VIS4AUI: VISUAL ANALYSIS OF BANKING ACTIVITY

NETWORKS

∗

Walter Didimo, Giuseppe Liotta and Fabrizio Montecchiani

D.I.E.I., Universit

a degli Studi di Perugia, Via Duranti 93, Perugia, Italy

Keywords:

Information Visualization, Network Analysis, Graph Drawing, Clustering, Social Network Analysis, Multi-

touch User Interfaces.

Abstract:

We present the system VIS4AUI, aimed at supporting the analyst to discover ﬁnancial crimes related to money

laundering. An anti-money laundering archive collects ﬁnancial information with regard to ongoing bank re-

lationships and high value transactions. VIS4AUI is able to import and analyze the Italian anti-money laun-

dering archive (AUI) in order to visualize the banking activity networks arising from it. In the demonstration,

the user will be given an evidence of a possible suspicious person or company; starting from such a seed

entity, the task will be that of exploring and analyzing her network of transactions through the tools provided

by the system. In order to immerse the user in a fully interactive experience, VIS4AUI is a touch-optimized

application, only requiring a touchscreen as interface.

1 INTRODUCTION

Money laundering is a well-known kind of ﬁnancial

crime based on relevant volumes of transactions to

conceal the identity, the source, or the destination of

illegally gained money. These transactions are con-

ceived to give the illegally gained capitals a licit sem-

blance, making their origin difﬁcult to identify. To

face this problem, most governments have created

special investigation agencies, called Financial Intel-

ligence Units (FIUs), whose main objectives are to

defend the integrity of worldwide ﬁnancial markets

and to prevent them from organized crimes that could

undermine the homeland security.

VIS4AUI is a system started from a proof of

concept implementation described in (Didimo et al.,

2011), and successively engineered by the academic

spin-off Vis4

, thanks to a close collaboration with

the FIU of the Republic of San Marino (AIF - Agenzia

di Informazione Finanziaria)

. VIS4AUI can import

and analyze the anti-money laundering archive col-

lected by Italian banks, named AUI (Archivio Unico

Informatico). The AUI archive holds ﬁnancial data

with regard to ongoing bank relationships and trans-

actions involving amounts exceeding EUR 15,000.00.

∗

Thanks to the programming team of Vis4 for the great

job done in the last year.

http://www.vis4you.com/

http://www.aif.sm

These data can be modelled as social networks whose

nodes represent persons and companies and whose

links represent their relationships, see Figure 1. It

is widely accepted that the exploration of such net-

works to discover criminal patterns strongly bene-

ﬁts from a strict integration of social network anal-

ysis (SNA) and visualization tools (Didimo and Li-

otta, 2007; Tang et al., 2010; Westphal, 2009; Xu and

Chen, 2005).

2 THE SYSTEM VIS4AUI

In order to collect the system requirements we coop-

erated with analysts of the Republic of San Marino

AIF. One important issue is that the system is not re-

quired to discover criminal patterns by itself, but it is

mainly intended by the analyst as a strong support for

the investigation activity. For this reason, the system

must provide strong interaction, conceived for semi-

automatic solutions.

The development of advanced methodologies and

software systems for the analysis of criminal net-

works has received increasing attention after the

September 11 terrorist attacks, see, e.g., (Chang et

al., 2008; Tang et al., 2010; Klerks and Smeets, 2001;

Goldberg and Senator, 1995; Chen et al., 2005; Stasko

et al., 2008). A survey on these systems is presented

by Xu and Chen (Xu and Chen, 2005).

799

Didimo W., Liotta G. and Montecchiani F..

VIS4AUI: VISUAL ANALYSIS OF BANKING ACTIVITY NETWORKS.

DOI: 10.5220/0003933407990802

In Proceedings of the International Conference on Computer Graphics Theory and Applications (IVAPP-2012), pages 799-802

ISBN: 978-989-8565-02-0

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: A bank activity network drawn by VIS4AUI. The near window shows some details about a subset of transactions

involved in the network.

In the remainder of this section we summarize the

main features of VIS4AUI; see (Didimo et al., 2011)

for further details on the algorithmic aspects of the

system.

