Alvarado-Acuña, Luis Segundo
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Alvarado-Acuña
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Luis Segundo
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lualvar@ucn.cl
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Publicación Method for Revenue Assurance and Financial Fraud Alerting Supported by LDS Techniques(Springer Nature, 2025-03-25) Pérez-Pupo, Iliana; Piñero-Pérez, Pedro Yobanis; Yzquierdo Herrera, Raykenler; Alvarado-Acuña, Luis Segundo; Piñero-Ramírez, Carlos Manuel; Piñero-Ramírez, Pedro E.Revenue assurance and the issuance of Financial Fraud alerts are essential challenges that impact both financial institutions and businesses. This is a complex problem where new challenges and methods consistently arise, necessitating the continuous improvement of detection systems. In this context, the use of various computational intelligence techniques and elements from neutrosophic theory can assist in managing uncertainty and indeterminacy. The methods section includes a brief analysis of the state of the art in artificial intelligence for detecting financial fraud situations. Furthermore, an algorithm is proposed for detecting potential financial fraud situations, supported by data linguistic summarization techniques. These techniques are employed in combination with principles from neutrosophic theory. Subsequently, in the results section, the proposal is validated by comparing the proposed method with a rule-based approach reported in the literature. Additionally, the model is evaluated by subject matter experts, demonstrating the contributions of the proposed model.Publicación Framework for Strategic Planning and Assisted by Artificial Intelligence(Springer Nature, 2025-03-25) Alvarado-Acuña, Luis Segundo; Piñero-Pérez, Pedro Yobanis; Yzquierdo-Herrera, Raykenler; Piñero-Ramírez, Carlos Manuel; López-Correa, Francisco Javier; García-Vacacela, RobertoStrategic forecasting continues to be one of the fundamental elements in decision making. This branch of management sciences, like other branches of knowledge, is being influenced by artificial intelligence and other emerging technologies. In this work, in the first section of the methods section, a brief study is made of the state of the art of trends in strategic planning and the points of contact with artificial intelligence. The opportunities for improvement of existing strategic forecasting techniques with respect to the treatment of uncertainty are analyzed. In the second section, a proposal is made for a framewrok for strategic planning assisted by artificial intelligence techniques. This model allows aiding decision making while maintaining an adequate management of information uncertainty. Then, in the results analysis session, the proposals made are validated. Statistical techniques and expert and data triangulation methods are used. As conclusions, the validity of the proposal and its power for decision making under uncertainty is demonstrated. Future lines of work are also presented.