Useful Resources

This section provides a list of books and resources that I have found useful in my journey to understand credit risk management and related fields. Don’t be overwhelmed by the number of resources listed here. I did not read all of them cover to cover. Instead, I used them as references to solve specific problems or to gain a deeper understanding of a particular topic I encountered in my work.

Decision Making

  • Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Cambridge, Massachusetts: The MIT Press, 2018.

  • Puterman, Martin L. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics Section. New York: Wiley, 1994.

  • Régis, Daniel Evangelista, and Rinaldo Artes. ‘Using Multi-State Markov Models to Identify Credit Card Risk’. Production 26 (24 November 2015): 330–44. https://doi.org/10.1590/0103-6513.160814.

  • Herasymovych, Mykola, Karl Märka, and Oliver Lukason. ‘Using Reinforcement Learning to Optimize the Acceptance Threshold of a Credit Scoring Model’. Applied Soft Computing 84 (1 November 2019): 105697. https://doi.org/10.1016/j.asoc.2019.105697.

  • So, Mee Chi. ‘Optimizing Credit Limit Policy by Markov Decision Process Models’. Phd, University of Southampton, 2009. https://eprints.soton.ac.uk/68761/.

Causal Inference

  • A Very Comprehensive List on Github

  • Kumar, Satyam, Yelleti Vivek, Vadlamani Ravi, and Indranil Bose. ‘Causal Inference for Banking, Finance, and Insurance – A Survey’, n.d.

  • Miao, Hang, Kui Zhao, Zhun Wang, Linbo Jiang, Quanhui Jia, Yanming Fang, and Quan Yu. ‘Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference’. arXiv, 10 July 2020. https://doi.org/10.48550/arXiv.2007.05188.

Economics and Finance

  • Fisher, Irving. The ‘Impatience Theory’ of Interest; a Study of the Causes Determining the Rate of Interest … Bologna, Nicola Zanichelli, 1911. http://archive.org/details/cu31924013755909.

Books on Risk Management

General Risk Management

  • Smithson, Charles. Credit Portfolio Management. Hoboken, N.J: John Wiley, 2003.

  • Gregoriou, Greg N., and Christian Hoppe, eds. The Handbook of Credit Portfolio Management. McGraw-Hill Finance & Investing. New York: McGraw-Hill, 2009.

  • 陈建著. and 陈建. 现代信用卡管理 = Credit card management. Beijing: 中国财政经济出版社, 2005.

  • Kostiv, Markiyan. ‘Customer Lifetime Value For Credit Limit Optimization’, n.d.

Credit Risk Modeling

Modelling

  • Refaat, Mamdouh. Credit Risk Scorecard: Development and Implementation Using SAS. United States: Printed by Lulu.com, 2011.

  • Christodoulakis, George, and Stephen Satchell, eds. The Analytics of Risk Model Validation. 1st ed. Quantitative Finance Series. Amsterdam ; Boston: Elsevier/Academic Press, 2008.

  • Thomas, L. C., David B. Edelman, and Jonathan N. Crook. Credit Scoring and Its Applications. Second edition. Mathematics in Industry. Philadelphia: Society for Industrial and Applied Mathematics, 2017.

  • Thomas, L. C. Consumer Credit Models: Pricing, Profit, and Portfolios. Oxford ; New York: Oxford University Press, 2009.

  • Verbraken, Thomas, Cristián Bravo, Richard Weber, and Bart Baesens. ‘Development and Application of Consumer Credit Scoring Models Using Profit-Based Classification Measures’. European Journal of Operational Research 238, no. 2 (16 October 2014): 505–13. https://doi.org/10.1016/j.ejor.2014.04.001.

  • Dey, Shubhamoy. ‘Credit Limit Management Using Action-Effect Models’. In 2010 International Conference on Financial Theory and Engineering, 112–15, 2010. https://doi.org/10.1109/ICFTE.2010.5499415.

