Adversarial Attacks on Deep Models for Financial Transaction Records

Fursov, Ivan; Morozov, Matvey; Kaploukhaya, Nina; Kovtun, Elizaveta; Rivera-Castro, Rodrigo; Gusev, Gleb; Babaev, Dmitry; Kireev, I.M.; Zaytsev, Alexey; Burnaev, Evgeny

doi:10.48550/arxiv.2106.08361

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The validation data are later in time, than the training data. The open dataset [23] consists of the history of transactions of bank clients for one year and three months. Information about day of week for these transactions is available for each day.…”

Section: A Data Structurementioning

confidence: 99%

Bank transactions embeddings help to uncover current macroeconomics

Begicheva¹,

Zaytsev²

2021

Preprint

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Macroeconomic indexes are of high importance for banks: many risk-control decisions utilize these indexes. A typical workflow of these indexes evaluation is costly and protracted, with a lag between the actual date and available index being a couple of months. Banks predict such indexes now using autoregressive models to make decisions in a rapidly changing environment. However, autoregressive models fail in complex scenarios related to appearances of crises.We propose to use clients' financial transactions data from a large Russian bank to get such indexes. Financial transactions are long, and a number of clients is huge, so we develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions consisting of millions of transactions. The approach uses a neural networks paradigm and a smart sampling scheme.The results show that our neural network approach outperforms the baseline method on hand-crafted features based on transactions. Calculated embeddings show the correlation between the client's transaction activity and bank macroeconomic indexes over time.

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Section: A Data Structurementioning

confidence: 99%

Bank transactions embeddings help to uncover current macroeconomics

Begicheva¹,

Zaytsev²

2021

Preprint

Self Cite

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“…The current paper concentrates on binary and multiclass classification and on the image processing domain. However, the presented algorithms may also be applicable to other domains in which attacks may occur, such as banking and finance [16], cyber security [17], automatic speech recognition [18], or medical machine learning [19].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics

Brama,

Dery,

Grinshpoun

2022

Preprint

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The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relies on traditional classification metrics, without adequate adaptation to adversarial scenarios. Most of these metrics are accuracy-based, and therefore may have a limited scope and low distinctive power. Other metrics do not consider the unique characteristics of neural networks functionality, or measure the effect of the attacks indirectly (e.g., through the complexity of their generation). In this paper, we present two metrics which are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in multiclass classification tasks. Inspired by the normalized discounted cumulative gain and the reciprocal rank metrics used in information retrieval literature, we treat the neural network predictions as ranked lists of results. Using additional information about the probability of the rank enabled us to define novel metrics that are suited to the task at hand. We evaluate our metrics using various attacks and defenses on a pretrained VGG19 model and the ImageNet dataset. Compared to the common classification metrics, our proposed metrics demonstrate superior informativeness and distinctiveness.

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Adversarial Attacks on Deep Models for Financial Transaction Records

Cited by 2 publications

References 19 publications

Bank transactions embeddings help to uncover current macroeconomics

Bank transactions embeddings help to uncover current macroeconomics

Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics

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