FinGAN: Chaotic generative adversarial network for analytical customer relationship management in banking and insurance

Neural Computing and Applications - Tập 35 Số 8 - Trang 6015-6028 - 2023
Prateek Kate1,2, Vadlamani Ravi3, Akhilesh Kumar Gangwar3
1Institute for Development and Research in Banking Technology
2UNIVERSITY OF HYDERABAD
3Center of Excellence in Analytics, Institute for Development and Research in Banking Technology, Hyderabad, India

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Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. Adv Neural Inf Process Syst. https://doi.org/10.1145/3422622

Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. https://arxiv.org/abs/1701.07875

Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K (2019) Modeling tabular data using conditional GAN. https://arxiv.org/pdf/1907.00503

Tax DM, Duin RP (2004) Support vector data description. Mach Learn 54:45–66. https://doi.org/10.1023/B:MACH.0000008084.60811.49

Kumar V, Reinartz W (2018) Customer relationship management: concept, strategy, and tools. Springer-Verlag GmbH, Germany

Gangwar AK, Ravi V (2019) Generative adversarial network for oversampling data in credit card fraud detection. In: ICISS, Hyderabad, India pp 123–134

Sisodia DS, Reddy NK (2017) Performance evaluation of class balancing techniques for credit card fraud detection. In: 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI), pp 2747–2752

Randhawa K, Chu Kiong L, Seera M, Lim C, Nandi A (2018) Credit card fraud detection using AdaBoost and majority voting. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2806420

Dos Santos Tanaka FHK, Aranha C (2019) Data augmentation using GAN. https://arxiv.org/abs/1904.09135

Motinni A, Lheritier A, Acuna-Agost R (2018) Airline passenger name record generation using generative adversarial networks. https://arxiv.org/abs/1807.06657

Fiore U, Santis AD, Perla F, Zanetti P, Palmieri F (2019) Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inf Sci 479:448–455

Vega-Marquez B, Rubio-Escudero C, Riquelme J, Nepomuceno-Chamorro C (2020) Creation of synthetic data with conditional generative adversarial networks. In: SOCO 2019. AISC. Springer, Cham pp 231–240

Che T, Li Y, Zhang R, Hjelm RD, Li W, Song Y, Bengio Y (2017) Maximum-likelihood augmented discrete generative adversarial networks. https://arxiv.org/abs/1702.07983

Kusner MJ, Hernández-Lobato (2016) JM GANs for sequences of discrete elements with the gumbel-softmax distribution. https://arxiv.org/abs/1611.04051

Ping H, Stoyanovich J, Howe B (2017) Data synthesizer: privacy-preserving synthetic datasets. In: Proceedings of the 29th international conference on scientific and statistical database management. ACM, p 42

Esteban C, Hyland SL, Rätsch G (2017) Real-valued (medical) time series generation with recurrent conditional GANs. https://arxiv.org/abs/1706.02633

Camino R, Hammer-schmidt C (2018) State R Generating multi-categorical samples with generative adversarial networks. https://arxiv.org/abs/1807.01202

Choi E, Biswal S, Malin B, Duke J, Stewart WF, Sun J (2017) Generating multi-label discrete patient records using generative adversarial networks. https://arxiv.org/abs/1703.06490

Patel S, Kakadiya A, Mehta M, Derasari R, Patel R, Gandhi R (2018) Correlated discrete data generation using adversarial training. https://arxiv.org/abs/1804.00925

Park N, Mohammadi M, Gorde K, Jajodia S, Park H, Kim Y (2018) Data synthesis based on generative adversarial networks. Proc VLDB Endow 11(10):1071–1083

Xu L, Veeramachaneni K (2018) Synthesizing tabular data using generative adversarial networks. https://arxiv.org/pdf/1811.11264

Smith KA, Gupta JN (2000) Neural networks in business: techniques and applications for the operations researcher. Comput Oper Res 27(11–12):1023–1044