  • Liu, Ke, Kin Keung Lai, and Sy-Ming Guu. ‘Dynamic Credit Scoring on Consumer Behavior Using Fuzzy Markov Model’. In 2009 Fourth International Multi-Conference on Computing in the Global Information Technology, 235–39, 2009. https://doi.org/10.1109/ICCGI.2009.42.

  • Malik, Madhur, and Lyn C. Thomas. ‘Transition Matrix Models of Consumer Credit Ratings’. International Journal of Forecasting 28, no. 1 (January 2012): 261–72. https://doi.org/10.1016/j.ijforecast.2011.01.007.

Validation

  • Scandizzo, Sergio. The Validation of Risk Models: A Handbook for Practitioners. Applied Quantitative Finance Series. Basingstoke, Hampshire: Palgrave Macmillan, 2016.

  • Meyer, Christian, and Peter Quell. Risk Model Validation: A Practical Guide to Address the Key Questions. London: Risk Books, 2012.

  • Lynch, David, Akhtar R. Siddique, and Iftekhar Hasan, eds. Validation of Risk Management Models for Financial Institutions: Theory and Practice. First edition. New York, NY: Cambridge University Press, 2022.

Reject Inference

  • Liao, Jingxian, Wei Wang, Jason Xue, and Anthony Lei. ‘Data Augmentation Methods for Reject Inference in Credit Risk Models’, 2021. https://www.semanticscholar.org/paper/Data-Augmentation-Methods-for-Reject-Inference-in-Liao-Wang/96f03f1fb147a5cb0d02cfb66df4ce316d208260.

  • Crook, Jonathan, and John Banasik. ‘Does Reject Inference Really Improve the Performance of Application Scoring Models?’ Journal of Banking & Finance, Retail Credit Risk Management and Measurement, 28, no. 4 (1 April 2004): 857–74. https://doi.org/10.1016/j.jbankfin.2003.10.010.

Survival Analysis

  • Cleves, Mario. An Introduction to Survival Analysis Using Stata, Second Edition. Stata Press, 2008.

  • Kleinbaum, David G., and Mitchel Klein. Survival Analysis: A Self-Learning Text. 2nd ed. Statistics for Biology and Health. New York, NY: Springer, 2005.

LLM and Credit Risk

  • Sanz-Guerrero, Mario, and Javier Arroyo. ‘Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending’. arXiv, 29 January 2024. https://doi.org/10.48550/arXiv.2401.16458.

  • Sanz Guerrero, Mario. ‘Performance Assessment of Credit Risk Models with Boosting Algorithms and Transfer Learning from Large Language Models’, 2023. https://hdl.handle.net/20.500.14352/101304.

Others

  • De Almeida Filho, Adiel T., Christophe Mues, and Lyn C. Thomas. ‘Optimizing the Collections Process in Consumer Credit’. Production and Operations Management 19, no. 6 (November 2010): 698–708. https://doi.org/10.1111/j.1937-5956.2010.01152.x.

  • So, Mee Chi, Christophe Mues, Adiel T. De Almeida Filho, and Lyn C Thomas. ‘Debtor Level Collection Operations Using Bayesian Dynamic Programming’. Journal of the Operational Research Society 70, no. 8 (3 August 2019): 1332–48. https://doi.org/10.1080/01605682.2018.1506557.

  • Sánchez, Catalina, Sebastián Maldonado, and Carla Vairetti. ‘Improving Debt Collection via Contact Center Information: A Predictive Analytics Framework’. Decision Support Systems 159 (August 2022): 113812. https://doi.org/10.1016/j.dss.2022.113812.

  • Witzany, Jiří, and Anastasiia Kozina. ‘Recovery Process Optimization Using Survival Regression’. Operational Research 22, no. 5 (November 2022): 5269–96. https://doi.org/10.1007/s12351-022-00703-3.

  • Van De Geer, Ruben, Qingchen Wang, and Sandjai Bhulai. ‘Data-Driven Consumer Debt Collection via Machine Learning and Approximate Dynamic Programming’. In SSRN Electronic Journal, 2018. https://doi.org/10.2139/ssrn.3250755.