References
[1]. Amir E Khandani, Adlar J Kim, and Andrew W Lo. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11):2767–2787, 2010
[2]. Khyati Chaudhary, Jyoti Yadav, and Bhawna Mallick. A review of fraud detection techniques: Credit card. International Journal of Computer Applications, 45(1):39–44, 2012
[3]. Aihua Shen, Rencheng Tong, and Yaochen Deng. Application of classification models on credit card fraud detection. In 2007 International conference on service systems and service management, pages 1–4. IEEE, 2007.
[4]. Sumit Agarwal, Paige Marta Skiba, and Jeremy Tobacman. Payday loans and credit cards: New liquidity and credit scoring puzzles? American Economic Review, 99(2):412–417, 2009.
[5]. Dejan Varmedja, Mirjana Karanovic, Srdjan Sladojevic, Marko Arsenovic, and Andras Anderla. Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), pages 1–5. IEEE, 2019
[6]. Tal Zarsky. The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1):118–132, 2016.
[7]. Zhenpeng Chen, Jie M Zhang, Federica Sarro, and Mark Harman. Maat: a novel ensemble approach to addressing fairness and performance bugs for machine learning software. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 1122–1134, 2022
[8]. Maria De-Arteaga, Stefan Feuerriegel, and Maytal Saar-Tsechansky. Algorithmic fairness in business analytics: Directions for research and practice. Production and Operations Management, 31(10):3749–3770, 2022.
[9]. Mahmoud Abdallah, Nhien An Le Khac, Hamed Jahromi, and Anca Delia Jurcut. A hybrid cnn-lstm based approach for anomaly detection systems in sdns. In Proceedings of the 16th International Conference on Availability, Reliability and Security, pages 1–7, 2021.
[10]. Tuong Le, Bay Vo, Hamido Fujita, Ngoc-Thanh Nguyen, and Sung Wook Baik. A fast and accurate approach for bankruptcy forecasting using squared logistics loss with gpu-based extreme gradient boosting. Information Sciences, 494:294–310, 2019.
[11]. María Óskarsdóttir, Cristián Bravo, Carlos Sarraute, Jan Vanthienen, and Bart Baesens. The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 74:26–39, 2019.
[12]. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6):1–35, 2021.
[13]. David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. Learning adversarially fair and transferable representations. In International Conference on Machine Learning, pages 3384–3393. PMLR, 2018
[14]. Nhlakanipho Michael Mqadi, Nalindren Naicker, and Timothy Adeliyi. Solving misclassification of the credit card imbalance problem using near miss. Mathematical Problems in Engineering, 2021:1–16, 2021.
[15]. Saharon Rosset. Model selection via the auc. In Proceedings of the twenty-first international conference on Machine learning, page 89, 2004.
[16]. Yulu Jin and Lifeng Lai. Fairness-aware regression robust to adversarial attacks. arXiv preprint arXiv:2211.04449, 2022
[17]. Clemens Kreutz, Andreas Raue, and Jens Timmer. Likelihood based observability analysis and confidence intervals for predictions of dynamic models. BMC Systems Biology, 6(1):1–9, 2012.
[18]. JD Balakrishnan and Roger Ratcliff. Testing models of decision making using confidence ratings in classification. Journal of Experimental Psychology: Human Perception and Performance, 22(3):615, 1996
[19]. Nazeeh Ghatasheh. Business analytics using random forest trees for credit risk prediction: a comparison study. International Journal of Advanced Science and Technology, 72(2014):19–30, 2014.
[20]. Trishita Saha, Saroj Kumar Biswas, Saptarsi Sanyal, Souvik Kumar Parui, and Biswajit Purkayastha. Credit risk prediction using extra trees ensemble method. In 2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), pages 1–8. IEEE, 2023
[21]. Wenyu Qiu. Credit risk prediction in an imbalanced social lending environment based on xgboost. In 2019 5th International Conference on Big Data and Information Analytics (BigDIA), pages 150–156. IEEE, 2019.
[22]. Maria Aparecida Gouvêa and Eric Bacconi Gonçalves. Credit risk analysis applying logistic regression, neural networks and genetic algorithms models. In POMS 18th annual conference, 2007.
