1. Research Scholar, Shri Khushal Das University, Hanumangarh, Rajasthan, India

INTRODUCTION

2. OBJECTIVES

• To create and assess machine learning and artificial intelligence models for real-time fraud detection in online transactions.
• To evaluate how well various class-balancing strategies & machine learning classifiers enhance fraud detection efficiency and model interpretability.

3. RESEARCH METHODOLOGY

3.1 Data Collection

3.2 Data Preprocessing

• Elimination of duplicates.
• What does happen when the data is missing?
• Identification and control of outliers.
• Categorical variables are converted to numbers.
• Normalisation and scaling of continuous variables.
Journal of Advances in Science and Technology
Vol.23, Issue No. 1 March-2026, ISSN 2230-9659
Journal of Advances in Science and Technology
Vol.23, Issue No. 1 March-2026, ISSN 2230-9659
Sachin Bagoria, Dr. Kavita www.ignited.in
www.ignited.in
 
• User behaviour pattern based feature extraction and selection based on transaction attributes.
The same pre-processing procedure was applied throughout all the stages of the experiment.

3.3 Handling Class Imbalance

To address the issue of class imbalance, data-level rebalancing techniques were employed, as the transactions that are fraudulent are a minority class. There were three ways of oversampling:
• Synthetic Minority Oversampling Technique for Nominal & Continuous Features or SMOTENC.
• Nearest Neighbours (NN) + SMOTE (Nearest Neighbours with noise) + Edited Nearest Neighbours (ENN).
• Tomek + SMOTENC Links.
These techniques were used to adjust the ratio between the number of fraudulent and nonfraudulent transactions, which improved model learning and reduced categorisation bias.

3.4 Model Development

To identify fraud, a number of machine learning classifiers were created and assessed. The performance of several classification algorithms was compared in the study, including:
• The Random Forest Classifier.
• Classifier using Stochastic Gradient (SG).
• Additional benchmark models for categorization.
To evaluate the effect of class balancing upon fraud detection performance, each classifier underwent training using both the original & rebalanced datasets.

3.5 Experimental Setup

There were four phases to the experiments:
Experiment 1: Classifiers are trained and evaluated on the original data preprocessed.
Experiment 2: Applying the processed data for feature engineering & model optimisation.
Experiment 3: Class Imbalance correction techniques using SMOTENC and retraining classifiers.
Experiment 4: Evaluation of hybrid resampling techniques (SMOTENC + ENN & SMOTENC + Tomek Links), followed by a comparison of the performances of the classifiers.

3.6 Model Evaluation Metrics

The models were evaluated by the both in-sample & out-of-sample datasets. The following standards of categorisation were used to assess a performer's performance:
• Precision.
• Accuracy.
• Remember.
• The F1-Score.
• AUC-ROC (Area Under Receiver Operating Characteristic Curve).
Thanks to the comparison study, the best approach to classification and balancing for fraud detection was identified.

3.7 Feature Importance Analysis

The model was improved to be more interpretable by using feature significance analysis [9]. The top 10 factors that were most important for fraud detection were identified using Feature significance scores & SHAP (SHapley Additive Explanations) values.
The explanation of model predictions was done by applying SHAP analysis, with the following conditions:
• A positive SHAP value was more likely to be considered fraudulent.
• Negative SHAP values were more likely to be considered not fraudulent.

3.8 Data Visualization and Trend Analysis

Analyzed the trends and pattern of fraud and transactions using visualisation tools. Monthly demand heatmap were created to facilitate the comparison of transactions on weekdays and across seasons. These were useful for identifying when the transactions were high, and for understanding consumers' purchase patterns.

3.9 Statistical and Comparative Analysis

In order to compare efficiency of each resampling method and each classifier, comprehensive analysis was made. Results indicated that the performance of fraud detection using the Random Forest classifier is better than using the Stochastic Gradient classifier for both the balanced and unbalanced datasets. The robustness and generalisability of the model were checked using comparative analysis utilising in-sample and out-of-sample assessment outcomes.

3.10 Ethical Considerations

The data in all transactions were anonymised before analysis. All information was anonymous and research was conducted in an ethical manner.

4. RESULT

In order to ensure that all subgroups are adequately represented, we use stratified sampling to choose 50,000 rows at random from the Marketplace listing data. This is done for the purpose of the fast-computing experiment. Within the scope of this study, our primary focus is on developing a model for the identification of fraudulent activity by using numerical & categorical characteristics [10–12]. Our first step will be to search the listings data for these traits and compare them to the demographics, behaviours, and purchases made by marketplace users. A distinct pipeline is responsible for the processing of trials 1 and 2, the encoding of categorical variables, the scaling of continuous features, the handling of outliers, duplicates, & missing data, & so on.
In order to fix class asymmetry, the third and fourth experiments use an extra pipeline that uses oversampling and/or undersampling to equalise the minority and majority class ratios. Despite the fact that SMOTENC, SMOTENC + ENN, and SMOTENC + TomekLinks all get a score of 90% when working with in-sample data, they all fall short when coping with out-of-sample data, achieving only approximately 55%. The Random Forest algorithm surpasses every other classifier-rebalancing system when applied to both in-sample and out-of-sample data, as well as every performance evaluation evaluation. When compared to other classifiers, the SG classifier comes in second position overall [13]. None of the competing classifiers demonstrate any discernible improvement across all comparison points. We provide comprehensive performance data in Fig. 1, which is located below.
Table 1: Comparative Performance of Classifiers under Different Rebalancing Techniques