2.1 Exploring and Analyzing the

Network

The ﬁnancial networks to be analyzed are automati-

cally drawn by the system using a customized force-

directed algorithm, which is able to handle position

constraints and clusters. We recall that force-directed

algorithms have been introduced by Eades (Eades,

1984) and received a lot of attention both in the graph

visualization literature and in the implementation of

industrial software, see, e.g., (Fruchterman and Rein-

gold, 1991; Chen et al., 2005). The basic idea of a

force-directed algorithm is to model the network as a

physical system: Different kinds of forces are exerted

on each node, and the ﬁnal placement of all nodes

in the visualization will correspond to an equilibrium

status of the physical system.

In VIS4AUI, the visual exploration of the ﬁnan-

cial networks is supported by a combination of a

bottom-up and of a top-down interaction paradigm.

With the former one the analyst can start from a sem-

inal node and iteratively enhance the network with

new elements by adding neighbours of the displayed

nodes. With the latter one the analyst can use hier-

archical clustering to recursively aggregate elements

on the whole network currently displayed. Each clus-

ter can be collapsed or expanded independently at any

time, so that the analyst can simplify the visual infor-

mation at her convenience. These two kinds of inter-

action paradigms can be alternated while maintaining

consistency.

Moreover, VIS4AUI makes it possible to mix au-

tomatic and manual clustering, and is equipped with

several tools for social network analysis other than

clustering, like different types of indices to measure

the centrality of each actor in the network.

Finally, the intelligence process is also supported

by a powerful saving system, which allows the user to

revert to any previous save and to create a new branch

of saves on the time-line (see Figure 2).

Figure 2: A tree of saves related to an investigation.

2.2 Interface and Interaction

Here we describe the interface and interaction of

VIS4AUI in more details. The user interface of

VIS4AUI is touch-optimized, hence every interac-

tion described below can be performed using either

a mouse or a touchscreen.

The analyst can start a new investigation from a

desired seed entity (e.g., a person or a company),

IVAPP 2012 - International Conference on Information Visualization Theory and Applications

800

Figure 3: The main window of VIS4AUI.

searching in the database accessed by VIS4AUI. At

the beginning only the seed entity and its adjacent

nodes are displayed. The analyst can then explore

and elaborate on the network with different interac-

tive tools:

Bottom-up Exploration. The set of nodes and

edges can be incrementally enhanced by exploring

some of the displayed entities. By double clicking or

double tapping a node, all of its neighbours and their

connections are added to the current network, if not

already shown. A new layout is automatically com-

puted according to the status of the previous network,

which depends on the presence of cluster regions or

other types of geometric constraints. If the previous

drawing contained a hierarchy of cluster regions, ev-

ery node that enters in the new network is automati-

cally assigned to a suitable cluster region according to

a criterion aimed at keeping the coherence of the clus-

tering. A small green box over a node indicates that

such a node has not been explored yet; this avoids the

analyst to repeat the same exploration action twice.

Top-down Exploration. The analyst can ask the

system to automatically compute a cluster hierarchy

on the current network. This action will group nodes

into clusters and sub-clusters according to some spe-

ciﬁc algorithm. Our current clustering algorithm ex-

ploits the concept of k-core, which has been proven to

be effective for discovering relevant groups in social

networks, see, e.g., (Batagelj et al., 1999; Dorogovt-

sev et al., 2005; Goltsev et al., 2006; Seidman, 1983).

However, different types of clustering algorithms can

be easily plugged in the system. Once a cluster hi-

erarchy has been computed, the system decides the

initial dimensions of each cluster region based on the

number of nodes inside it. In the layout, the boundary

of each cluster region is displayed as a rectangle. To

help the analyst in capturing the structure of the clus-

ter hierarchy, the corresponding cluster inclusion tree

is also displayed on the left-hand side of the interface.

VIS4AUI offers various interaction facilities with the

clusters and their regions. The analyst can drag nodes

inside or outside a cluster region, so modifying its

associated cluster. She can move a cluster inside or

outside another cluster, so modifying the cluster hi-

erarchy. She can create new clusters or delete some

of the existing ones. She can collapse or expand a

cluster region, so to hide/show its interior. The draw-

ing algorithm will react at each user’s change in order

to rearrange the layout. The analyst can also resize

each cluster region at her convenience with the same

kind of interaction used to resize a window in a classi-

cal operating system graphical user interface, that is,

dragging the boundary of the cluster. Resizing clus-

ters acts as a focus+context technique with multiple

foci.

