Fraud detection in the banking sector : a multi-agent approach
Buoni, Alessandro (2012-11-08)
Turku Centre for Computer Science (TUCS)
08.11.2012
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https://urn.fi/URN:ISBN:978-952-12-2801-8
https://urn.fi/URN:ISBN:978-952-12-2801-8
Tiivistelmä
Fraud is an increasing phenomenon as shown in many surveys carried out
by leading international consulting companies in the last years. Despite
the evolution of electronic payments and hacking techniques there is still a
strong human component in fraud schemes.
Conflict of interest in particular is the main contributing factor to the
success of internal fraud.
In such cases anomaly detection tools are not always the best instruments,
since the fraud schemes are based on faking documents in a context
dominated by lack of controls, and the perpetrators are those ones who
should control possible irregularities.
In the banking sector audit team experts can count only on their experience,
whistle blowing and the reports sent by their inspectors.
The Fraud Interactive Decision Expert System (FIDES), which is the
core of this research, is a multi-agent system built to support auditors in
evaluating suspicious behaviours and to speed up the evaluation process in
order to detect or prevent fraud schemes. The system combines Think-map,
Delphi method and Attack trees and it has been built around audit team
experts and their needs.
The output of FIDES is an attack tree, a tree-based diagram to ”systematically
categorize the different ways in which a system can be attacked”.
Once the attack tree is built, auditors can choose the path they perceive as
more suitable and decide whether or not to start the investigation.
The system is meant for use in the future to retrieve old cases in order
to match them with new ones and find similarities.
The retrieving features of the system will be useful to simplify the risk
management phase, since similar countermeasures adopted for past cases
might be useful for present ones.
Even though FIDES has been built with the banking sector in mind, it
can be applied in all those organisations, like insurance companies or public
organizations, where anti-fraud activity is based on a central anti-fraud unit
and a reporting system.
by leading international consulting companies in the last years. Despite
the evolution of electronic payments and hacking techniques there is still a
strong human component in fraud schemes.
Conflict of interest in particular is the main contributing factor to the
success of internal fraud.
In such cases anomaly detection tools are not always the best instruments,
since the fraud schemes are based on faking documents in a context
dominated by lack of controls, and the perpetrators are those ones who
should control possible irregularities.
In the banking sector audit team experts can count only on their experience,
whistle blowing and the reports sent by their inspectors.
The Fraud Interactive Decision Expert System (FIDES), which is the
core of this research, is a multi-agent system built to support auditors in
evaluating suspicious behaviours and to speed up the evaluation process in
order to detect or prevent fraud schemes. The system combines Think-map,
Delphi method and Attack trees and it has been built around audit team
experts and their needs.
The output of FIDES is an attack tree, a tree-based diagram to ”systematically
categorize the different ways in which a system can be attacked”.
Once the attack tree is built, auditors can choose the path they perceive as
more suitable and decide whether or not to start the investigation.
The system is meant for use in the future to retrieve old cases in order
to match them with new ones and find similarities.
The retrieving features of the system will be useful to simplify the risk
management phase, since similar countermeasures adopted for past cases
might be useful for present ones.
Even though FIDES has been built with the banking sector in mind, it
can be applied in all those organisations, like insurance companies or public
organizations, where anti-fraud activity is based on a central anti-fraud unit
and a reporting system.