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@article{167769,
        author = {SAPTAPARNI CHATTERJEE},
        title = {FRAUD DETECTION IN BANKING USING MACHINE LEARNING ALGORITHM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {298-304},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167769},
        abstract = {The number of financial transactions has grown dramatically in the last few years due to the growth of financial institutions as well as the acceptance of web-based e-commerce. In internet banking, fraudulent transactions are becoming an increasingly common issue, and detecting fraudulent activity has never been easy. In banking, detection of fraud is observed as a binary machine learning problem where input is either categorized as fraud or not. In this proposed methodology, we have proposed a banking fraud detection using three steps: Pre-processing, feature extraction as well as classification. Pre-processing has done for data cleaning process, and then the feature extraction used for detecting transaction time and finally classification hybrid (Decision Tree-Random Forest) for detecting the fraud in banking. Results showed that the hybrid algorithm gives 95.8% accuracy and least error compared with single machine learning algorithms},
        keywords = {Fraud detection, Hybrid ML, Banking system, Decision Tree and Random forest},
        month = {September},
        }