[23]. Siddharth Bhatore, Lalit Mohan, and Y Raghu Reddy. Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4:111–138, 2020
Cite this article
Wu,S.;Yang,T. (2023). Towards Fair Credit Risk Assessment: Developing Mathematical Models with Equity and Accuracy. Advances in Economics, Management and Political Sciences,44,151-165.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Amir E Khandani, Adlar J Kim, and Andrew W Lo. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11):2767–2787, 2010
[2]. Khyati Chaudhary, Jyoti Yadav, and Bhawna Mallick. A review of fraud detection techniques: Credit card. International Journal of Computer Applications, 45(1):39–44, 2012
[3]. Aihua Shen, Rencheng Tong, and Yaochen Deng. Application of classification models on credit card fraud detection. In 2007 International conference on service systems and service management, pages 1–4. IEEE, 2007.
[4]. Sumit Agarwal, Paige Marta Skiba, and Jeremy Tobacman. Payday loans and credit cards: New liquidity and credit scoring puzzles? American Economic Review, 99(2):412–417, 2009.
[5]. Dejan Varmedja, Mirjana Karanovic, Srdjan Sladojevic, Marko Arsenovic, and Andras Anderla. Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), pages 1–5. IEEE, 2019
[6]. Tal Zarsky. The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1):118–132, 2016.
[7]. Zhenpeng Chen, Jie M Zhang, Federica Sarro, and Mark Harman. Maat: a novel ensemble approach to addressing fairness and performance bugs for machine learning software. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 1122–1134, 2022
[8]. Maria De-Arteaga, Stefan Feuerriegel, and Maytal Saar-Tsechansky. Algorithmic fairness in business analytics: Directions for research and practice. Production and Operations Management, 31(10):3749–3770, 2022.
[9]. Mahmoud Abdallah, Nhien An Le Khac, Hamed Jahromi, and Anca Delia Jurcut. A hybrid cnn-lstm based approach for anomaly detection systems in sdns. In Proceedings of the 16th International Conference on Availability, Reliability and Security, pages 1–7, 2021.
[10]. Tuong Le, Bay Vo, Hamido Fujita, Ngoc-Thanh Nguyen, and Sung Wook Baik. A fast and accurate approach for bankruptcy forecasting using squared logistics loss with gpu-based extreme gradient boosting. Information Sciences, 494:294–310, 2019.
[11]. María Óskarsdóttir, Cristián Bravo, Carlos Sarraute, Jan Vanthienen, and Bart Baesens. The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 74:26–39, 2019.
[12]. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6):1–35, 2021.
[13]. David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. Learning adversarially fair and transferable representations. In International Conference on Machine Learning, pages 3384–3393. PMLR, 2018
[14]. Nhlakanipho Michael Mqadi, Nalindren Naicker, and Timothy Adeliyi. Solving misclassification of the credit card imbalance problem using near miss. Mathematical Problems in Engineering, 2021:1–16, 2021.
[15]. Saharon Rosset. Model selection via the auc. In Proceedings of the twenty-first international conference on Machine learning, page 89, 2004.
[16]. Yulu Jin and Lifeng Lai. Fairness-aware regression robust to adversarial attacks. arXiv preprint arXiv:2211.04449, 2022
[17]. Clemens Kreutz, Andreas Raue, and Jens Timmer. Likelihood based observability analysis and confidence intervals for predictions of dynamic models. BMC Systems Biology, 6(1):1–9, 2012.
[18]. JD Balakrishnan and Roger Ratcliff. Testing models of decision making using confidence ratings in classification. Journal of Experimental Psychology: Human Perception and Performance, 22(3):615, 1996
[19]. Nazeeh Ghatasheh. Business analytics using random forest trees for credit risk prediction: a comparison study. International Journal of Advanced Science and Technology, 72(2014):19–30, 2014.
[20]. Trishita Saha, Saroj Kumar Biswas, Saptarsi Sanyal, Souvik Kumar Parui, and Biswajit Purkayastha. Credit risk prediction using extra trees ensemble method. In 2023 11th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON), pages 1–8. IEEE, 2023
[21]. Wenyu Qiu. Credit risk prediction in an imbalanced social lending environment based on xgboost. In 2019 5th International Conference on Big Data and Information Analytics (BigDIA), pages 150–156. IEEE, 2019.
[22]. Maria Aparecida Gouvêa and Eric Bacconi Gonçalves. Credit risk analysis applying logistic regression, neural networks and genetic algorithms models. In POMS 18th annual conference, 2007.
[23]. Siddharth Bhatore, Lalit Mohan, and Y Raghu Reddy. Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4:111–138, 2020