Electronic bank transactions are very common today. Services given by an Automatic Teller Machine (ATM), for example, are very popular and widely used by bank clients. Unfortunately, in the same way as the use of these devices is increasing, the proliferation of different frauds to try to violate these systems to steal user’s money is also increasing. Sometimes, the modus operandi used by the delinquents depends on different factors, such as the country or the city where fraud is committed or, as in the case of ATMs, the model or location of these devices. Since the detection of these modus operandi is not easy and they could be different from a bank institution to another, having both an environment capable of following up the swindler agents learning processes and a way to prevent the cooperation between these agents to share the learned knowledge, would be very useful to discover different modus operandi before crimes are committed. In this paper, a framework designed to follow up the swindlers’ agents learning process and to share the knowledge between the agents is presented. This framework is based on the FIPA (Foundation for Intelligent Physical Agents) specifications and it emphasizes on the swindler agents learning process to fulfil the human-like agent behaviour and a realistic interaction with the environment.
Electronic bank transactions are very common today. Services given by an Automatic Teller Machine (ATM), for example, are very popular and widely used by bank clients. Unfortunately, in the same way as the use of these devices is increasing, the proliferation of different frauds to try to violate these systems to steal user’s money is also increasing. Sometimes, the modus operandi used by the delinquents depends on different factors, such as the country or the city where fraud is committed or, as in the case of ATMs, the model or location of these devices. Since the detection of these modus operandi is not easy and they could be different from a bank institution to another, having both an environment capable of following up the swindler agents learning processes and a way to prevent the cooperation between these agents to share the learned knowledge, would be very useful to discover different modus operandi before crimes are committed. In this paper, a framework designed to follow up the swindlers’ agents learning process and to share the knowledge between the agents is presented. This framework is based on the FIPA (Foundation for Intelligent Physical Agents) specifications and it emphasizes on the swindler agents learning process to fulfil the human-like agent behaviour and a realistic interaction with the environment. Read More
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