2,359
Views
2
CrossRef citations to date
0
Altmetric
Research Papers

A data-driven explainable case-based reasoning approach for financial risk detection

ORCID Icon, &
Pages 2257-2274 | Received 07 Jul 2021, Accepted 19 Aug 2022, Published online: 28 Sep 2022

References

  • Aamodt, A. and Plaza, E., Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun., 1994, 7, 39–59.
  • Aamodt, A., Sandtorv, H.A. and Winnem, O.M., Combining case based reasoning and data mining – a way of revealing and reusing rams experience. In Safety and Reliability; Proceedings of ESREL '98, edited by S. Lydersen, G.K. Hansen, H. Sandtorv, pp. 16–19, 1998 (Balkena: Rotterdam).
  • Abdou, H., Pointon, J. and El-Masry, A., Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Syst. Appl., 2008, 35, 1275–1292.
  • Ahn, H. and Jae Kim, K., Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Appl. Soft Comput., 2009, 9, 599–607.
  • Alam, T.M., Shaukat, K., Hameed, I.A., Luo, S., Sarwar, M.U., Shabbir, S., Li, J. and Khushi, M., An investigation of credit card default prediction in the imbalanced datasets. IEEE. Access., 2020, 8, 201173–201198.
  • Arshadi, N. and Jurisica, I., Data mining for case-based reasoning in high-dimensional biological domains. IEEE Trans. Knowl. Data Eng., 2005, 17, 1127–1137.
  • Atiya, A.F., Estimating the posterior probabilities using the k-nearest neighbor rule. Neural Comput., 2005, 17, 731–740.
  • Bach, K. and Althoff, K.D., Developing case-based reasoning applications using mycbr 3. In Case-based reasoning research and development, edited by B.D. Agudo, I. Watson, pp. 17–31, 2012 (Springer: Berlin, Heidelberg).
  • Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R. and Herrera, F., Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Inf. Fusion, 2020, 58, 82–115.
  • Bensic, M., Sarlija, N. and Zekic-Susac, M., Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intell. Syst. Account. Finance Manag., 2005, 13, 133–150.
  • Brown, C.E. and Gupta, U.G., Applying case-based reasoning to the accounting domain. Intell. Syst. Account. Finance Manag., 1994, 3, 205–221.
  • Brunette, E.S., Flemmer, R.C. and Flemmer, C.L., A review of artificial intelligence. In 2009 4th International Conference on Autonomous Robots and Agents, pp. 385–392, 2009 (IEEE: Wellington).
  • Bryant, S.M., A case-based reasoning approach to bankruptcy prediction modeling. Intell. Syst. Account. Finance Manag., 1997, 6, 195–214.
  • Byanjankar, A., Heikkilä, M. and Mezei, J., Predicting credit risk in peer-to-peer lending: A neural network approach. In 2015 IEEE Symposium Series on Computational Intelligence, pp. 719–725, 2015 (IEEE: Cape Town).
  • Ceriani, L. and Verme, P., The origins of the gini index: Extracts from variabilità e mutabilità (1912) by Corrado Gini. J. Econ. Inequal., 2012, 10, 421–443.
  • Chi, R.T., Chen, M. and Kiang, M.Y., Generalized case-based reasoning system for portfolio management. Expert Syst. Appl., 1993, 6, 67–76.
  • Cost, S. and Salzberg, S., A weighted nearest neighbor algorithm for learning with symbolic features. Mach. Learn., 1993, 10, 57–78.
  • Cunningham, P., Doyle, D. and Loughrey, J., An evaluation of the usefulness of case-based explanation. In Case-Based Reasoning Research and Development, edited by K.D. Ashley, D.G. Bridge, pp. 122–130, 2003 (Springer Berlin Heidelberg: Berlin, Heidelberg).
  • Dahiya, S., Handa, S. and Singh, N., A feature selection enabled hybrid-bagging algorithm for credit risk evaluation. Expert Syst., 2017, 34, e12217.
  • Dal Pozzolo, A., Caelen, O., Le Borgne, Y.A., Waterschoot, S. and Bontempi, G., Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl., 2014, 41, 4915–4928.
  • Ebrahimi, Kaggle Financial Distress Prediction, 2017. Available at: https://www.kaggle.com/shebrahimi/financial-distress.
  • Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P., From data mining to knowledge discovery in databases. AI Mag., 1996, 17, 37.
  • Gardner, M. and Dorling, S., Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences. Atmos. Environ., 1998, 32, 2627–2636.
