scholarly journals Financial Fraud Detection Model: Based on Random Forest

2015 ◽  
Vol 7 (7) ◽  
Author(s):  
Chengwei Liu ◽  
Yixiang Chan ◽  
Syed Hasnain Alam Kazmi ◽  
Hao Fu
Keyword(s):  
Random Forest ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Detection Model ◽  
Model Based

scholarly journals A Financial Fraud Detection Model Based on Organizational Impression Management Strategy

2020 ◽  
Vol 1616 ◽  
pp. 012093
Author(s):  
Jie Zhang ◽  
Jianrong Yao ◽  
Lu Wang ◽  
Yuangao Chen ◽  
Yanqin Pan
Keyword(s):  
Impression Management ◽  
Management Strategy ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Detection Model ◽  
Model Based

A Financial Fraud Detection Model Based on LSTM Deep Learning Technique

2020 ◽  
Vol 15 (4) ◽  
pp. 498-516
Author(s):  
Yara Alghofaili ◽  
Albatul Albattah ◽  
Murad A. Rassam
Keyword(s):  
Deep Learning ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Detection Model ◽  
Model Based ◽  
Learning Technique

Fraud Detection Model Based on Multi-Verse Features Extraction Approach for Smart City Applications

2019 ◽  
pp. 241-251 ◽  
Author(s):  
Ali Safa Sadiq ◽  
Hossam Faris ◽  
Ala' M. Al-Zoubi ◽  
Seyedali Mirjalili ◽  
Kayhan Zrar Ghafoor
Keyword(s):  
Smart City ◽  
Fraud Detection ◽  
Features Extraction ◽  
Detection Model ◽  
Model Based

scholarly journals Emerging Approach for Detection of Financial Frauds Using Machine Learning

2021 ◽  
pp. 9-22
Author(s):  
Upasana Mukherjee ◽  
Vandana Thakkar ◽  
Shawni Dutta ◽  
Utsab Mukherjee ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Credit Card ◽  
Fraud Detection ◽  
Loan Default ◽  
Credit Card Fraud ◽  
Detection Model ◽  
Default Prediction ◽  
Automated Process

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.

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