Enhancing Fraud Detection: Leveraging Deep Learning Methods for Retail Transactions
Keywords:
Economic statements, fraud, machine learning, data miningAbstract
In this work, deep learning models are used to expect the swindle in monetary declarations and to derive the important features that will be used by the auditors to control the deception in the reported declarations. Totally, eight models are developed as part of this work. For under sampled and oversampled datasets, the models are built on deep neural networks and convolutional neural networks, each having three and five layers. Top two models from the eight models are selected based on performance factors like accuracy, sensitivity and precision. The precision is defined for both positive predictive value and negative predictive price. From the top two models, important features are derived using the SHAP methodology. The important features from both the top models are analyzed to derive a common set of features that would be recommended to auditors to make their job easy and accurate.
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