Need help? We are here

Teles, G., Rodrigues, J. J., Rabê, R. A., & Kozlov, S. A. (2020). Artificial neural network and Bayesian network models for credit risk prediction. Journal of Artificial Intelligence and Systems, 2, 118-132.
Lakhani, M., Dhotre, B., & Giri, S. (2019). Prediction of credit risks in lending bank loans using machine learning. SAARJ Journal on Banking & Insurance Research, 8(1), 55-61.
Sun, T., & Vasarhelyi, M. A. (2018). Predicting credit card delinquencies: An application of deep neural networks. Intelligent Systems in Accounting, Finance and Management, 25(4), 174-189.
Fitzpatrick, Trevor & Mues, Christophe, 2016. An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market, European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317-324.
Chi, G., Uddin, M. S., Abedin, M. Z., & Yuan, K. (2019). Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches. International Journal on Artificial Intelligence Tools, 28(05), 1950017.
Barboza, Flavio & Kimura, Herbert & Altman, Edward. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Systems with Applications. 83. 10.1016/j.eswa.2017.04.006. mortgage default using convolutional neural networks)

Graves, J. T., Acquisti, A., & Christin, N. (2018). Should Credit Card Issuers Reissue Cards in
Response to a Data Breach? Uncertainty and Transparency in Metrics for Data Security
Policymaking. ACM Transactions on Internet Technology (TOIT), 18(4), 1-19.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2017). Credit card fraud
detection: realistic modeling and a novel learning strategy. IEEE transactions on neural
networks and learning systems, 29(8), 3784-3797.
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., & Zeineddine, H. (2019). An
experimental study with imbalanced classification approaches for credit card fraud
detection. IEEE Access, 7, 93010-93022.
Kalid, S. N., Ng, K. H., Tong, G. K., & Khor, K. C. (2020). A Multiple Classifiers System for
Anomaly Detection in Credit Card Data with Unbalanced and Overlapped Classes. IEEE
Access, 8, 28210-28221.

Taha, A. A., & Malebary, S. J. (2020). An Intelligent Approach to Credit Card Fraud Detection
Using an Optimized Light Gradient Boosting Machine. IEEE Access, 8, 25579-25587.
Can, B., Yavuz, A. G., Karsligil, E. M., & Guvensan, M. A. (2020). A Closer Look into the
Characteristics of Fraudulent Card Transactions. IEEE Access, 8, 166095-166109.
Kundu, A., Panigrahi, S., Sural, S., & Majumdar, A. K. (2009). Blast-ssaha hybridization for
credit card fraud detection. IEEE transactions on dependable and Secure Computing,
6(4), 309-315.
Al-Khater, W. A., Al-Maadeed, S., Ahmed, A. A., Sadiq, A. S., & Khan, M. K. (2020).
Comprehensive Review of Cybercrime Detection Techniques. IEEE Access, 8, 137293-
Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud
detection using AdaBoost and majority voting. IEEE access, 6, 14277-14284.

Connect with a professional writer in 5 simple steps

Please provide as many details about your writing struggle as possible

Academic level of your paper

Type of Paper

When is it due?

How many pages is this assigment?