## 2 Literature Review
### Theoretical Study  
Theoretical studies are methodologies focused on analyzing which socio-economic and demographic factors and variables allow for the identification of NTLs within a limited area or location [15; 18]. The results of these studies can identify those responsible and point out the causes of the NTL phenomenon, allowing for the adoption of decisions and policies that help to reduce them [15]. In this regard, [PERSON] makes a distinction based on surveys and fieldwork to understand those agents involved in electricity theft in a region of Tanzania and in India [19]. [PERSON] studies the socio-economic variables obtained from illegal electricity consumers to conduct an econometric study. He concludes that education, income, social capital, the rural population rate, the temperature index, and the agricultural production rate are important determinants for characterizing electricity theft. Furthermore, he suggests that special education programs could be designed to focus on preventing electricity theft [79]. He also recommends the implementation of social tariffs that take into account the purchasing power and levels of social capital of different individuals. \begin{table}
\begin{tabular}{l l l l} \hline
**Methodology** & **Concept** & **Algorithm** & **Reference** \\ \hline \multirow{4}{*}{Theoretical study} & & They analyze socio-economic and demographic factors that & [19; 20; 21; 22; 23; 24] \\  & & evidence NTLs & \\ \hline \multirow{8}{*}{Supervised learning} & & Nearest neighbor (K-NN) & [25; 26; 27; 28; 29; 30] \\ \cline{2-3}  & & Support Vector Machine (SVM) & [6; 25; 27; 29; 31; 32; 33; 34; 35; 36; 37; 38; 39] \\ \cline{2-3}  & & Decision Tree (DT) & [28; 29; 30; 33; 40; 41; 42; 43; 44; 45] \\ \cline{2-3}  & & Random Forest (RF) & [38] \\ \cline{2-3}  & & Optimum Path Forrest (OPF) & [6; 43; 46; 47; 48; 49] \\ \cline{2-3}  & & Artificial Neural & \\ \cline{2-3}  & & Network (ANN) & [30; 37; 38; 50; 51; 52; 53; 54; 55; 56; 57] \\ \cline{2-3}  & & Bayesian classifiers & [6; 39; 41; 58; 59] \\ \cline{2-3}  & & Rule induction & [32; 40; 53; 60; 61] \\ \hline \multirow{8}{*}{Unsupervised learning} & & Self-Organizing Map (SOM) & [45; 55; 61] \\ \cline{2-3}  & & Clustering algorithms & [17; 62; 63; 64; 65; 66; 67; 68] \\ \cline{1-1} \cline{2-3}  & & Regression models & [37; 38; 43; 69; 70] \\ \cline{1-1} \cline{2-3}  & & Outlier detection & [61; 65; 71; 72] \\ \cline{1-1} \cline{2-3}  & & Expert system & [10; 34; 53; 66; 73] \\ \hline \multicolumn{3}{l}{Bibliographic analysis and state} & & [2; 3; 6; 11; 15; 16; 74; 75; 76; 77; 78] \\ \cline{1-1} \cline{2-3}  & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Bibliography summary. [PERSON] and [PERSON] analyze the socio-economic and governance factors that determine the extent of electricity theft in Indian states. Their results show that electricity theft is related to the degree of corruption, the relationship between state taxes and GDP, the efficiency in collecting electricity bills, poverty, and even literacy levels or income [20]. They also point out that a better understanding of the key determinants of thefts in electricity distribution is vital for policymakers to design policies. On the other hand, [PERSON] and [PERSON] investigate the relationship between energy theft and corruption, identifying a series of operationally measurable variables, such as the wage rate in electricity utility companies, conviction rate, fines, and civil society participation, which can help reduce electricity theft. Their study indicates that efficient wages, continuous motivation of officials, and active consumer involvement in reporting crimes can help combat corruption and energy fraud [24]. [PERSON] analyzes the effects, consequences, and measures to control electricity theft in Nigeria. He points out that illegal electricity users are divided into two general categories: those who cannot afford it and those who can afford it but choose not to pay.