  • Gouttaya, N. and Begdouri, A., Integrating data mining with case based reasoning (CBR) to improve the proactivity of pervasive applications. In 2012 Colloquium in Information Science and Technology, pp. 136–141, 2012 (IEEE: Fez).
  • Gramespacher, T. and Posth, J.A., Employing explainable ai to optimize the return target function of a loan portfolio. Front. Artif. Intell., 2021, 4, 13.
  • Grömping, U., South German credit data: Correcting a widely used data set. Beuth University of Applied Sciences Berlin, 2019.
  • Guessoum, S., Laskri, M.T. and Lieber, J., Respidiag: A case-based reasoning system for the diagnosis of chronic obstructive pulmonary disease. Expert Syst. Appl., 2014, 41, 267–273.
  • Ha, V.S., Lu, D.N., Choi, G.S., Nguyen, H.N. and Yoon, B., Improving credit risk prediction in online peer-to-peer (p2p) lending using feature selection with deep learning. In 2019 21st International Conference on Advanced Communication Technology (ICACT), pp. 511–515, 2019 (IEEE: PyeongChang).
  • Henley, W.E. and Hand, D.J., A k-nearest-neighbour classifier for assessing consumer credit risk. J. R. Stat. Soc. Series B Stat. Methodol., 1996, 45, 77–95.
  • Hu, X., Xia, B., Skitmore, M. and Chen, Q., The application of case-based reasoning in construction management research: An overview. Autom. Constr., 2016, 72, 65–74.
  • Ince, H., Short term stock selection with case-based reasoning technique. Appl. Soft Comput., 2014, 22, 205–212.
  • Jaiswal, A. and Bach, K., A data-driven approach for determining weights in global similarity functions. In Case-Based Reasoning Research and Development, edited by K. Bach, C. Marling, pp. 125–139, 2019 (Springer International Publishing: Cham).
  • Kao, L.J., Chiu, C.C. and Chiu, F.Y., A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring. Knowl-Based Syst., 2012, 36, 245–252.
  • Kleinbaum, D.G., Introduction to Logistic Regression, pp. 1–38, 1994 (Springer: New York).
  • Kononenko, I., Šimec, E. and Robnik-Šikonja, M., Overcoming the myopia of inductive learning algorithms with relief. Appl. Intell., 1997, 7, 39–55.
  • Kraskov, A., Stögbauer, H. and Grassberger, P., Estimating mutual information. Phys. Rev. E, 2004, 69, 066138.
  • Kullback, S., Information Theory and Statistics, 1959 (Wiley: New York).
  • Lahmiri, S., Bekiros, S., Giakoumelou, A. and Bezzina, F., Performance assessment of ensemble learning systems in financial data classification. Intell. Syst. Account. Finance Manag., 2020, 27, 3–9.
  • Lahmiri, S., Giakoumelou, A. and Bekiros, S., An adaptive sequential-filtering learning system for credit risk modeling. Soft Comput., 2021, 25, 8817–8824.
  • Lamy, J.B., Sekar, B., Guezennec, G., Bouaud, J. and Séroussi, B., Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach. Artif. Intell. Med., 2019, 94, 42–53.
  • Li, H. and Sun, J., Business failure prediction using hybrid2 case-based reasoning (h2cbr). Comput. Oper. Res., 2010, 37, 137–151.
  • Li, H.G. and Hand, D.J., Direct versus indirect credit scoring classifications. J. Oper. Res. Soc., 2002, 53, 647–654.
  • Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. and Talwalkar, A., Hyperband: A novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Resh., 2018, 18, 1–52.
  • Li, W. and Becker, D.M., Day-ahead electricity price prediction applying hybrid models of lstm-based deep learning methods and feature selection algorithms under consideration of market coupling. Energy, 2021, 237, 121543.
  • Likas, A. and Vlassis, N., The global k-means clustering algorithm. Pattern Recognit., 2003, 36, 451–461.
  • Lin, H. and Ding, H., Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. J. Theor. Biol., 2011, 269, 64–69.
  • Mitchell, T.M., Machine Learning, 1st ed., 1997 (McGraw-Hill, Inc: USA).
  • Mohammed, M.A., Abd Ghani, M.K., Arunkumar, N., Obaid, O.I., Mostafa, S.A., Jaber, M.M., Burhanuddin, M. and Matar, B.M., Genetic case-based reasoning for improved mobile phone faults diagnosis. Comput. Electr. Eng., 2018, 71, 212–222.
  • Morris, B.W., Scan: A case-based reasoning model for generating information system control recommendations. Intell. Syst. Account. Finance Manag., 1994, 3, 47–63.
