003 | 0.767 $\pm$ 0.002 | 0.802 $\pm$ 0.002 |
| DialogMean+TrxTabGPT | 0.810 $\pm$ 0.002 | 0.779 $\pm$ 0.001 | 0.847 $\pm$ 0.004 | 0.789 $\pm$ 0.001 | 0.828 $\pm$ 0.003 |
| DialogMean+TrxTabGPT+GeoTabGPT | 0.808 $\pm$ 0.001 | 0.770 $\pm$ 0.001 | 0.848 $\pm$ 0.003 | 0.790 $\pm$ 0.001 | 0.826 $\pm$ 0.003 |
| DialogMean+TrxTabBERT | 0.773 $\pm$ 0.003 | 0.730 $\pm$ 0.006 | 0.822 $\pm$ 0.004 | 0.749 $\pm$ 0.004 | 0.791 $\pm$ 0.003 |
| DialogMean+TrxTabBERT+GeoTabBERT | 0.775 $\pm$ 0.003 | 0.728 $\pm$ 0.005 | 0.827 $\pm$ 0.003 | 0.752 $\pm$ 0.004 | 0.794 $\pm$ 0.004 |
| GeoAggregation | 0.555 $\pm$ 0.001 | 0.539 $\pm$ 0.000 | 0.590 $\pm$ 0.002 | 0.533 $\pm$ 0.001 | 0.560 $\pm$ 0.001 |
| GeoCoLES | 0.598 $\pm$ 0.004 | 0.568 $\pm$ 0.003 | 0.663 $\pm$ 0.005 | 0.568 $\pm$ 0.007 | 0.593 $\pm$ 0.005 |
| GeoTabGPT | 0.621 $\pm$ 0.003 | 0.589 $\pm$ 0.002 | 0.696 $\pm$ 0.010 | 0.586 $\pm$ 0.002 | 0.614 $\pm$ 0.002 |
| GeoTabBERT | 0.603 $\pm$ 0.002 | 0.573 $\pm$ 0.003 | 0.672 $\pm$ 0.007 | 0.570 $\pm$ 0.004 | 0.598 $\pm$ 0.004 |
| TrxAggregation | 0.783 $\pm$ 0.002 | 0.741 $\pm$ 0.003 | 0.828 $\pm$ 0.003 | 0.770 $\pm$ 0.004 | 0.792 $\pm$ 0.007 |
| TrxAggregation+GeoAggregation | 0.783 $\pm$ 0.002 | 0.740 $\pm$ 0.002 | 0.829 $\pm$ 0.003 | 0.771 $\pm$ 0.001 | 0.792 $\pm$ 0.011 |
| TrxCoLES | 0.773 $\pm$ 0.002 | 0.734 $\pm$ 0.002 | 0.812 $\pm$ 0.004 | 0.758 $\pm$ 0.002 | 0.790 $\pm$ 0.003 |
| TrxCoLES+GeoCoLES | 0.775 $\pm$ 0.002 | 0.734 $\pm$ 0.002 | 0.815 $\pm$ 0.004 | 0.760 $\pm$ 0.002 | 0.789 $\pm$ 0.003 |
| TrxTabGPT | 0.802 $\pm$ 0.001 | 0.751 $\pm$ 0.001 | 0.844 $\pm$ 0.002 | 0.787 $\pm$ 0.001 | 0.825 $\pm$ 0.003 |
| TrxTabGPT+GeoTabGPT | 0.800 $\pm$ 0.001 | 0.752 $\pm$ 0.001 | 0.846 $\pm$ 0.005 | 0.785 $\pm$ 0.002 | 0.817 $\pm$ 0.006 |
| TrxTabBERT | 0.762 $\pm$ 0.004 | 0.717 $\pm$ 0.006 | 0.819 $\pm$ 0.004 | 0.734 $\pm$ 0.006 | 0.777 $\pm$ 0.006 |
| TrxTabBERT+GeoTabBERT | 0.764 $\pm$ 0.004 | 0.716 $\pm$ 0.006 | 0.823 $\pm$ 0.004 | 0.737 $\pm$ 0.006 | 0.780 $\pm$ 0.005 |

