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This study aims to explore the digital divide between students living in metropolitan and non-metropolitan areas in the Antioquia region of Colombia. This is achieved by collecting data about student interactions from the Moodle learning management system (LMS), and subsequently applying supervised machine learning models to infer the gap between students in metropolitan and non-metropolitan areas.

This work uses the well-established Cross-Industry Standard Process for Data Mining methodology, which comprises six phases, viz., problem understanding, data understanding, data preparation, modelling, evaluation and implementation. In this case, student data was collected from the Moodle platform from the Antioquia campus of the UNAD distance learning university.

The digital divide is evident in the classification model when observing differences in variables such as the number of accesses to the LMS, the total time spent and the number of distinct IP addresses used, as well as the number of system modification events.

This study provides conclusions regarding the problems students in virtual education may face as a result of the digital divide in Colombia which have become increasingly visible since the implementation of machine learning methodologies on LMS such as Moodle. However, these practices may be replicated in any virtual educational context and furthermore be extended to enable personalisation of various aspects of the Moodle platform to meet the individual needs of students.

The purpose of this paper is to examine the artificial intelligence (AI) methodologies to fight against money laundering crimes in Colombia.

This paper examines Colombian money laundering situations with some methodologies of network science to apply AI tools.

This paper identifies the suspicious operations with AI methodologies, which are not common by number, quantity or characteristics within the economic or financial system and normal practices of companies or industries.

Access to financial institutions’ data was the most difficult element for research because affect the implementation of a set of different algorithms and network science methodologies.

This paper tries to reduce the social and economic implications from money laundering (ML) that result from illegal activities and different crimes against inhabitants, governments, public resources and financial systems.

This paper proposes a software architecture methodology to fight against ML and financial crime networks in Colombia which are common in different countries. These methodologies complement legal structure and regulatory framework.

The contribution of this paper is how within the flow already regulated by financial institutions to manage the ML risk, AI can be used to minimize and identify this kind of risk. For this reason, the authors propose to use the graph analysis methodology for monitoring and identifying the behavior of different ML patterns with machine learning techniques and network science methodologies. These methodologies complement legal structure and regulatory framework.