  • Moxey, A., Robertson, J., Newby, D., Hains, I., Williamson, M. and Pearson, S.A., Computerized clinical decision support for prescribing: Provision does not guarantee uptake. J. Am. Med. Inform. Assoc., 2010, 17, 25–33.
  • Novaković, J., Toward optimal feature selection using ranking methods and classification algorithms. Yugosl. J. Oper. Res., 2011, 21, 119–135.
  • O'Roarty, B., Patterson, D., McGreal, S. and Adair, A., A case-based reasoning approach to the selection of comparable evidence for retail rent determination. Expert Syst. Appl., 1997, 12, 417–428.
  • Pavlidis, N.G., Tasoulis, D.K., Adams, N.M. and Hand, D.J., Adaptive consumer credit classification. J. Oper. Res. Soc., 2012, 63, 1645–1654.
  • Peng, Y., Wang, G., Kou, G. and Shi, Y., An empirical study of classification algorithm evaluation for financial risk prediction. Appl. Soft Comput., 2011, 11, 2906–2915.
  • Prati, R.C., Combining feature ranking algorithms through rank aggregation. In The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2012 (IEEE: Brisbane).
  • Quinlan, J.R., C4.5: Programs for Machine Learning, 1993 (Morgan Kaufmann Publishers Inc.: San Francisco).
  • Rahman, M. and Kumar, V., Machine learning based customer churn prediction in banking. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1196–1201, 2020 (IEEE: Coimbatore).
  • Richter, M.M. and Weber, R.O., Case-Based Reasoning: A Textbook, 2013 (Springer Publishing Company: Berlin).
  • Sariev, E. and Germano, G., An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default. Rev. Financ. Econ., 2019, 37, 404–427.
  • Selvamani, B.R. and Khemani, D., Decision tree induction with CBR. In Pattern Recognition and Machine Intelligence, edited by S.K. Pal, S. Bandyopadhyay, S. Biswas, pp. 786–791, 2005 (Springer Berlin Heidelberg: Berlin, Heidelberg).
  • Sermpinis, G., Tsoukas, S. and Zhang, P., Modelling market implied ratings using lasso variable selection techniques. J. Empir. Finance, 2018, 48, 19–35.
  • Song, Y. and Peng, Y., A mcdm-based evaluation approach for imbalanced classification methods in financial risk prediction. IEEE. Access., 2019, 7, 84897–84906.
  • Song, Y.Y. and Lu, Y., Decision tree methods: Applications for classification and prediction. Shanghai Arch. Psychiatry, 2015, 27, 130–135.
  • Sørmo, F. and Cassens, J., Explanation goals in case-based reasoning. In ECCBR 2004. LNCS (LNAI), edited by P. Funk, P.A. González Calero, pp. 165–174, 2004 (Springer: Madrid).
  • Sørmo, F., Cassens, J. and Aamodt, A., Explanation in case-based reasoning–perspectives and goals. Artif. Intell. Rev., 2005, 24, 109–143.
  • Stoltzfus, J.C., Logistic regression: A brief primer. Acad. Emerg. Med., 2011, 18, 1099–1104.
  • Tibshirani, R., Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol., 1996, 58, 267–288.
  • Trivedi, S.K., A study on credit scoring modeling with different feature selection and machine learning approaches. Technol. Soc., 2020, 63, 101413.
  • Tsai, C.F. and Wu, J.W., Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst. Appl., 2008, 34, 2639–2649.
  • Voigt, P. and Bussche, A.V.D., The EU General Data Protection Regulation (GDPR): A Practical Guide, 1st ed., 2017 (Springer Publishing Company: Cham).
  • Vukovic, S., Delibasic, B., Uzelac, A. and Suknovic, M., A case-based reasoning model that uses preference theory functions for credit scoring. Expert Syst. Appl., 2012, 39, 8389–8395.
  • West, D., Neural network credit scoring models. Comput. Oper. Res., 2000, 27, 1131–1152.
  • Wihartiko, F.D., Wijayanti, H. and Virgantari, F., Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem. IOP Conference Ser.: Materials Sci. Eng., 2018, 332, 012020.
  • Wu, J. and Lin, Z., Research on customer segmentation model by clustering. In Proceedings of the 7th International Conference on Electronic Commerce, pp. 316–318, 2005 (Association for Computing Machinery: New York).
  • Yeh, I.C., The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl., 2009, 36, 2473–2480.
  • Zakrzewska, D. and Murlewski, J., Clustering algorithms for bank customer segmentation. In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), pp. 197–202, 2005 (IEEE: Warsaw).
  • Zhang, Y., Wang, S. and Ji, G., A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng., 2015, 2015, 931256.