• I. Manimozhi
• Suganthi Sivakumar
• Santhiya .M
• S.Pramela devi

Keeping money and financial services organizations give a few administrations, for example, retail saving money, corporate venture saving money, protection, resource administration and portfolio administration. While managing multitudinous cash Transactions, Company will have an interior group which intently screens and cautions the exchange which could be considered fake. Any incremental pick up accomplished in prescient precision can be especially valuable for the association. This work proposes an approach that coordinates unsupervised and managed learning strategies to manufacture a prescient model that goes for enhancing the exactness. Encourage it predicts the likelihood of an exchange being fake. Four characterization calculations considered to manufacture the model are: Random Forest, Logistic Regression, Naïve Bayes and Gradient Boosted Tree. Review and f1-score are utilized to evaluate the models legitimacy. The machine learning apparatuses resemble Python, Sklearn and anaconda Jupyter are used for the utilized for the execution method.

Consumer Credit Fraud Detection, Neural Networks, Gradient Boosted Tree, Logistic Regression

"AN INTELLIGENT APPROACH TOWARDS TO DETECT FRAUDULENT IN FINANCIAL SERVICE MANAGEMENT SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.529-536, August-2018, Available :http://www.jetir.org/papers/JETIRA006096.pdf

"AN INTELLIGENT APPROACH TOWARDS TO DETECT FRAUDULENT IN FINANCIAL SERVICE MANAGEMENT SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp529-536, August-2018, Available at : http://www.jetir.org/papers/JETIRA006096.pdf