VaBank — Visual Analytics for Banking Transactions

To analyse and detect fraudulent patterns in banking transactions, most fraud analysts use spreadsheets which makes the overall process time-consuming and complex. In this project, we propose a visualization tool that aims to ease the analysis of banking transactions over time and the detection of the transactions’ topology and of suspicious behaviours. Our main contributions are: (i) a user-centred visual tool, developed with the aid of fraud experts; (ii) a method that characterises the transactions topology through a self-organising algorithm; (iii) the visual characterisation of transactions through complex glyphs; and (iv) a user study to assess the tool effectiveness.


The tool


Figure 1

Transaction History view and its components.


VaBank is divided into three views: the transaction history, the transactions topology, and the transactions relations. The first arranges all transactions by time and amount. The last two display the results of a SOM algorithm in a grid and through a force-directed graph, respectively. All views have access to a GUI panel (Fig.1, A).


In the Transaction History view, the main representation, which occupies more canvas space, is the the transaction matrix (Fig.1, B). It divides the space in different ranges of amounts on the y-axis and temporal values on the x-axis. The transactions, represented as glyphs according to their characteristics (Fig.2) are then distributed by the cells of the matrix, according to their date and amount. If more than one transaction with the same characteristics occur within the same cell, they are aggregated and its glyph grows in size.


Figure 2

Glyph elements that characterise each transaction (side A) and timeline bar composition and respective colour ranges (side B)


In the bottom and right sides of the matrix, histograms are drawn to show the total number of transactions per column and row, respectively (Fig.1, B and C). The histogram’s bars are coloured according to the number of transactions: the darker, the higher the number of transactions. By hovering each bar, the total number of transactions is shown. In the bottom right corner of the matrix area, we draw a small matrix of glyphs that represents the result of a SOM algorithm, concerning: amount, transaction type, and fraud (Fig.1, C). With this, we aim to enhance the understanding of typical/atypical transactions.


In the bottom, we placed an interactive timeline, so the user can select and visualise different periods of time in the data (Fig.1, D). The different time periods are defined by a hierarchical aggregation algorithm.


Transactions Topology


Figure 3

Projections of the SOM results for the same bank client through the matrix projection (left) and force-directed graph (right).


We applied a SOM algorithm to enhance the detection of atypical transactions, which can be related to fraudulent behaviours (Fig.3). The results of the SOM are visualised through a matrix or a force-directed projections. The goal of the first (Fig.3, left) is to represent the distribution of different types of transactions present in the data and extrapolate at a higher level the characteristics of the dataset. The goal of the second (Fig.3, right) is to express the relations among clustered data and emphasise the most typical transaction, allowing a more detailed analysis of the dataset.

To be published in
IV2020 – 24th International Conference Information Visualisation