prioritized class typically requires a comprehensive strategy, integrating data preprocessing and enhancement, algorithm refinement, and customized assessment. The application of artificial intelligence (AI) technology for the automatic identification and early warning of risks serves as a significant asset in supporting financial regulation. Consequently, future applications of ML in financial regulation must focus on evaluating strategies that balance the advantages and costs associated with algorithms [4]. Although machine learning greatly enhances security in the Fintech sector, it also presents certain challenges. Implementing machine learning models in security systems necessitates substantial high-quality data and computational resources, as well as continuous maintenance, to guarantee that models stay effective against evolving threats [5; 6]. Multi-class data classification issues are a significant challenge in the field of intrusion detection [7; 8]. Rigid ML models and thorough feature engineering are necessary for identifying and categorizing threats in IoT networks [9]. Real-time data processing systems produce substantial volumes of data that require classification. Prioritization in multi-class ML requires novel methodologies and the modification of current classification techniques [10]. ML algorithms assume that all misclassification errors made by a model, if not configured, are equivalent. Missing a positive or minority class case in the context of an intrusion detection issue is more detrimental than inaccurately classifying an example from the negative or majority class. As regards altering weights, the most straight-forward and widely used method of implementing cost-sensitive learning is to penalize the model less for training errors committed on examples from the minority class [11]. The concept of sorting probabilities is frequently applied in extreme multi-label classification to optimize particular loss functions, such as average precision loss, disattribution ranking loss, gradient harmonizing mechanism loss, and label distribution-aware margin loss [12; 13]. An error-correcting output code (ECOC) model has been extensively researched and implemented across multiple domains, including computer vision, Fintech, and CTI [14]. The ECOC classification process necessitates a coding design that specifies the classes for the binary learners to train on, as well as a decoding scheme that outlines the method for aggregating the results (predictions) from the binary classifiers. In an ECOC code matrix, each row signifies a distinct class, while each column pertains to a specific class reassignment scheme. Data preprocessing is an important part of creating strong ML models, which includes cleaning the data (getting rid of duplicates, fixing missing information, and correcting inconsistencies), creating features (turning categorical variables into a usable format), and dealing with class imbalance. Class imbalance can result in the distortion of the decision border, increasing classification errors for high-value data important for various domains, especially in Fintech [15]. Data preparation and enhancement address class imbalance. Algorithmic methods focus on modifying or creating machine learning algorithms to proficiently manage datasets, improving their capability to reliably classify instances from minority class [16]. Examples of algorithms that are typically resistant to class imbalance include Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), and Support Vector Machines (SVMs) [17]. In many cases, researchers do not give priority to the classification of cyber-attacks in order to accomplish the most crucial attack detection in multi-class datasets. Instead, they divide the multi-class dataset into two classes and develop models that determine whether or not there is an attack (see Section 2 for more information). Resolving the mentioned problems will make multi-class prioritized classification more accurate, which will improve ML model performance.