Since 2014, Emotet has been using Man-in-the-Browsers (MITB) attacks to target companies in the finance industry and their clients. Its key aim is to steal victims' online money-lending records and vital credentials as they go to their banks' websites. Without analyzing network packet payload computing (PPC), IP address labels, port number traces, or protocol knowledge, we have used Machine Learning (ML) modeling to detect Emotet malware infections and recognize Emotet related congestion flows in this work. To classify emotet associated flows and detect emotet infections, the output outcome values are compared by four separate popular ML algorithms: RF (Random Forest), MLP (Multi-Layer Perceptron), SMO (Sequential Minimal Optimization Technique), and the LRM (Logistic Regression Model). The suggested classifier is then improved by determining the right hyperparameter and attribute set range. Using network packet (computation) identifiers, the Random Forest classifier detects emotet-based flows with 99.9726 percent precision and a 92.3 percent true positive rating.

This primary research paper emphasizes cross-validation, where data samples are reshuffled in each iteration to form randomized subsets divided into n folds. This method improves model performance and achieves higher accuracy than the baseline model. The novelty lies in the data preparation process, where numerical features were imputed using the mean, categorical features were imputed using chi-square methods, and normalization was applied. This research study involves transforming the original datasets and comparative model analysis of four Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) cross-validation methodologies to heart disease open datasets. The objective is to easily identify the average accuracy of model predictions and subsequently make recommendations for model selection based on data preprocessing cross-validation model increased (5 to 14%) more than baseline model for best model selection. From comparing each model’s accuracy scores, it is found that the logistic regression and k-nearest neighbor models achieved the highest accuracy of 81% among the four models when single accuracy is a concern. However, the random forest model summary statistics attained an F1 score of 95%, precision (96%), and recall (97%), indicating the highest overall macro accuracy score. These findings can be further compared using learning curve validation. Conversely, the logistic regression model exhibited the lowest accuracy of 84% among the four machine learning models. However, this research does not cover hyperparameter optimization, which could potentially improve model performance.

The rapid growth of FinTech services, particularly robo-advisors, has transformed how individuals engage with digital financial platforms. Understanding the behavioral drivers of technology acceptance in this context is critical for enhancing adoption and designing more effective user experiences. This study investigates whether user-level behavioral and transactional data can be leveraged to predict technology acceptance, operationalized through daily app usage. Grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the study uses behavioral proxies such as customer satisfaction, loyalty points, and lifetime value to reflect constructs like perceived usefulness, performance expectancy, and facilitating conditions. Using a real-world dataset of 7000 FinTech users sourced from Kaggle, we applied four machine learning algorithms, Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, to classify users into high and low acceptance categories. Results revealed that ensemble models, particularly XGBoost, outperformed linear classifiers, achieving moderate improvements in precision and recall for the high-acceptance class. However, overall predictive performance remained constrained by class imbalance and overlapping behavioral patterns. These findings suggest that while machine learning can reveal patterns linked to technology acceptance, predictive precision remains limited without richer temporal and psychographic features. The study contributes to the evolving discourse on FinTech adoption by offering a data-driven lens to complement intention-based models and inform adaptive engagement strategies.