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@article{175961,
        author = {Om Kandpal and Deepali Deshpande and Deep Khanchandani and Tanishq Masram and Shyam Pareek and Ayush Jagtap},
        title = {Comparative Analysis Of Financial Fraud And Enhancing Accuracy Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6830-6837},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175961},
        abstract = {The performance of several machine learning algorithms for detecting financial fraud has been evaluated and compared in this study using an extremely unbalanced data set of over 100,000 records containing only a mere 0.1% of that data considered to be fraudulent transactions. For this purpose, we use the machine learning algorithms Naïve Bayes, K-Nearest Neighbors (KNN), Decision Tree, Logistic Regression, Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). Comprehensive preprocessing and data manipulation techniques were employed to handle the class imbalance and ensure reliable results. Diagnostics in terms of accuracy, precision, recall, f1-score, and AUC-ROC were elaborately scrutinized with the support of visual presentation. It compares the merits and demerits of each of the algorithms with regard to detecting fraud. Our findings provide insights into selecting the most effective models for fraud detection, emphasizing the importance of dataset quality and algorithmic customization.},
        keywords = {Financial fraud detection, Machine learning algorithms, Imbalanced datasets, Data preprocessing, Algorithm comparison, Fraud detection metrics, Accuracy evaluation, Neural networks},
        month = {April},
        }