Pillar 3 and Modelling of Stakeholders’ Behaviour at the Commercial Bank Website during the Recent Financial Crisis

AbstractThe paper analyses domestic and foreign market participants’ interests in mandatory Basel 2, Pillar 3 information disclosure of a commercial bank during the recent financial crisis. The authors try to ascertain whether the purposes of Basel 2 regulations under the Pillar 3 - Market discipline, publishing the financial and risk related information, have been fulfilled. Therefore, the paper focuses on modelling of visitors’ behaviour at the commercial bank website where information according to Basel 2 is available. The authors present a detailed analysis of the user log data stored by web servers. The analysis can help better understand the rate of use of the mandatory and optional Pillar 3 information disclosure web pages at the commercial bank website in the recent financial crisis in Slovakia. The authors used association rule analysis to identify the association among content categories of the website. The results show that there is in general a small interest of stakeholders in mandating the commercial bank's disclosure of financial information. Foreign website visitors were more concerned about information disclosure according to Pillar 3, Basel 2 regulation, and they have less interest in general information about the bank than domestic ones

A Fraud Detection System Based on Anomaly Intrusion Detection Systems for E-Commerce Applications

The concept of exchanging goods and services over the Internet has seen an exponential growth in popularity over the years. The Internet has been a major breakthrough of online transactions, leaping over the hurdles of currencies and geographic locations. However, the anonymous nature of the Internet does not promote an idealistic environment for transactions to occur. The increase in online transactions has been added with an equal increase in the number of attacks against security of online systems. Auction sites and e-commerce web applications have seen an increase in fraudulent transactions. Some of these fraudulent transactions that are executed in e-commerce applications happen due to successful computer intrusions on these web sites. Although a lot of awareness has been raised about these facts, there has not yet been an effective solution to adequately address the problem of application-based attacks in e-commerce. This paper proposes a fraud detection system that uses different anomaly detection techniques to predict computer intrusion attacks in e-commerce web applications. The system analyses queries that are generated when requesting server-side code on an e-commerce site, and create models for different features when information is extracted from these queries. Profiles associated with the e-commerce application are automatically derived from a training dataset

Adaptive hypertext and hypermedia : workshop : proceedings, 3rd, Sonthofen, Germany, July 14, 2001 and Aarhus, Denmark, August 15, 2001

This paper presents two empirical usability studies based on techniques from Human-Computer Interaction (HeI) and software engineering, which were used to elicit requirements for the design of a hypertext generation system. Here we will discuss the findings of these studies, which were used to motivate the choice of adaptivity techniques. The results showed dependencies between different ways to adapt the explanation content and the document length and formatting. Therefore, the system's architecture had to be modified to cope with this requirement. In addition, the system had to be made adaptable, in addition to being adaptive, in order to satisfy the elicited users' preferences

Adaptive hypertext and hypermedia : workshop : proceedings, 3rd, Sonthofen, Germany, July 14, 2001 and Aarhus, Denmark, August 15, 2001

This paper presents two empirical usability studies based on techniques from Human-Computer Interaction (HeI) and software engineering, which were used to elicit requirements for the design of a hypertext generation system. Here we will discuss the findings of these studies, which were used to motivate the choice of adaptivity techniques. The results showed dependencies between different ways to adapt the explanation content and the document length and formatting. Therefore, the system's architecture had to be modified to cope with this requirement. In addition, the system had to be made adaptable, in addition to being adaptive, in order to satisfy the elicited users' preferences

Operational change management and change pattern identification for ontology evolution

Ontologies can support a variety of purposes, ranging from capturing the conceptual knowledge to the organization of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. In this sense, the application and representation of ontology changes in terms of higher-level change operations can describe more meaningful semantics behind the applied change. We propose a four phase process that covers the operationalization, representation and detection of higher-level changes in ontology evolution life cycle. We present different levels of change operators based on the granularity and domain-specificity of changes. The first layer is based on generic atomic level change operators, whereas the next two layers are user-defined (generic/domain-specific) change patterns. We introduce the layered change logs for an explicit and complete operational representation of ontology changes. The layered change log model has been used to achieve two purposes, i.e. recording of ontology changes and mining of implicit knowledge such as intent of change, change patterns etc. We formalize the change log using a graph-based approach. We introduce a technique to identify composite changes that not only assist in formulating ontology change log data in a more concise manner, but also help in realizing the semantics and intent behind any applied change. Furthermore, we discover the reusable ordered/unordered domain-specific change patterns. We describe the pattern mining algorithms and evaluate their performance

Towards knowledge discovery from WWW log data

As the result of interactions between visitors and a web site, an http log file contains very rich knowledge about users on-site behaviors, which, if fully exploited, can better customer services and site performance. Different to most of the existing log analysis tools which use statistical counting summaries on pages, hosts, etc., we propose a transaction model to represent users access history and a framework to adapt data mining techniques such as sequence and association rule mining to these transactions. In this framework, all transactions are extracted from the raw log file though a series of step by step data preparation phases. We discuss different methods to identify a user, and separate long convoluted sequences into semantically meaningful sessions and transactions. A new feature called interestingness is defined to model user interests in different web sections. With all the transactions being imported into an adapted cube structure with a concept hierarchy attached to each dimension of it, it is possible to carry out multi-dimensional data mining at multi-abstract levels. Using interest context rules, we demonstrate the potentially significant meaning of this system prototype