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Purpose The purpose of this paper is to examine the artificial intelligence (AI) methodologies to fight against money laundering crimes in Colombia. Design/methodology/approach This paper examines Colombian money laundering situations with some methodologies of network science to apply AI tools. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications 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. Originality/value 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.

The design of the robotic vehicle VILMA at UNICAMP is developed in-vehicle platform Fiat Punto. In addition to a set of sensors, actuators, mechanism and components (hardware and/or software), new technologies should be developed in support of Automation, Control, Perception, Localization and Navigation. This work presents the design and simulation of path tracking control using model predictive control (MPC) which attempts to exploit the characteristics of the structured environment where the future path is previously known. The model for design the controller is based in a single tracking model of the vehicle and in a model of the steering which the state variables are observed by the Extended Kalman Filter (EKF). Finally, it is explained how the path is smoothed generating an arc between the points and making an optimization process by the gradient algorithm.Index Terms-Autonomous Vehicle, Path Tracking, Model Predictive Control, VDA test 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol 978-1-4799-6711-7/14 $31.00

Intelligent autonomous vehicles have received a great degree of attention in recent years. Although the technology required for these vehicles is relatively advanced, the challenge is firstly to ensure that drivers can understand the capabilities and limitations of such systems and secondly to design a system that can handle the interaction between the driver and the automated intelligent system. In this study, we describe an approach using different strategies for an autonomous system and a driver to drive a vehicle cooperatively. The proposed strategies are referred to as cooperative planning and control and determine when and how the path projected by the autonomous system can be changed safely by the driver to a path that he wishes to follow. The first phase of the project is described, covering the design and implementation of an autonomous test vehicle. Experiments are carried out with a driver to test the cooperative planning and control concepts proposed here.

The activities of money laundering are a result of corruption, illegal activities, and organized crime that affect social dynamics and involved, directly and indirectly, several communities through different mechanisms to launder illegal money. In this article, we propose a machine learning approach to the analysis of suspicious activities in nonbanking correspondents, a type of financial agent that develops some financial transactions for specific banking customers. This article uses several algorithms to identify anomalies in a transaction set of a nonbanking correspondent during 2019 for an intermediary city in Colombia. Our results show that some methodologies are more appropriate than others for this case and facilitate to identify the anomalies and suspicious transactions in this kind of financial intermediary.

Context: Before autonomous vehicles being a reality in daily situations, outstanding issues regarding the techniques of autonomous mobility must be solved. Hence, relevant aspects of a path planning for terrestrial vehicles are shown.Method: The approached path planning technique uses splines to generate the global route. For this goal, waypoints obtained from online map services are used. With the global route parametrized in the arc-length, candidate local paths are computed and the optimal one is selected by cost functions.Results: Different routes are used to show that the number and distribution of waypoints are highly correlated to a satisfactory arc-length parameterization of the global route, which is essential to the proper behavior of the path planning technique.Conclusions: The cubic splines approach to the path planning problem successfully generates the global and local paths. Nevertheless, the use of raw data from the online map services showed to be unfeasible due the consistency of the data. Hence, a preprocessing stage of the raw data is proposed to guarantee the well behavior and robustness of the technique.

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