Hybrid Machine Learning for Fraud Detection: Balancing Accuracy and Security in Digital Transactions

There are certain studies on fraud detection in financial transactions that cause loss of money for the customer as well as bank too in some cases. To overcome these issues, the studies involved in this domain are analyzed and demonstrated here. Achary and Shelke [1], the usage of machine learning and AI would quickly detect fraud, which enables the taking of necessary steps to prevent the loss of money. The methodology used to get better accuracy than traditional methods used in this scenario. From Maskale et al. [2], the history of transactions is analyzed and cleaned using data preprocessing and applied using machine learning models. These models' accuracy is computed and compared. The objective is for the number of customers' churning rate to be detected and to provide the best practices to retain those customers. From Gandhi and Gajjar [3], the review is conducted on various domains that increase fraud detection rates and increase protection on customer interests in making secure payments over digital transactions. From Akash et al. [4], demonstrated many methodologies for financial fraud detection. Effective protection schemes as well as by combining a few methodologies for efficient financial transactions are provided to safeguard the organizational resources. According to Tatineni [5], the integration of ML approaches with blockchain technology avoids easy fraudulent activity. This integration improves traceability as well as secure financial transactions. From Ismail, M.M., Haq [6], the demonstration of various ML approaches with AI, are compared for accuracy. The random forest against other classifiers is evaluated and found random forest has having best accuracy. From Kajol et al. [7], demonstrated the motivators for the adoption of digital technology such as fast accomplishment, security, comfort, and others. The challenges also identified such as training, cost, complexity, and others that would stop from expansion of adoption. From Abad-Segura and González-Zamar [8], demonstrates the publications that have recently in the domain of banking, and financial-related transactions. In this, oxford students have done many publications in this domain. This signifies the importance of financial transactions today. From Ushadevi [9], the differences between mobile transaction banking and traditional banking are demonstrated. The revolution in usage of applications of many banks in India made paperless, and easy to do tasks in terms of depositing as well as fund transfer. From Westermeier [10], the European countries suffer from technology, political, and economic issues. When individual data is transformed into application data, where the customer can perform transactions. This became base for more insights to perform any trending analysis.

From Aburbeian andFernández-Veiga [11], demonstrated two level layers which are MFA and ML techniques. There are ML models such as Random Forest, Logistic regression, and other models that were used, and their accuracies are evaluated. This system validates authentication and increases security measures. From Kajol and Singh [12], the users in India status on digital transactions is reported that male members have more ideas on digital awareness, and challenges listed in this study. This survey is done by imposing a set of questionnaires to a few samples to know the digital awareness of our Indian people. From Liu et al. [13], demonstrated the fintech research in terms of adoption, and its development. There were many articles published in this domain. Mostly. classification is one popular approach against 5 measures that showcase current status and future analysis. From Salam et al. [14], the challenges in the banking sector faced during the pandemic are highlighted and are minimized with the increase in mobile banking applications with security and user comfort. Most people's lives are safe and threats is minimized using mobile banking applications, and usage of them are increased thereafter. From Pereira et al. [15], demonstrated avoiding and minimizing cyberbullying using normalization of data preprocessing, then a decision tree algorithm with sophistication, and also used PSO for forecasting financial marketing. these models are compared and recommended network models with feature extraction for achieving more accuracy and efficiency. From Gaur and Verma [16], demonstrated advertisements, and necessary steps to promote the usage of digital and mobile financial applications in Haryana. The initiations that government agencies, NGO firms, to invest and service agencies provide effective services. From Malagatti et al. [17], demonstrated the various tools and assessments to increase digital platform usage awareness. The objective of Fintech is to assess the usage of digital platforms using techniques such as correlation, regression, and Few tests. From Kotni and Botta [18], demonstrated the people's patterns of Visakhapatnam City who perform digital transactions, and their demographics. This reports people are less usage of cash but make use of mostly digital platforms for money transactions. The union budget initiations for deep awareness on digital literacy. From Al-Hashedi and Magalingam [19], demonstrated the articles published during specific periods in which most are done on bank fraud, and insurance fraud, using popular method SVM, then followed other models such as random forest, and naive Bayesian. From Dama et al. [20], demonstrated the various models that minimize the false positives in fraudulent activities. This system focused on detecting unusual patterns as well as able to detect new types of vulnerabilities also effectively. From Al Marri and AlAli [21], the fraudulent tasks were detected using many ML techniques, and their accuracies were compared. Their challenges are addressed in this study. From West et al. [22], demonstrated various approaches that were used to detect fraud behavior. In this, traditional and automated processes are analyzed, and compared. From Ali et al. [23], demonstrated the type of fraud called credit card fraud, and its detection using famous methods such as Support vector machines and Neural network approaches. Many articles are studied and explored their gaps and restrictions for further future development. The other studies represent storage and distribution of load in which Dey and Sangaraju [24] demonstrated the issues of using local, global load balancing strategies for the distribution of workload among the available entities over the cloud and Dey and Sangaraju [25] demonstrated effective mechanisms for evaluating the performance of load balancing involved over the cloud usage. In regard to work of Talukder et al. [26], the combination of methods used and integrated are such as Decision Trees, Random Forests, Multilayer perceptron, K-nearest neighbor, and instant hardness threshold approach together. Their performances, and accuracies when compared in which proposed hybrid approach produces too far better result but complexity, and lack of appropriate links among the strategies is the drawback of this model. In the view of Btoush et al. [27], the considered models from machine learning and deep learning are used and ensemble approaches such as stacking and resampling are applied. The best method which produces better accuracy, and performance is taken and yields higher F1-score than others used in the evaluation but considering many methods results complexity of the process.

Table 4. Metrics intensity against specific metrics

Technique	Accuracy	Performance	Interpretability	Error Rate	Cost
Random Forest	High	Good for large datasets	Moderate	Moderate	Moderate
Support Vector Machine (SVM)	High	Good, but slower with large datasets	Low	Low	High
Logistic Regression	Moderate to High	Fast	High	Moderate	Low
Neural Networks	Very High	High, but resource-intensive	Low	Low	High
XGBoost	Very High	Fast and efficient	Moderate	Low	Moderate to High
Hybrid Approach (RF, LightGBM, Ensemble)	Very High	Very efficient	Moderate	Very Low	Moderate

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Figure 4. Effectiveness of models against the hybrid method for fraud detection

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Figure 5. Performance of considered models separately against combined

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Figure 6. Error rate of considered models

Table 5. Comparison of metrics against the considered models

Technique	Accuracy	Performance	Interpretability	Error Rate	Cost
Random Forest	95	92	70	15	85
Support Vector Machine (SVM)	95	90	50	5	90
Logistic Regression	85	95	80	20	60
Neural Networks	98	97	40	3	90
XGBoost	98	98	70	5	90
Hybrid Approach (RF, LightGBM, Ensemble)	99	99	85	2	85

Table 6. Specific performance metrics against individual methods of hybrid model

Model	Accuracy	Precision	Recall	F1 Score
Random Forest	95	92	90	91
LightGBM	97	94	91	92
Ensemble	98	95	93	93
Hybrid Approach	99	97	95	95

Table 7. Error rate against the considered models

The implication of Table 5 analyzes the measures such as interpretability in terms of understanding and tuning, Error rate in terms of wrong predictions, time taken to complete the process is denoted by performance, and cost to implement, perfectness of the mechanism is described with evaluated values. From Table 6, the specific measures are described such as precision, recall, and F1-score, and are found good compared against individual models. From Table 7, the method that has a minimum error rate would be the best approach, and ensure reduced false positives, and effective detection rate.