**Table 9: Multimodal matching results**
|  | Recall@1 | Recall@50 | Recall@100 |
| --- | --- | --- | --- |
| Trx2Geo | 0.006 $\pm$ 0.0003 | 0.1967 $\pm$ 0.002 | 0.303 $\pm$ 0.004 |
| Geo2Trx | 0.004 $\pm$ 0.0003 | 0.1624 $\pm$ 0.002 | 0.262 $\pm$ 0.004 |
| Trx2Dial | 0.00004 $\pm$ 0.00001 | 0.0001 $\pm$ 0.00005 | 0.003 $\pm$ 0.0001 |
| Dial2Trx | 0.00003 $\pm$ 0.00001 | 0.001 $\pm$ 0.00005 | 0.003 $\pm$ 0.0001 |
| Dial2Geo | 0.00002 $\pm$ 0.000001 | 0.0006 $\pm$ 0.00001 | 0.001 $\pm$ 0.0001 |
| Geo2Dial | 0.00002 $\pm$ 0.000001 | 0.0005 $\pm$ 0.00001 | 0.001 $\pm$ 0.0001 |

Figure: Figure 7: Model performance comparison on private and public data. Kendall-Tau=0.94
Refer to caption: x5.png

Figure: Figure 8: Example data of client’s purchases.
Refer to caption: extracted/5880953/pictures/sample_data/Task.png

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Is there a plan to expand the set of downstream tasks in future work? Highlighting a larger application list can increase the dataset's appeal across different financial research areas.

Please look into the weakness section for other questions.
soundness: 3
strengths: 1. The work releases a first large scale banking dataset for public availability for financial applications. 
2. The authors present a good benchmark comparing unimodal and multimodal methods across various predictive tasks. The experimental protocol and metrics are clearly laid out as well.
summary: 1. The paper introduces a new dataset, Multimodal Banking Dataset which integrates multiple modalities for over 2 million corporate clients.
2. The authors highlight the potential applications of this dataset for campaign planning and client behavior analysis, using multimodal benchmarks to demonstrate its value along with baseline model implementation.
weaknesses: 1. The authors do not explore or discuss advanced multimodal sequence models or advanced fusion techniques' cross-attention mechanisms as they can better capture interactions across modalities. They mention it at the end as a scope for future work.
2. Though authors discuss using AUC ROC as their metric for mitigating label imbalance issues for example in their campaigning downstream task, they do not discuss or incorporate any additional techniques for handling the label imbalance.
3. Details about anonymization techniques applied are mentioned in the paper but it lacks quantitative evaluation of the impact of these techniques on temporal dependencies within the data.

---

confidence: 4
contribution: 4
initial_rating: 8
presentation: 2
questions: (1) Will the dataset be openly downloadable or will the authors be controlling the access? Openly downloadable option is obviously preferable.
(2) Will the source code of the bench-marking studies be openly available? 
(3) it will be useful to the community if the set of relevant real-world problems could be articulated that can inspire researchers to work on this dataset. Especially, in order to attract young researchers into the field.
soundness: 4
strengths: (1) This will be first and the largest multimodal banking dataset that will be released. This can potentially be tremendously useful to the research community.

(2) The baseline methods and benchmark data for a few problems outlined will also be immensely useful to the research community.
summary: This is not a research contribution per se but it is a dataset paper.

The paper presents the first large-scale multimodal banking dataset for the user community. It offers a new dataset that has millions of users and millions of transactions that have been suitably anonymized. Moreover, the authors have provided baselines for a few standard tasks. The dataset will be released in order to spur research in the use of machine learning for banking applications.
weaknesses: (1) The details of the data are somewhat sparse. More details of each type of data will be useful to the reader. Perhaps this article may be useful for improving this aspect of the exposition in the paper: 

https://cacm.acm.org/research/datasheets-for-datasets/

(2)  Can some details of the anonymization be provided without compromising on the privacy of the customers? That can help estimate the errors of any model developed using this data.

(3) The data has been collected during the pandemic period? Will it have any effect on any of the conclusions drawn using the data? For examples, could this lead to systemic under-estimation or over-estimation of any phenomenon? Ideally, it would be useful to have another data-set which is outside of the pandemic period - perhaps the second version of this data set? This can serve as a basis for many natural experiments.