Node Centrality. VIS4AUI implements a wide

range of indices for measuring the centrality of a

node, like betweenness, closeness and degree. The

value of a speciﬁc type of index is conveyed in the

layout by displaying a small disk near to its associated

node, the color of the disk reﬂect the value in a scale

from white to black. The analysts can quickly switch

from the visualization of a type of index to another

and all indices are normalized so that they can be eas-

VIS4AUI: VISUAL ANALYSIS OF BANKING ACTIVITY NETWORKS

801

Figure 4: A clustered network with one collapsed cluster.

ily compared. We remark that centrality indices make

sense only for nodes representing the actors of the net-

work, like persons, companies, and banks. Hence, in

order to compute the centrality of these actors, we run

the algorithm on a different suitable network consist-

ing only of actors. Namely, we add an edge between

two actors if the length of the shortest path between

them is at most d (for a pre-set constant d), and then

we remove all nodes that are not actors.

3 DEMO PROPOSAL

The database of the system will be loaded with an

anonymized anti-money laundering archive. In the

demonstration, the user will be given an evidence of a

possible suspicious person or company. An evidence

can be for example a set of suspicious transactions

made by a person or by a company. Starting from this

seed entity, the task will be that of exploring and ana-

lyzing the related network using the tools provided in

the system. User’s impressions and comments will be

recorded by means of an interview.

Figure 5: A user interacting with VIS4AUI by a touch-

screen.

REFERENCES

Batagelj, V., Mrvar, A., and Zaver

snik, M. (1999). Par-

titioning approach to visualization of large networks.

In GD 1999, volume 1731 of LNCS, pages 90–97.

Chang et al., R. (2008). Scalable and interactive visual anal-

ysis of ﬁnancal wire transactions for fraud detection.

Information Visualization, 7(1):63–76.

Chen, H., Atabakhsh, H., Tseng, C., Marshall, B., Kaza,

S., Eggers, S., Gowda, H., Shah, A., Petersen, T., and

Violette, C. (2005). Visualization in law enforcement.

In CHI, pages 1268–1271. ACM.

Didimo, W. and Liotta, G. (2007). Mining Graph Data,

chapter Graph Visualization and Data Mining, pages

35–64. Wiley.

Didimo, W., Liotta, G., Montecchiani, F., and Palladino, P.

(2011). An advanced network visualization system for

ﬁnancial crime detection. In PaciﬁcVis, pages 203–

210. IEEE.

Dorogovtsev, S. N., Goltsev, A. V., and Mendes, J. F. F.

(2005). k-core organization of complex networks.

CoRR, abs/cond-mat/0509102.

Eades, P. (1984). A heuristic for graph drawing. In Con-

gressus Numerantium, volume 42, pages 149–160.

Fruchterman, T. M. J. and Reingold, E. M. (1991). Graph

drawing by force-directed placement. Softw., Pract.

Exper., 21(11):1129–1164.

Goldberg, H. G. and Senator, T. E. (1995). Restructuring

databases for knowledge discovery by consolidation

and link formation. In Proceedings of the First In-

ternational Conference on Knowledge Discovery and

Data Mining, pages 136–141. AAAI Press.

Goltsev, A. V., Dorogovtsev, S. N., and Mendes, J. F. F.

(2006). k-core (bootstrap) percolation on complex

networks: Critical phenomena and nonlocal effects.

CoRR, abs/cond-mat/0602611.

Klerks, P. and Smeets, E. (2001). The network paradigm ap-

plied to criminal organizations: Theoretical nitpicking

or a relevant doctrine for investigators? Recent devel-

opments in the netherlands. Connections, 24:53–65.

Seidman, S. B. (1983). Network structure and minimum

degree. Social Networks, 5:269–287.

Stasko, J., G

org, C., and Liu, Z. (2008). Jigsaw: support-

ing investigative analysis through interactive visual-

ization. Information Visualization, 7:118–132.

Tang, L., Barbier, G., Liu, H., and Zhang, J. (2010). A so-

cial network analysis approach to detecting suspicious

online ﬁnancial activities. In Advances in Social Com-

puting, LNCS, pages 390–397.

Westphal, C. (2009). Data Mining for Intelligence, Fraud,

& Criminal Detection. CRC Press.

Xu, J. and Chen, H. (2005). Criminal network analysis and

visualization. Comm. of the ACM, 48(6):101–107.

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