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A COMBINED DEEP LEARNING APPROACH FOR FRAUDULENT DETECTION IN THE FINANCIAL SECTOR

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Fraud Detection (FD) is one of the important topics in the field of the financial sector. With the increase in the arrival of new technologies day by day, there is a huge increase in the involvement of fraud.

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Fraud Detection (FD) is one of the important topics in the field of the financial sector. With the increase in the arrival of new technologies day by day, there is a huge increase in the involvement of fraud. Mainly, existing systems fail to detect the fraudulent accurately due to inappropriate feature learning and prediction mechanisms. The majority of the works only focussed on the limited parameters to detect fraudulent. However, in fact, the frauds vary their identities and other characteristics rapidly. This research work resolves this problem by combining two data mining approaches Optimum Feature Learning (OFL) and Accurate classification approaches. The system first collects the financial data and then applies Tri-Teaching Learning (TTL) optimization algorithm. Then, the data is classified into normal and fraudulent by using the Fully Recurrent neural Network (FRNN) algorithm. The proposed feature learning and deep detection methodology achieves better accuracy, precision, and recall for financial data in experiments.

Keywords: Fraud Detection, Data Mining, Deep Learning, Financial Sector, Feature Extraction.

Number of Words : 4732

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