---

confidence: 5
contribution: 1
initial_rating: 3
presentation: 1
questions: 1. Do you have any specific method to model the different text modalities?
2.The paper contains numerous tense and grammatical errors, which do not meet the standards expected for ICLR.
soundness: 1
strengths: 1.A large-scale multimodal banking dataset, MBD, is provided. This dataset contains anonymized banking transactions, geographic locations, and technical support dialogues, which contributes to the development of large-scale sequential event tasks in the future.
2.The dataset addresses privacy concerns through effective data anonymization, ensuring that the algorithm's performance is not significantly compromised.
3.The dataset and experimental code are publicly available, promoting transparency and reproducibility in research.
summary: This paper introduces a large-scale publicly available multimodal banking dataset (Multimodal Banking Dataset, MBD). The MBD contains data from over 2 million banking customers, covering four different modalities: 9.5 million banking transaction records, 1 billion geographic location data points, 5 million customer interactions with technical support embedded dialogues, and banking product purchase activity over a 12-month period. This dataset provides researchers with a rich resource for analyzing customer behavior dynamics and contributes to the development of large-scale multimodal event sequence algorithms in the future.
weaknesses: 1.The dataset's multimodal data includes banking transaction records, geographic locations, dialogue embeddings, and banking product purchase history. However, it appears that many of these modalities are essentially text-based. This differs from typical multimodal datasets, which include modalities such as video, audio, and text. 

2.The main contribution of the paper lies in the introduction of a large-scale dataset, but it lacks innovative methods for addressing related tasks. Additionally, the experimental section presents insufficient comparisons with current state-of-the-art methods. Overall, the paper's innovativeness needs improvement. I suggest that the authors include more experiments involving fully supervised methods [1][2]. Additionally, it would be beneficial to propose a simple and practical innovative approach based on their dataset.
[1]Learning Deep Time-index Models for Time Series Forecasting. [2]Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting

3.This paper contains numerous grammatical and tense issues. Additionally, the expression in the paper is not sufficiently clear, making it difficult for readers to accurately understand certain points and arguments. For example, on page three, phrases like “we selected” and “the dataset was collected”; on page five, “the coordinates were”; line 225 includes “we concentrated,” “whether it was,” and “random noise was added.” Additionally, on line 295, “we propose a downstream task — multimodal matching” (Zong et al., 2023).

---

confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: -  The paper could be strengthened by exploring more advanced multimodal fusion techniques beyond late fusion.
-  A more detailed analysis of the anonymization process and its potential impact on model performance would be beneficial.
-  The authors could consider expanding the benchmark to include other relevant tasks, such as risk assessment or fraud detection.

Overall, while the MBD dataset and benchmark are valuable contributions, the paper needs significant revisions to address the lack of novelty and provide a more convincing argument for the practical impact of their work.
soundness: 2
strengths: - Large-scale dataset: The MBD dataset is the largest of its kind, offering a significant amount of data for research purposes.   

- Multimodality: The dataset incorporates various data modalities, providing a more comprehensive view of client behavior.   

- Practical tasks: The benchmark focuses on practically relevant tasks, such as purchase prediction, which can be useful for real-world applications.   

- Anonymization: The authors have taken steps to anonymize the data, protecting client privacy.
summary: The paper introduces a new multimodal dataset, MBD, from over 2 million corporate clients, including bank transactions, geo-locations, technical support dialogues, and monthly aggregated purchases of four banking products. The authors also present a benchmark for evaluating models on this dataset and two other financial datasets, focusing on tasks like purchase prediction and multimodal matching.
weaknesses: - Lack of novelty: The paper primarily focuses on introducing a dataset and benchmark. For ICLR submissions, I'd see more emphasis on novel methods or algorithms. In addition, a careful evaluation of previous models (for example, the MMBench https://arxiv.org/abs/2307.06281) will bring more novelty to the community. 

- Missing comparison with LLMs: The paper lacks a comparison with more recent and powerful language models like GPT-4 or BloombergGPT, which have shown strong performance in various financial NLP tasks. For example, can we use prompt engineering to guide the GPT4o to process the transactions and geo-locations. 