Classifier	RandomOverSampler	SMOTENC	SMOTENC + ENN	SMOTENC + TomekLinks
LR	Moderate	Moderate	Moderate	Moderate
KNN	Good	Good	Very Good	Very Good
CART	Low	Low	Low	Low
RF	Good	Good	Good	Good
SVC	Excellent	Excellent	Excellent	Excellent
GNB	Low	Low	Low	Low
GBC	Excellent	Excellent	Excellent	Excellent
SG	Moderate	Moderate	Moderate	Moderate

Table 1 shows the performance of various machine learning classifiers, under different class rebalancing strategies. Among all the balancing strategies, the Support Vector Classifier (SVC) & Gradient Boosting Classifier (GBC) were the best. Also, Random Forest (RF) was found to be consistent and reliable and rose to become a prominent classifer in fraud detection. The Hybrid Balancing techniques such as ENN and Tomek Links along with SMOTENC have resulted in better performance for K-Nearest Neighbours (KNN). GNB and CART, on the other hand, had also a much lower performance than all the rebalancing schemes. According to these results, when it comes to identifying fraudulent transactions in skewed e-commerce datasets, ensemble-based and sophisticated classification algorithms work better.

Figure 1: Each classifier's improvement in out-of-sample performance for a particular data-level class rebalancing approach.
Figure 2 shows a monthly demand heatmap that displays the demand for weekdays. This particular online shop had a perceptible increase in demand from the month of December to the month of May. It is typical for things to get quite chaotic in the beginning of April [14]. When businesses are in possession of all the relevant information, they are also in a position to take the right actions to encourage customers to make purchases at various times.
Table 2: Monthly Transaction Demand by Weekday

Weekday	Ja n	Fe b	Ma r	Ap r	Ma y	Ju n	Jul	Au g	Se p	Oc t	No v	De c
Sunday	33 9	268	566	619	559	194	23 1	189	10 0	32 8	165	215
Monday	25 2	244	540	660	564	191	16 3	183	11 8	25 0	159	231
Tuesday	28 0	314	651	611	390	189	19 2	133	15 0	21 9	156	190
Wednesda y	21 8	285	555	569	430	200	19 5	144	12 7	25 2	123	207
Thursday	26 2	262	534	696	314	225	20 8	141	13 9	18 7	163	222
Friday	27 7	241	530	693	319	215	31 3	123	12 0	25 5	168	243
Saturday	28 6	250	514	797	406	176	32 5	144	12 9	24 3	144	181

Monthly and weekly trends in transaction demand are seen in Table 2. The results showed that the volume of transactions has risen considerably from January to April, with April having the highest overall demand. The busiest day of the month of April was definitely when customers were the busiest with 797 transactions. Beginning in May, demand dropped steadily until it hit rock bottom in August and September. The study indicates that the heat map also shows some degree of seasonal buying behaviour and that there is some months when there are increased transactions. In order to maximise inventory management, promotional marketing, & fraud monitoring efforts throughout peak transaction times, e-commerce enterprises might benefit from understanding these demand swings.

Figure 2: Transaction heatmap for a single online business.
The top 10 traits are responsible for our model's remarkable performance in recognising instances of fraudulent activity, as seen in the global feature significance plot that can be found in Figure 3.
Table 3: Top Features Influencing Fraud Detection

Rank	Feature	Mean SHAP Value
1	median_bsg_size	1.28
2	mp_tab_initial_messages_30d	0.78
3	account_age	0.75
4	friend_count	0.41
5	initial_price	0.39
6	good_seller_rating_received_lifetime	0.34
7	largest_bsg_size	0.28
8	seller_selected_condition [new]	0.27
9	age	0.16
10	Sum of 23 Other Features	0.14

Based on SHAP values, Table 3 shows the most significant factors that contribute to fraud detection. The feature median_bsg_size had a significant impact on the model's fraud prediction capacity, as shown by its high relevance score of 1.28. Other significant predictors were initial price (0.39), friend count (0.41), account age (0.75) and mp_tab_initial_messages_30d (0.78). These variables are based on user actions, account information and transaction characteristics that are closely associated with fraud. The SHAP analysis not only enhances the interpretability and reliability of the fraud detection framework, but it also contributes to the transparency of the model.

Figure 3: Crucial elements affecting the identification of fraudulent cases
In the event that the SHAP value is negative, the chance of the model predicting a nonfraudulent instance is raised. On the other hand, the likelihood of the model predicting a fraudulent instance is enhanced when the SHAP value is positive [17].

5. CONCLUSION

The use of machine learning and artificial intelligence (AI) was demonstrated in this study as effective tools in real-time detection of online shopping fraud. Using multiple classification algorithms, class imbalance handling methods and comprehensive data pre-processing techniques, the study determined that Random Forest was the most effective model for detecting fraudulent activity. SHAP analysis helped to understand the models better by highlighting the most important characteristics related to fraud, while rebalancing strategies based on SMOTENC boosted the performance of the model during the study and classification. The results show that online marketplaces may considerably improve their transaction security, reduce financial losses, and help with trustworthy decision-making with the use of fraud detection systems powered by artificial intelligence. To counteract these more complex kinds of fraud, e-commerce systems may benefit from using powerful machine learning algorithms.

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Journal of Advances in Science and Technology
Vol.23, Issue No. 1 March-2026, ISSN 2230-9659
Journal of Advances in Science and Technology
Vol.23, Issue No. 1 March-2026, ISSN 2230-9659
Sachin Bagoria, Dr. Kavita www.ignited.in
Sachin Bagoria, Dr. Kavita www.ignited.in