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@article{164276,
        author = {O.Durga Bhavani and M.Hemanjali and K.Bhaskar Sai and K.Santosh and J.Abhinay},
        title = {FINTECH SHIELD: Detecting anomalies in financial transactions},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {498-501},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164276},
        abstract = {The fintech shield project leverages python libraries such as pandas, plotly express, and machine learning algorithms like random forest and isolation forest to detect irregularities in financial transactions. By employing classification metrics and statistical models, including correlation matrix analysis, the system can effectively classify transactions as either regular or irregular based on input data, offering a robust solution for fraud detection in financial transactions. One of the primary purposes is to identify fraudulent activities such as unauthorized transactions, identity theft, or money laundering. By analyzing patterns and anomalies in transaction data, python scripts can flag potentially fraudulent activities for further investigation. The purpose of detecting irregularities in financial transactions with python is to enhance security, mitigate risks, ensure compliance, and improve operational efficiency within financial systems. },
        keywords = {},
        month = {},
        }