annotated bibliography for below articles In apa no specific word count

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. 

https://www.sciencedirect.com/science/article/abs/pii/S0957417418301179(Predicting 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-

137311.

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.