## 7 Conclusions
Additionally, models such as K-NN, RL, and NB demonstrated low performance and were only included for comparative purposes. To validate the cross-validation results, a Test Matrix was classified, and performance metrics were computed. The models exhibited only a slight decrease in performance, with CGB maintaining the highest accuracy (Acc = 0.869, F1 = 0.876). However, as previously noted, its computational cost remains higher than LGB (Acc = 0.869, F1 = 0.875), which offers a more efficient alternative. Although the positive results are notable, this study presents limitations that must be considered. On one hand, the dependence on the quality and availability of data can affect the accuracy and effectiveness of the models. The data used come from a single Ecuadorian distribution company and may have biases or limitations in their representativeness. Including historical information from multiple companies could improve the robustness and generalization capability of the model. On the other hand, the exclusive use of technical data and the absence of socio-economic variables limit the model's predictive capacity. Combining technical data with socio-economic factors could improve performance and reduce false positives in fraud detection. It is also worth noting that hyperparameter optimization through grid search can be computationally expensive and does not guarantee the best global solution. Alternatives such as Bayesian optimization or random search could be more efficient. Finally, the applicability and scalability of the model in other regions or countries with different consumption patterns and regulations may require adjustments. Replicating the study in other contexts would allow for a better evaluation of the model's adaptability. From the methodology, more effective regulations and policies aimed at reducing electricity theft and fraud could be created. High-risk areas and consumers prone to committing offenses could also be identified. In the same vein, resource allocation could be optimized, as inspections and specific interventions could be more efficiently targeted, maximizing the impact of anti-theft measures. However, this or any fraud detection technique should also be accompanied by awareness and education directed at specific demographics and regions, in order to reduce the incidence of theft. This research study promotes the development of regulatory frameworks that encourage the use of advanced detection and data analysis technologies in the electricity sector. Regulators could employ these techniques to effectively monitor compliance with anti-theft regulations and ensure that distribution companies are taking the appropriate measures to reduce NTLs. **Author Contributions:** Conceptualization, C.A.-M. and A.B.-E.; methodology, C.A.-M. and A.B.-E.; software, C.A.-M.; validation, A.B.-E. and M.T.-O. ; formal analysis, A.B.-E., M.T.-O. and X.S.-G.; investigation, C.A.-M.; resources, A.B.-E.; data curation, M.T.-O. ; writing--original draft preparation, C.A.-M.; writing--review and editing, A.B.-E. and X.S.-G. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was carried out thanks to the support of Salesian Polytechnic University and the Energy Transition Research Group of said university. It was supported by \"Sostenibilidad y resiliencia de ciudades medias y su contribucion a la transicion energetica\", Convocatoria 2021 de Proyectos estratgeicos orientados transicion ecologica y transicion digital 2021, Ministerio de Ciencia e Innovacion--Cod. TED2021-131097B-I00 (project No. TED2021-131097B-I00 funded by MCIN/AEI/10.13039/501100011033/\"European Union NextGenerationEU/PRTR\"). Situacion actual y metodologia para integracion de generacion y redes electricas en el territorio y PDOTs\" research project. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available upon request from the corresponding author due to data security and confidentiality requirements.