- Unclear practical impact of the proposed metric: While the paper mentions that the benchmark can lead to financial benefits, I am curious if real world users care recalls/AUCs, or there are better metrics that map to financial success.

Meta-review: # Summary and Recommendation for Rejection

---

## Strengths:
1. **Dataset Scale and Scope**:  
   - The authors present the first large-scale, multimodal banking dataset (MBD) covering over 2 million corporate clients.  
   - Data sources include bank transactions, geolocation data, customer support dialogues, and aggregated purchases.  

2. **Benchmarking Contribution**:  
   - Includes baseline evaluations comparing unimodal and multimodal methods, showcasing the advantages of multimodal approaches for financial tasks.

3. **Public Availability and Reproducibility**:  
   - The dataset and experimental code are promised to be made publicly available, promoting transparency and fostering further research.

4. **Privacy Measures**:  
   - Anonymization techniques are applied to protect user data while retaining significant information for downstream tasks.

---

## Weaknesses:
1. **Lack of Novelty**:  
   - The paper primarily focuses on dataset creation and lacks significant methodological innovation.  
   - For ICLR, novel methods tailored to the dataset are expected but are absent here.

2. **Sparse Details on Anonymization**:  
   - While anonymization is discussed, there is insufficient quantitative evaluation of its impact on preserving temporal dependencies.  
   - This raises concerns about data reliability, especially for fine-grained tasks like fraud detection.

3. **Limited Methodological Exploration**:  
   - Advanced multimodal fusion techniques, such as cross-attention mechanisms, are not thoroughly explored.  
   - Comparisons with state-of-the-art methods, including large language models (LLMs), are limited.

4. **Repetitive Modality Representations**:  
   - The modalities are primarily tabular or text-based, limiting the diversity seen in standard multimodal datasets (e.g., including audio or video).  

5. **Presentation Issues**:  
   - The manuscript contains grammatical errors, unclear expressions, and inconsistent tense usage, detracting from readability.

---

## Authors' Mitigation:
1. **Advanced Fusion Techniques**:  
   - The authors added preliminary results using early fusion techniques (e.g., CrossTransformer), but these showed no significant performance improvement.

2. **Expanded Benchmarking**:  
   - Efforts to address label imbalance (e.g., stratified batching, oversampling) were included, but metric improvements were marginal.

3. **Anonymization Impact**:  
   - Claims of minimal performance differences between anonymized and original datasets are made, but quantitative evaluations for complex tasks are missing.

4. **Future Dataset Enhancements**:  
   - The authors promise to expand the dataset with new modalities and tasks in future versions but do not address current limitations.

---

## Remaining Weaknesses:
1. **Insufficient Novelty**:  
   - The authors fail to provide novel frameworks or techniques tailored to the dataset.

2. **Unsubstantiated Claims**:  
   - Assertions regarding anonymization and dataset reliability are not adequately supported by experiments or results.

3. **Limited Practicality**:  
   - Metrics like AUC are used as proxies for campaign success, but their real-world implications for financial decision-making are not validated.

4. **Incomplete Benchmarks**:  
   - Comparisons with modern multimodal learning approaches (e.g., MMBench) and integration with LLMs (e.g., GPT-4 or BloombergGPT) are insufficient.

---

## Decision Justification:
While the dataset's scale and intent are commendable, the paper falls short in several critical areas:

1. **Scope Misalignment**:  
   - The focus on dataset release, with minimal methodological contributions, does not align with ICLR's emphasis on innovation.

2. **Reliability Concerns**:  
   - The anonymized dataset's adequacy for sensitive tasks like fraud detection remains questionable.

3. **Limited Community Impact**:  
   - Without novel methods or robust comparative analyses, the paper's contribution is constrained to the dataset.

4. **Quality Issues**:  
   - Presentation flaws and lack of clarity weaken the submission.

---

## Recommendation:
**Reject**.   While the dataset provides a strong foundation for future research, significant revisions and expansions are necessary to meet the standards of a premier conference like ICLR.

Decision: Reject