Ferreira JB, Vellasco M, Pacheco MA, Barbosa CH (2004) Data mining techniques on the evaluation of wireless churn. In: (ESANN’2004). Proceedings european symposium on artificial neural networks bruges (Belgium), d-sidepublication ISBN 2-930307-04-8, pp 483–488

Kumar DA, Ravi V (2008) Predicting credit card customer churn in banks using data mining. Int J Data Anal Tech Strat 1(1):4–28

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

Larivie’re B, den Poel (2018) DV Investigating the role of product features in preventing customer churn, by using survival analysis and choice modelling: the case of financial services. Expert Syst Appl 27(2):277–285

Ali OG, ArÕtürk U (2014) Dynamic churn prediction framework with more effective use of rare event data: the case of private banking. Expert Syst Appl 41(17):7880–7903

Verbeke W, Martens D, Mues C, Baesens B (2011) Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst Appl 38(3):2354–2364

Tsai CF, Lu YH (2009) Customer churn prediction by hybrid neural networks. Expert Syst Appl 36(10):12547–12553

Sundarkumar GG, Ravi V (2015) A novel hybrid under-sampling method for mining unbalanced datasets in banking and insurance. Eng Appl Artif Intell 37:368–377

Sundarkumar GG, Ravi V, Siddeshwar V (2015) One-class support vector machine based under-sampling: application to churn prediction and insurance fraud detection. In: 2015 IEEE international conference on computational intelligence and computing research

Farquad MAH, Ravi V, Bapi Raju S (2011) Analytical CRM in banking and finance using SVM: a modified active learning-based rule extraction approach. Int J Electron Cust Relatsh Manag 6(1):48–73

Phua C, Damminda A, Lee V (2004) Minority report in fraud detection: classification of skewed data Issue on Imbalanced datasets. SIGKDD Explor 6(1):50-S9

Sublej L, Furlan S, Bajec M (2011) An expert system for detecting automobile insurance fraud using network analysis. Expert Syst Appl 38(1):1039–1042

Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141

Dhanya CT, Nagesh Kumar D (2010) Nonlinear ensemble prediction of chaotic daily rainfall. Adv Water Resour 33(3):327–347

Packard NH, Crutchfield JP, Farmer JD, Shaw RS (1980) Geometry from a time series. Phys Rev Lett 45:712

Qasim OS, Thanoon A, Algamal ZY (2020) Feature selection based on chaotic binary black hole algorithm for data classification. Chem Intell Lab Syst 204:104104

Ahmed AE, Mohamed AA, Aboul EH (2019) Chaotic multi-verse optimizer-based feature selection. Neural Comput Appl 31(4):991–1006

Hu J, Heidari AA, Zhang L, Xue X, Gui W, Chen H, Pan Z (2021) Chaotic diffusion‐limited aggregation enhanced grey wolf optimizer: Insights, analysis, binarization, and feature selection. Int J Intell Syst 1–64

Schölkopf B, Williamson RC, Smola A, Shawe-Taylor J, Platt J (1999) Support vector method for novelty detection. Adv Neural Inf Process Syst 12

Jais I, Ismail A, Nisa SQ (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2:41. https://doi.org/10.17977/um018v2i12019p41-46

Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray D, Steiner B, Tucker P, Vasudevan V, Warden P, Zhang X (2016) TensorFlow: a system for large-scale machine learning

Pedregosa F, Varoquaux G, Gramfort A, Thirion MB, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. 12(85): 2825–2830

Vasu M, Ravi V (2011) A hybrid under-sampling approach for mining unbalanced datasets: application to Banking and insurance. Int J Data Min Model Manag 3(1):75–105

Mudholkar GS, Hutson AD (1996) The exponentiated Weibull family: some properties and a flood data application. Commun Stat Theory Methods 25:3059–3083

https://erdogant.github.io/distfit/pages/html/index.html

KStest-https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstest.html

Arik SO, Pfister T (2020) TabNet: attentive interpretable tabular learning. https://arxiv.org/abs/1908.07442